HP_2009_10 by androsov.info

OCTOBER 2009

HPIMPACT

SPECIALREPORT

TECHNOLOGY

How natural gas impacts US economy

PROCESS CONTROL AND INFORMATION SYSTEMS

Equipment integrity management

Mixed outlook for LNG projects in Iran

Advanced controls, supply chain planning

Consider advanced reformer catalysts

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OCTOBER 2009 • VOL. 88 NO. 10 www.HydrocarbonProcessing.com

SPECIAL REPORT: PROCESS CONTROL AND INFORMATION SYSTEMS

Practical process control system metrics

Agile supply chain planning

Service-oriented architecture simplifies data source integration

Cover The cover photo is courtesy of Matrikon Inc., with offices and partners worldwide in North America, Asia, Australia, Europe and the Middle East. The photo depicts how Matrikon is leading the way with technology with 3D touch screens in a control room of the future. Matrikon’s industrial intelligence solutions transform production data into the knowledge you need to anticipate the future and optimize operations— empowering and sustaining the achievement of operational excellence.

Here are several useful examples A. G. Kern Providing a common workspace improves data integration C. Thomas, D. Tong, D. Jasper and C. Acuff

Here’s how the approach helps refinery scheduling and also contributes to business-wide SOA adoption K. Samdani

Predicting octane numbers for gasoline blends using artificial neural networks

HPIMPACT

The ANN models were more accurate than regression models E. Paranghooshi, M. T. Sadeghi and S. Shafiei

19 How natural gas impacts the US economy

Implementing and maintaining advanced process control on continuous catalytic reforming

21 Mixed outlook, at best, for LNG projects in Iran

The primary benefit was an increase in reformate octane barrel yield from operating the plant at its economic constraints P. Banerjee, A. Al-Majed and S. Kaushal

21 US EPA and NHTSA propose program to reduce greenhouse gases and improve fuel economy

SAFETY/MAINTENANCE

Design and implement an effective equipment integrity management system Consider this “integrity-only-specific” innovative methodology M. Spampinato and F. Nicolò

PROCESS AND LAB ANALYZERS

Fine tune accuracy in analytic measurement—Part 1 Understanding the root causes of time delay D. Nordstrom and T. Waters

9 HPIN RELIABILITY Modern pumps have stiffer shafts

PROCESS DEVELOPMENTS

Consider advanced multi-promoted catalysts to optimize reformers Improved catalyst systems strike a new balance to increase yields with greater selectivity for end-products P.-Y. Le Goff

COLUMNS

Supply nozzle

Field station

Fast loop ﬁlter

11 HPIN EUROPE NOC megaprojects, not climate policies, will be closing your local refiner

Stream #2 #3

Switch Condition streams sample

Process analyzer

Calibration ﬂuid Return nozzle

Sample disposal

Page 77 Basic sections of an analytical instrumentation sampling system.

13 HPINTEGRATION STRATEGIES Rethinking cyber security for HPI operations 15 HPIN CONTROL APC for min maintenance or max profit?—Part 1 17 HPIN ASSOCIATIONS ISA and Chem Show gear up for events

DEPARTMENTS 7 HPIN BRIEF • 19 HPIMPACT • 23 VIEWPOINT • 25 HPIN CONSTRUCTION • 29 HPI CONSTRUCTION BOXSCORE UPDATE • 86 HPI MARKETPLACE • 89 ADVERTISER INDEX

90 HPIN AUTOMATION SAFETY Integrating security into the safety lifecycle

www.HydrocarbonProcessing.com Houston Office: 2 Greenway Plaza, Suite 1020, Houston, Texas, 77046 USA Mailing Address: P. O. Box 2608, Houston, Texas 77252-2608, USA Phone: +1 (713) 529-4301, Fax: +1 (713) 520-4433 E-mail: editorial@HydrocarbonProcessing.com www.HydrocarbonProcessing.com Publisher Bill Wageneck bill.wageneck@gulfpub.com EDITORIAL Editor Les A. Kane Senior Process Editor Stephany Romanow Process Editor Tricia Crossey Reliability/Equipment Editor Heinz P. Bloch News Editor Billy Thinnes European Editor Tim Lloyd Wright Contributing Editor Loraine A. Huchler Contributing Editor William M. Goble Contributing Editor Y. Zak Friedman Contributing Editor ARC Advisory Group (various) MAGAZINE PRODUCTION Director—Editorial Production Sheryl Stone Manager—Editorial Production Chris Valdez Artist/Illustrator David Weeks Manager—Advertising Production Cheryl Willis ADVERTISING SALES See Sales Offices page 88. CIRCULATION +1 (713) 520-4440 Director—Circulation Suzanne McGehee E-mail: circulation@gulfpub.com SUBSCRIPTIONS

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If you would like to have a recent article reprinted for an upcoming conference or for use as a marketing tool, contact us for a price quote. Articles are reprinted on quality stock with advertisements removed; options are available for covers and turnaround times. Our minimum order is a quantity of 100. For more information about article reprints, call Cheryl Willis at +1 (713) 525-4633 or e-mail EditorialReprints@gulfpub.com HYDROCARBON PROCESSING (ISSN 0018-8190) is published monthly by Gulf Publishing Co., 2 Greenway Plaza, Suite 1020, Houston, Texas 77046. Periodicals postage paid at Houston, Texas, and at additional mailing office. POSTMASTER: Send address changes to Hydrocarbon Processing, P.O. Box 2608, Houston, Texas 77252. Copyright © 2009 by Gulf Publishing Co. All rights reserved. Permission is granted by the copyright owner to libraries and others registered with the Copyright Clearance Center (CCC) to photocopy any articles herein for the base fee of $3 per copy per page. Payment should be sent directly to the CCC, 21 Congress St., Salem, Mass. 01970. Copying for other than personal or internal reference use without express permission is prohibited. Requests for special permission or bulk orders should be addressed to the Editor. ISSN 0018-8190/01. www.HydrocarbonProcessing.com

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HPIN BRIEF BILLY THINNES, NEWS EDITOR

BT@HydrocarbonProcessing.com

Qatargas recently honored two contractors involved with the Laffan refinery project located in Ras Laffan Industrial City, Qatar. GS Engineering & Construction and Daewoo Engineering & Construction were recognized for maintaining an excellent safety record throughout the refinery’s construction period. “We are thankful to the management, staff and all the workers of GS Engineering & Construction, Daewoo Engineering & Construction, and their subcontractors for their excellent safety record. I am very pleased with the safety leadership exhibited by these contractors and their use of effective safety processes which were implemented throughout the entire project. The results were outstanding,” said Faisal M Al Suwaidi, chairman and CEO of Qatargas. During the project, GS and Daewoo (along with their subcontractors) worked more than 31 million man-hours with only one lost-time incident (LTI). Since that incident, more than 23 million man-hours were worked without a further LTI. GS and Daewoo formed a consortium to undertake the engineering, procurement and construction phase of the Laffan refinery after being awarded a contract in May 2005.

Cal Dooley, president and CEO of the American Chemistry Council (ACC), recently announced that for every unit of greenhouse gases (GHGs) emitted by the chemical industry, society saves more than two units via the use of chemistry products and technologies provided to other industries and consumers. He pulled this information from a carbon-lifecycle analysis of the chemical industry performed by McKinsey and Company. According to the study, the most significant GHG emissions savings by volume come from items such as building insulation materials, anti-fouling coatings and synthetic textiles.

Excelerate Energy recently completed a liquefied natural gas (LNG) receiving facility and dockside regasification facility located at the South Jetty facility within the Mina Al-Ahmadi refinery approximately 20 miles south of Kuwait City, Kuwait. The facility was designed and constructed by Excelerate under an EPC agreement with the Kuwait National Petroleum Co. (KNPC). Following commissioning activities, the facility entered service on August 27, 2009, and received its first commercial delivery of LNG several days after with the arrival of the conventional LNG carrier Grand Aniva. Since the commissioning, there have been three complete LNG vessel transfers onto the energy bridge regasification vessel Explorer. At the Mina Al-Ahmadi gas port, the company’s energy-bridge vessel Explorer is docked alongside a newly constructed jetty where it is connected to the onshore facility and feeding natural gas directly into Kuwait’s gas distribution network. LNG cargoes are supplied to the Explorer via traditional LNG carriers (LNGCs) utilizing a fixed cryogenic ship-to-ship transfer system with LNG transfer rates between the LNGC and the EBRV in excess of 5,000 m3/hour through two cryogenic liquid transfer arms.

Darling International Inc. has joined with a subsidiary of Valero Energy Corp. to discuss forming a joint venture to build a facility capable of producing over 10,000 bpd of renewable diesel on a site adjacent to Valero’s St. Charles refinery near Norco, Louisiana. The proposed facility would principally convert waste grease—supplied by Darling—and other feedstocks that become economically and commercially viable into renewable diesel. Darling and Valero will jointly seek a loan guarantee for the proposed joint venture from the US Department of Energy under the Energy Policy Act of 2005, which makes $8.5 billion of debt financing guarantees available for projects that employ innovative energy efficiency technologies. HP

■ Oil demand up in 2010 Global oil demand will grow next year for the first time since 2007, says a recent report from IHS Cambridge Energy Research Associates (IHS CERA). The group also projects demand returning to its pre-recession levels by 2012. IHS CERA sees a five-year turnaround scenario, pegging oil demand growth at 900,000 bpd in 2010 and, then, the big news of the report, a return to its 2007 high of 86.5 million bpd by 2012. “Oil demand dropped by 2.8 million bpd from its high point of 86.5 million bpd in 2007 to 83.8 million bpd in 2009,” IHS CERA says. “The last time that the world experienced such a severe decline in oil consumption was in the early 1980s and it took nine years for demand to return to the 1979 pre-recession high. A five-year turnaround—while still a substantial amount of time—would be swift in comparison.” IHS CERA thinks emerging markets will drive the recovery of oil demand. It expects oil demand to increase from 83.8 million bpd in 2009 to 89.1 million bpd in 2014, with 83% coming from non-OECD countries. The research group expects China alone to account for 1.6 million bpd of cumulative growth, while only 900,000 bpd of growth will come from OECD countries. The miniscule growth numbers from the OECD countries highlight structural changes like “higher fuel efficiency, the displacement of conventional oil with renewable energy sources and a slower pace of growth in transportation fuel consumption.” All of these factors trend toward flat demand in the OECD. HP

HYDROCARBON PROCESSING OCTOBER 2009

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HPIN RELIABILITY HEINZ P. BLOCH, RELIABILITY/EQUIPMENT EDITOR HB@HydrocarbonProcessing.com

Modern pumps have stiffer shafts Some multistage centrifugal pumps are designed with relatively slender shafts and operate at speeds above the so-called rotor critical. When that is the case and as the pump comes up to operating speed, there is a brief time period when the “ramping-up” speed will coincide with the rotor critical and undesirable shaft deflection will have taken place. Designs with reduced bearing spans have been used in modern canned-motor pumps as an approach that avoids the amount or risk of shaft deflection. Furthermore, canned-motor pumps are product-lubricated and require no mechanical seals. That makes them inherently less prone to experience shaft deflection.

FIG. 1

A five-stage high-performance centrifugal pump with hydraulic thrust balance. Note thrust bearing on drive side (Source: Sulzer-Bingham, Portland, Oregon).

However, while canned-motor pumps are among the viable options and will be the subject of other articles in HP, traditional centrifugal pump designs have made progress as well. In fact, operation above rotor critical speed has also been made possible with intelligent redesigns of the more traditional centrifugal pumps. Although some of these stiff-shaft, intelligent redesigns (Figs. 1 and 2) are still using mechanical seals, they merit a closer look. Note how they, like canned-motor pumps, are product-lubricated. The extent to which either the pumps shown in Figs. 1 and 2, or the stiff-shaft product-lubricated canned-motor pumps of Fig. 3, are best suited to serve in a given application must be determined on a case-by-case basis. That said, reliability professionals must stay informed on the pros and cons of all available pump types. There are also reconfigured 10-stage pumps. In its reconfigured version, an “old” multistage pump is equipped with hydrostatic bearings at the center stage and throttle bushing locations. Each stage is stepped and the impeller retained with split rings instead of the typical “stacked” rotor. Pump upgrade manufacturers often coat the center-stage bushing with proprietary hard coatings and have outstanding experience with well-chosen materials unless there is excessive solids ingestion. Of course, while no multistage pump will survive a serious catalyst ingestion event, there are certain important opportunities pump users should pursue in their quest for extended run length. You might consult the Sulzer, Conhagen and Hydro websites as an important first step. Hydro is the largest non-OEM pump rebuilder; the company specializes in combining repair with optimized upgrading. HP

The author is the Reliability/Equipment Editor of HP. A practicing engineer and ASME Life Fellow with close to 50 years of industrial experience, he advises process plants on maintenance cost-reduction and reliability upgrade issues. His 16th and 17th textbooks on reliability improvement subjects were published in 2006. An excerpt was taken from Bloch-Geitner, Maximizing Machinery Uptime, (Gulf Publishing, ISBN 10:0-7506-7725-2).

FIG. 2

A six-stage high-performance centrifugal pump with hydraulic thrust balance. Note thrust bearing on non-drive side (Source: Conhagen Inc., Houston, Texas).

FIG. 3

Multistage canned-motor pump (Source: Lederle-Hermetic, Inc., Gundelfingen, Germany). HYDROCARBON PROCESSING OCTOBER 2009

In troubled times fierce global competition for premium crudes means that refinery units must have the flexibility to handle heavy, viscous, dirty crudes that increasingly threaten to dominate markets. And flexibility must extend to products as well as crudes, for refinery product demand has become more and more subject to violent economic and political swings. Thus refiners must have the greatest flexibility in determining yields of naphtha, jet fuel, diesel and vacuum gas oil products.

Why Do Many Crude/Vacuum Units Perform Poorly?

Rather than a single point process model, the crude/vacuum unit design must provide continuous flexibility to operate reliably over long periods of time. Simply meeting the process guarantee 90 days after start-up is very different than having a unit still operating well after 5 years. Sadly few refiners actually achieve this—no matter all the slick presentations by engineers in business suits!

modeling. Refinery hands-on experience teaches that fouling, corrosion, asphaltene precipitation, crude variability, and crude thermal instability, and many other non-ideals are the reality. Theoretical outputs of process or equipment models are not. In this era of slick colorful PowerPoint® presentations by well-spoken engineers in Saville Row suits, it’s no wonder that units don’t work. Shouldn’t engineers wearing Nomex® coveralls who have worked with operators and taken field measurements be accorded greater credibility?

In many cases it’s because the original design was based more on virtual than actual reality. There is no question: computer simulations have a key role to play but it’s equally true that process design needs to be based on what works in the field and not on the ideals of the process simulator. Nor should the designer simply base the equipment selection on vendor-stated performance. The design engineer needs to have actual refinery process engineering experience, not just expertise in office-based

Today more than ever before this is important. Gone are the days when a refiner could rely on uninterrupted supplies of light, sweet, easy-to-process crudes.

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HPIN EUROPE TIM LLOYD WRIGHT, EUROPEAN EDITOR tim.wright@gulfpub.com

NOC megaprojects, not climate policies, will be closing your local refiner Some 800,000 bpd (800 Mbpd) of refinery expansion capacity, which faded and disappeared from the radar of the OECD’s energy forecasts last year, is now back. There was quite a fanfare over capacity reductions this time last year when two major refining projects were shelved and thereby fell way off the horizon of the International Energy Agency’s (IEA’s) mid-term reporting. More bad news. Now, at a time of extreme economic stress

for the European refining industry, it’s sobering that, when the medium term report is published next month, these megaprojects will be back in the IEA analysis. If you’re a European refiner, it has to leave you feeling glum. It will mean the possible closure of eight European refineries—or more. The crude oil that once made its way to Europe will now stay in the Middle East. Now, a large share of the 800 Mbpd of refined products will find its way to Europe instead. Saudi Aramco is building two refineries in the Kingdom, each with a capacity of 400 Mbpd in joint ventures with Total, at Jubail, and with ConocoPhillips, at Yanbu. First to go. By the time you read this, one can only assume that

the first of the refinery closures resulting from a meager short- and mid-term outlook will be a fait accompli. The Petroplus Teesside refinery has been undergoing an economic shutdown—these shutdowns are all the rage in Europe since April. There’ve been some rumors that a trader like Vitol might take over the site and use its tank farm to play the contango market. But, as I write, there’s little cheer for the 150 workers at Teesside, or for their local member of Parliament, Frank Cook, who is trying to keep the refinery open. Teesside was my first newspatch in BBC local radio. So when I came across that newspaper’s coverage of Frank Cook’s efforts, I e-mailed him offering to share information. “My fight’s been for the jobs,” Cook said when he called me back. He’d recently met with Petroplus and asked about Blackstone—the private equity operation with the highly paid CEO, who, in 2008 announced that they’d finance Petroplus acquisitions in the US. Once it was clear that neither of us really knew of any potential white knight for the site, there was a pause in which one so wanted to say, “It’s too bad because it should really be kept open.” But what can one really say? It’s hard to shake off an idea that rescuing the site would be like laying a picnic below an oncoming avalanche. Plan B. All I could think of saying was that, in the post-Copen-

hagen summit world, there may be a premium on hydrocarbon sites that are close to mature offshore reservoirs, where carbon dioxide (CO2) capture projects can be implemented first. So, that’s what I said. And the more I thought about it, the more I reflected that Teesside could be just the place to develop a low-emissions refinery with carbon storage. Teesside has a well-developed hydrogen network.

There are plans to pump biohydrogen into that network from the gasification of forest woodchips. There are well-developed plans by Progressive Energy and Centrica to build a coal-fired integrated gasification combined cycle unit with carbon sequestration in the region, as well as, that could mean off-peak hydrogen too. Furthermore, many excellent offshoots of the Imperial Chemical Industries (ICI) era remain in the northeast of England. Synetix, now owned by Johnson Matthey, is an example of expertise that could be brought to bear on the challenges of Life Cycle Greenhouse Gas Reduction legislation. And carbon sequestration isn’t all pie in the sky, either. The British Geological Survey estimates that the UK can store some 60–150 billion tons of CO2 in strata below the North Sea that most countries lack. They estimate that it could be a profitable business, generating £2–4 billion/yr for the UK by 2030 and could sustain between 30,000 and 60,000 jobs. It would be an irony if carbon efficiency saved the mid-sized European refiner. After all, the Middle East may be on the right end of an equation that is moving millions of barrels a day of oil refining from the oil majors into the hands of national oil companies (NOCs). But, the Middle East is poor-to-disastrous at carbon efficiency, and almost as antagonistic to climate change mitigation as the American Petroleum Institute (API). Institute behavior. I mention the latter, because, in the recent affair of the “Energy Citizens” memo and the so-called “Astroturf” rallies, this institute is entering more deeply into a field very foreign to the European understanding of what an institute is for. An exposé, published in the Financial Times, depicts this organization exhorting its members to bus their employees to “grassroots” demonstrations, coordinated by professional event organizers, in an attempt to influence—derail even—a US legislative response to climate change. Funded partly by European companies like Siemens, BP and Shell, this institute is positioning itself a long way from the institutes that huddle around Parliament Square in London, such as IChemE, IMechE and the Energy Institute, or their counterpart at Rueil-Malmaison, the Institut Français du Pétrole. “An institute is there to be studiously objective in exercising its members’ professional expertise to establish the truth and the best way forward for the community. It should not lobby for narrow interests—it should lobby for its professional opinion, which should be derived from objective evidence and logical analysis,” one IMechE Fellow told me. HP

The author is HP’s European Editor. He has been active as a reporter and conference chair in the European downstream industry since 1997, before which he was a feature writer and reporter for the UK broadsheet press and BBC radio. Mr. Wright lives in Sweden and is the founder of a local climate and sustainability initiative.

HYDROCARBON PROCESSING OCTOBER 2009

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HPINTEGRATION STRATEGIES ROBERT MICK, CONTRIBUTING EDITOR bmick@arcweb.com

Rethinking cyber security for HPI operations Cyber security attacks and defenses both continue to escalate and grow in sophistication. Fortunately, to date, most of the attack energy has been directed at commercial targets and individuals. However, many of the same attacks and tools can affect businesses with critical operations, such as HPI plants. Here the situation is different. Operations typically has different systems and risks, requiring a separate security community focused on these issues. This community has been working for many years, but problems persist. Furthermore, new requirements are emerging and today’s established practices may not apply. Certainly, we must continue to invest in existing programs and initiatives, but we also need to identify persistent problems, examine new requirements and search for new ways to think about solutions. Resilient control systems. There are many reasons to try to address cyber security issues from a different perspective. The most compelling is that cyber security activity is relatively mature, yet HPI and other businesses are still living with high risk. It is time to rethink some issues to provide a context for refocusing some energy in more effective directions and to find a few new solution paths that recognize today’s trends. Most installed control systems were not designed with security in mind and most traditional device protocols had no security provisions. Components were designed assuming either a trusted (isolated) environment or an environment where other components implement various protections. Increasing sophistication of threats and insider threats constantly challenge these assumptions. Craig Rieger, David Gertman and Miles McQueen of Idaho National Laboratory proposed an interesting resilient control system (RCS) concept in an IEEE paper for the Second International Conference on Human Systems Interaction, May 2009. RCS includes the assumption of a malicious attacker, as well as other considerations, not previously considered during control system design. The RCS concept provides a framework for expanding our traditional thinking about control systems and is worth exploring at least from that perspective. Security is an end-to-end business process. Cyber security work has traditionally taken a design perspective in which protections are designed and implemented, people are trained and problems are handled as they occur. However, cyber security is really a very dynamic activity where execution speed and consistency are critical to success. Furthermore, many of these activities cross organizational and system boundaries. This all suggests that cyber security is similar to other end-to-end business processes and could benefit from the same analysis, structuring and automation methods. Using a process perspective to security might have several benefits. Some security processes, such as patch and identity management, need more integration and automation to reduce cost and risk; too much time is now spent on manual processes and chasing

down information. An analysis of processes would also facilitate developing best practices and provide a framework for standardizing security information and communications. Finally, better structuring and automation of security processes will provide security metrics and visibility to help balance security spending. Government and business are inseparable. Governments have overall responsibility for protecting nations (and their citizens), but businesses must implement most of the cyber security protections wherever the attacks originate and whatever the motivation. Consequently, governments and business should approach the challenges of cyber security using a working partnership perspective. The US Government’s role has been expanding and the current administration has been increasing its cyber security focus and activities. (See www.whitehouse.gov/assets/documents/ Cyberspace_Policy_Review_final.pdf ). This demands a corresponding response from high-level business managers as well as security experts. It will require rethinking how business and government interact to define common goals, establish clear roles and develop effective solutions. Industry-level visibility is critical. Security activity in general has been stimulated by the drama associated with hacks and amazing spy-like feats, but that is no longer productive. No one should still be surprised that intrusions can and do happen. Evolving attack tools will only make it easier. Security is in the details and businesses need facts about real situations and incidents to prioritize security spending and efforts, as well as to learn from each other’s experiences. Yes, incident reporting has been tried before, but better visibility is important and we need to rethink exactly what is needed and how to do it. While many in the industry continue to make valuable contributions to cyber security progress, some problems persist, suggesting that some rethinking is appropriate. This can be accomplished in various ways, such as simply re-examining problems in today’s context, attempting to apply techniques from other disciplines, organizing differently and others. First, we need to identify a short list of persistent cyber security problems in operations that need to be addressed. I’d love to hear from HP readers about their own short lists. Please feel free to e-mail me at bmick@arcweb.com. HP The author is vice president of enterprise systems for ARC Advisory Group. He has been a member of the enterprise team at ARC for six years and brings over 30 years of product development, systems integration, and operations experience in industrial automation and software applications. Mr. Mick’s focus areas include architectures and emerging technologies, enterprise and product integration, IT and infrastructure, enterprise networks, security, portals, e-commerce, standardization and standards. Mr. Mick has an MS degree in nuclear engineering and BS degree in chemical engineering from West Virginia University.

HYDROCARBON PROCESSING OCTOBER 2009

I 13

Where do You Want to be on the Performance Curve?

P = People M= Methodologies T = Technologies

Your Company + KBC Produces NextGen Performancen We collaborate with our clients to create unique solutions to their speciďŹ c challenges. Some of these challenges may include: Strategic Challenges

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U Improved Organisational Effectiveness U Reduced Maintenance Costs U Improved Energy EfďŹ ciency U Behaviour-based Reliability/Performance U Improved Safety Performance U Operational Risk Management

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Environmental Challenges U Reduced Emissions U Enhanced Compliance

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HPIN CONTROL PIERRE R. LATOUR, GUEST COLUMNIST SR2@msn.com

APC for min maintenance or max profit?—Part 1 First, thanks to Dr. Y. Zak Friedman1 for referencing my July 1997 HP editorial.2 I commend him for using HP publications from more than a decade ago in his editorial. While I appreciate Dr. Friedman’s effort to offer ideas to design advanced process control (APC) to perform better in the real HPI world, I continue to be dismayed about the inability of the process control, instrumentation and IT businesses to measure its financial profit contribution properly,3,4 which leads Friedman to recommend1 sacrificing 30% of potential APC benefits to reduce maintenance costs, by simplifying a MVC software matrix, without quantifying the performance loss or maintenance saving.1 I believe Dr. Friedman and I agree on the facts and situation of APC practice in the HPI,3,5 but naturally differ on how to address them. I am troubled by Zak’s broad claim in his first paragraph,1 “Considering the history of APC maintenance, the latter (forgoing 30% of potential benefits) is better by far.”1 It may be pragmatic, but it also may be unprofitable. Just beware of the faith theory.3 Since well-designed APC can generate $1 to $2/bbl crude refined,2,3,6,7 sacrificing $0.3 to 0.6/bbl x 200 kbpd/refinery = $60,000 to $120,000/day for a typical refinery to minimize APC maintenance illustrates the source of my dismay. Like Zak, I certainly would also sacrifice a little performance in exchange for a substantial maintenance cost reduction; and I suppose Zak would retain significant performance in exchange for a little maintenance. Surely he would agree if one could quantify these, the decision becomes easier. And forgoing 30% of APC benefits is an ad hoc rule-of-thumb from his experience. It would be helpful if Zak reported the maintenance cost saving he achieved by simplifying the MVC matrix on that column;1 compared to the 30% lost financial performance.1 My experience suggests the reason the HPI does not maintain APC well is its perception that maintenance cost is high and return is invisible.2–7 No one seems able to properly quantify that >$1/bbl benefit.8–10 It will remain hard to perform good maintenance until this value is visible, quantified, accepted and worthwhile. Kern8 explains the common organizational barriers that hinder operations management, process control and IT from acquiring their own economics to optimize risky CV/KPI tradeoffs and align their hydrocarbon processing with their economics.6,7 I also agree with Deshpande9 on the importance of combining CV mean target setting with variance reduction.3,6,7 All I am recommending is better financial quantification11–17 of benefit – cost = profit, to sharpen the decisions Friedman proposes.11–17 I find keeping financial score correctly is a useful and pragmatic way to communicate with management and customers about the value of APC work and techniques. I think process control engineers should know process economics intimately. I fail to find any disagreement from Friedman’s writings on that idea. I renew my pleas to the HPI to adopt a clear way to measure performance value before spending money on automation.2,3,5–7,12–17

One must quantify the Clifftent profit tradeoff for each risky CV/ KPI to have any hope of setting means properly, assessing the value of variance reduction and justifying control system and IT expenses. I will identify the panacea in Part 2 next month. HP LITERATURE CITED Friedman, Y. Z., “APC designs for minimum maintenance—Part 1,” HPIn Control Editorial, Hydrocarbon Processing, V88, n6, June 2009, p. 90. 2 Latour, P. R., “Does the HPI do its CIM business right?,” HPIn Control guest Editorial, Hydrocarbon Processing, V76, n7, July 1997, pp. 15–16. 3 Latour, P. R., “Demise and keys to the rise of process control,” Hydrocarbon Processing, V85, n6, March 2006, pp. 71–80. 4 Friedman, Y. Z., “Audit your APC applications,” Hydrocarbon Processing, V85, n12, December 2006. 5 Friedman, Y. Z., (and P. R. Latour), “Dr. Pierre Latour’s views on APC,” HPIn Control Editorial, Hydrocarbon Processing, V84, n11, November 2005, pp. 17–18. 6 Latour, P. R., “Set vapor velocity setpoints properly,” Hydrocarbon Processing, V85, n10, October 2006, pp. 51–56. 7 Latour, P. R., “Align HPI operations to economics—Clifftent optimizes risky tradeoffs at limits,” Hydrocarbon Processing, V87, n12, December 2008, pp. 103–111. 8 Kern, A. G., “IT/automation convergence revisited—Keeping automation close-coupled to operation is key,” Hydrocarbon Processing, V88, n6, June 2009, pp. 61–62. 9 Deshpande, P. B. and R. Z. Tantalean, “Unifying framework for six sigma and process control,” Hydrocarbon Processing, V88, n6, June 2009, pp. 73–78. 10 Al-Dossary, A. et al., “Optimize plant performance using dynamic simulation—This plant case history illustrates the benefits,” Hydrocarbon Processing, V88, n6, June 2009, pp. 33–43. 11 Sharpe, J. H. and P. R. Latour, “Calculating Real Dollar Savings from Improved Dynamic Control,” Texas A&M University Annual Instruments and Controls Symposium, College Station, Texas, January 23, 1986. 12 Latour, P. R., “Advanced Computer Control of Oil Refineries—Where We Are, Where We Are Going,” Paper 21, Petroleum Refining Conference, Tokyo, Japan, 1988, The Japan Petroleum Institute, October 21, 1988, and Paper 6A, Fourth Refinery Technology Conference, Center for High Technology, Indian Oil Corporation Ltd., Vadodara, India, June 9, 1989. 13 Latour, P. R., “Modeling Intangible, Hidden Benefits from Better Product Quality Control,” International Conference on Productivity and Quality in the Hydrocarbon Process Industry, Hydrocarbon Processing magazine/Coopers and Lybrand, Houston, Texas, February 27, 1992, Hydrocarbon Processing, V71, n5, May 1992, pp. 61–66. 14 Latour, P. R., “Role of RIS/APC for Running Refineries in the 1990’s,” Fuel Reformulation, V2, n2, March 1992, p. 14. 15 Latour, P. R., “A Personal Vision for Process Automation,” Keynote Guest Speaker, Technology Exchange Symposium, ARCO Chemical Company, Houston Marriott North at Greenspoint, Houston, Texas, October 19, 1992. 16 Latour, P. R., “Role of RIS/APC for Manufacturing RFG/LSD,” National Petroleum Refiners Association 1994 Annual Meeting, San Antonio, Texas, March 21, 1994. 17 Latour, P. R., “CIMFUELS”, bi-monthly contributing editorial, FUEL Reformulation, September 1995 - February 1998. 1

The author, president of CLIFFTENT Inc., is an independent consulting chemical engineer specializing in identifying, capturing and sustaining measurable financial value from HPI dynamic process control, IT and CIM solutions (CLIFFTENT) using performance-based shared risk–shared reward (SR2) technology licensing.

HYDROCARBON PROCESSING OCTOBER 2009

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HPIN ASSOCIATIONS BILLY THINNES, NEWS EDITOR

bt@HydrocarbonProcessing.com

ISA and Chem Show gear up for events The OpenO&M demonstration is a nologies, as well as the broader strategy to The International Society of Automa- collaborative effort with POSC Caesar, secure and manage IT assets in this area. tion (ISA) has announced the co-location Fiatech and key ISO working groups, For more information about ISA Expo of several events throughout the week at as well as leading solutions suppliers. It 2009, visit www.isa.org/expo. ISA Expo 2009 being held October 6–8 will enable the needed sustainable IT at the Reliant Center in Houston, Texas. and IS infrastructure spanning engineer- VMA offering free seminars at Co-locating organizations include ARC ing and construction, operations and Chem Show Advisory Group, Industry2Grid The Valve Manufacturers (I2G), The Measurement, ConAssociation (VMA) will be pretrol and Automation Associasenting two educational sessions tion (MCAA), OpenO&M and on the use of valves and actuaMicrosoft. The co-location of tors in the chemical processthese events with the ISA Fall ing industry during the Chem Training Institute, Industry Show (November 17–19) at Standards Forum, six technical the Javits Convention Center conferences and the exhibition in New York City. brings an abundance of technical Designed for newcomers to content, professional developthe process industries as well as ment and networking opportuexperienced engineers who need nities to attendees. a refresher course in valves and ARC Advisory Group will actuators, attendees will benefit host an Asset Lifecycle Manfrom the free three-hour session agement (ALM) Knowledge that is divided into two parts: Exchange and Technology • Valves 101—A broad Showcase that includes a series overview of the valve industry of educational workshops and with a focus on key elements presentations by ARC analysts, ISA Expo provides a great setting for networking in conjunction with such as valve standards, basic many learning opportunities. end users and leading technolpiping information and applicaogy suppliers. Topics will cover recent maintenance, controls and enterprise tion issues that are critical to effective valve ALM developments and best practices business systems. Rather than having specification and usage. Detailed discussions that enable asset-intensive organizations isolated islands of activity, the suppliers of the major valve types, including gate, to reduce risk, costs, downtime, energy of all major classes of industrial systems globe, check, ball, butterfly, plug, control use and the carbon footprint of complex are now adopting open standards based and pressure relief, will provide attendees capital facilities. interfaces providing the needed levels of with valuable knowledge that can be applied The Committee of the National Institute sustainable interoperability. in their daily PVF work, whether in sales, as of Standards and Technology industry2Grid OpenO&M will also conduct an a specifying engineer or an end user. (I2G) Summit will provide an overview of executive summit for those with strategic • Actuators 101—A description of NIST and I2G SmartGrid activities as well accountability for plant and platform per- the various actions, such as linear, rotary, as a forum for identifying and discussing formance. This summit is designed to help etc., that are employed to operate the valve issues of importance to industry. management understand the business value types discussed in Valves 101. Each actuaMCAA will hold an industry break- of ISA membership and ISA standards tor type (electric, pneumatic, hydraulic) fast. Jeff Dietrich, senior analyst with the activities through an executive level discus- is defined in expanded detail to impart an Institute for Trend Research will update sion and private demonstration of the next understanding of their variations, characMCAA members on the economy and generation of open standards-enabled plant teristics and relative technical matters. It the business cycles of the key customer and platform management solutions. provides sufficient information to educate industries for MCAA products. AdditionMicrosoft will hold its annual World- users about their options and to gain a firm ally, Paul Rasmusson and Mike Willey, wide Manufacturing Operations Forum. understanding of how each actuator type principals of Global Foresight Group, Microsoft will present its role and strategy may apply to their specific needs. will provide a mid-year update to their in manufacturing operations, an updated For more information on the 2009 Chem 2009–2011 market forecast. roadmap of its key products and tech- Show, visit www.chemshow.com. HP ISA Expo 2009

HYDROCARBON PROCESSING OCTOBER 2009

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www.thermo.com/sola to download the application note, SOLA II Trace in Reforming and Isomerization. And have a good day.

Part of Thermo Fisher Scientific Select 115 at www.HydrocarbonProcessing.com/RS

HPIMPACT BILLY THINNES, NEWS EDITOR

BT@HydrocarbonProcessing.com

How natural gas impacts the US economy In advance of the US Senate’s debate on climate change legislation, two studies have been commissioned to emphasize the contributions the natural gas industry makes to the US economy. The first, produced for an interest group called America’s Natural Gas Alliance, was written by IHS Global Insight. The second study was undertaken by PriceWaterhouseCoopers on behalf of the American Petroleum Institute. IHS Global Insight’s study found that valued added economic impact by the natural gas industry to the US economy was at $385 billion for 2008. The term “value added” was quantified as equal to the value of the industry’s output minus the costs of its materials and services inputs. IHS Global Insight also culled numbers from the US Energy Information Administration that indicated natural gas “currently constitutes approximately 25% of total primary energy consumption and 29% of primary energy production in the US, when measured on a Btu-equivalent basis. PriceWaterhouseCoopers went on to extrapolate out natural gas’ impact on future US energy needs by saying that “900 of the next 1,000 US power plants are projected to use natural gas.” The PriceWaterhouseCoopers study combined both the oil and natural gas industries into its analysis, while IHS Global Insight’s research was exclusively focused on natural gas. With that established, it’s easier to understand the differences in employment numbers that each analysis cites. “The US oil and natural gas industry’s total employment contribution to the [US] national economy amounted to 9.2 million full-time and part-time jobs, accounting for 5.2% of the total employment in the country,” PriceWaterhouseCoopers says. Meanwhile, IHS Global Insight’s gas industry only-focus says natural gas-related jobs contribute 2.1% of total US employment. How are these jobs distributed across the states? According to PriceWaterhouseCoopers, the top 15 states with jobs directly or indirectly related to the oil and natural gas industry were, as of 2007: Texas, California, Oklahoma, Louisiana, New York, Penn-

TABLE 1. The total operational impact of the oil and natural gas industry in 2007; listed are the top 15 states, ranked by total employment contribution

State

Employment* Percent of Amount state total

Texas

Labor income** Percent of ($ million) state total

Value added Percent of ($ million) state total

1,772,335

13.1%

140,941

19.5%

293,760

California

752,614

3.7%

54,122

4.6%

100,958

24.2% 5.5%

Oklahoma

348,627

16.3%

22,550

24.7%

47,839

31.3%

Louisiana

330,053

13.4%

18,449

16.6%

35,986

20.6% 3.3%

New York

281,267

2.6%

21,452

3.0%

36,347

Pennsylvania

271,250

3.8%

14,494

4.1%

25,772

4.8%

Florida

267,277

2.6%

11,441

2.6%

19,946

2.8%

Illinois

260,001

3.5%

16,953

4.2%

31,323

5.0%

Ohio

229,438

3.4%

11,121

3.7%

20,201

4.5%

Colorado

190,408

6.0%

12,438

7.7%

24,099

9.3%

Michigan

179,495

3.3%

9,820

3.8%

17,711

4.4%

Georgia

145,806

2.7%

6,841

2.7%

12,032

3.0%

North Carolina

145,779

2.7%

6,007

2.6%

10,623

2.9%

Virginia

143,479

3.0%

6,923

2.7%

11,968

3.1%

New Jersey

143,342

2.8%

9,461

3.1%

16,853

3.5%

Numbers may not add to total due to rounding * Employment is defined as the number of payroll and self-employed jobs, including part-time jobs. ** Labor income is defined as wages and salaries and benefits as well as proprietors’ income.

TABLE 2. The total operational impact of the oil and natural gas industry in 2007; in this table, the top 15 states are ranked by employment share of state total

State Wyoming

Employment* Percent of Amount state total 71,063

Labor income** Percent of ($ million) state total

18.8%

4,060

24.3%

Value added Percent of ($ million) state total 8,432

29.4%

Oklahoma

348,627

16.3%

22,550

24.7%

47,839

31.3%

Louisiana

330,053

13.4%

18,449

16.6%

35,986

20.6%

Texas

1,772,335

13.1%

140,941

19.5%

293,760

24.2%

Alaska

43,454

9.8%

3,143

13.5%

6,064

16.6%

New Mexico

88,814

8.1%

4,307

9.5%

8,292

12.2%

West Virginia

60,891

6.7%

2,740

7.4%

5,412

9.4%

Kansas

119,051

6.5%

6,738

8.8%

14,029

11.4%

Colorado

190,408

6.0%

12,438

7.7%

24,099

9.3%

North Dakota

27,914

5.7%

1,346

7.6%

2,773

9.6%

Mississippi

83,820

5.5%

3,609

6.5%

7,244

8.4%

Montana

34,210

5.3%

1,584

7.0%

3,324

8.9%

Utah

76,188

4.7%

3,960

5.9%

7,822

7.6%

Arkansas

69,640

4.4%

2,884

4.9%

5,589

6.0%

Nebraska

49,784

4.0%

2,743

5.6%

5,112

6.7%

sylvania, Florida, Illinois, Ohio, Colorado, Michigan, Georgia, North Carolina, Virginia

and New Jersey (Table 1). “Combined, these states account for nearly 70% of the total HYDROCARBON PROCESSING OCTOBER 2009

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HPIMPACT jobs attributable to the US oil and natural gas industry’s operations,” the study says. Taking another tack on the same point for further emphasis, PriceWaterhouseCoopers points out that the oil and natural gas industry supported 4% or more of the total employment in 15 states in 2007 (Table 2).

Mixed outlook, at best, for LNG projects in Iran

ing the construction of sweetening and liquefaction units and a completion date for the project remains uncertain. So while construction is proceeding at an up and down pace, FGE sees the major challenge to completion of the Iran LNG project (and the other projects, as well) to be on the technical side. “Although the plant will use Linde liquefaction technology (applied in Norway’s SnØhvit with poor performance) there are serious concerns in providing the equipment for liquefaction units and in achieving successful technical results from the liquefaction units, which will be procured and constructed by local and/or Asian contractors,” the report says. It goes on to speculate that, if Iran could bring in a company with significant LNG experience as a partner on the project, “commercial and construction activities would proceed much faster.” Pars LNG and Persian LNG are slightly behind the Iran LNG project but no less important. These projects involve Total, Petronas, Shell and Repsol. But the problem with these projects and all of the Iranian LNG projects are political issues. “Total and Shell declared several times that they will consider the political parameters in their FIDs for these projects,” the report says. The combination of a volatile Iranian political situation extending indefinitely into the future and a lack of technical wherewithal on some aspects of LNG production could stall Iran’s LNG exporting capabilities for some time. For instance, FGE reports that “within the current political environment, the partners for Pars LNG and Persian LNG are under increasing pressure to slow down work and/or opt out from the projects. This is not necessarily resulting from sanctions, but from informal pressure by the US Treasury and key European countries.” On the technical conundrum side, FGE’s analysis indicates that, if the technical challenges can be solved on the Iran

LNG project, it might be eyeing a 2016 startup, at the earliest. Pars LNG and Persian LNG startups are projected for 2017 under a best-case scenario. Finally, the startup for the remaining projects is deemed unlikely in the foreseeable future given current technical, political, financing and marketing problems.

US EPA and NHTSA propose program to reduce greenhouse gases and improve fuel economy

Within the last decade, Iran has proposed a number of LNG projects that were targeted to supply 73 million tpy of new LNG into the global market. However, a The US Environmental Protection new analysis from FACTS Global Energy Agency (EPA) and the Department of (FGE), headquartered in Singapore, states Transportation’s National Highway Traffic that Iran is facing various challenges on Safety Administration (NHTSA) are issuthese projects and predicts that these chaling a joint proposal to establish a program lenges will cause long delays and possibly consisting of new standards for model year cancellation of some of the LNG projects. 2012 through 2016 light-duty vehicles that The analysis proceeds from Iranian LNG will reduce greenhouse gas emissions and projects most likely to succeed to those that improve fuel economy. The EPA is proposappear to be long shots at best. The projects ing the first-ever US greenhouse gas (GHG) by name (from most likely to least likely) emissions standards under the Clean Air Act, include Iran LNG, Pars LNG, Persian LNG, and NHTSA is proposing Corporate AverNorth Pars, Glosham, Lavan and Qeshm. age Fuel Economy (CAFE) standards under Iran LNG gets top billing because the the Energy Policy and Conservation Act. National Iranian Gas Export Co. is on The standards proposed would apply record as defining this project as one of its to passenger cars, light-duty trucks and top priorities. To that end, it has farmed medium-duty passenger vehicles. They project management out to its subsidiary require these vehicles to meet an estimated Iran LNG Ltd. (ILG), and chief among its combined average emissions level of 250 oversight responsibilities is making sure the grams of carbon dioxide (CO2) per mile in two centerpiece 5.4 million-tpy trains are model year 2016. completed successfully. FGE believes this project to be the most advanced in Iran EPA’s proposed standards. The EPA due to the country having already spent is proposing a set of fleet-wide average CO2 $1 billion on it. Engineering, procurement emission standards for cars and trucks. These and construction (EPC) contracts have standards are based on CO2 emissionsalready been delegated to several consorfootprint curves, where each vehicle has a tiums. Rah Sahel and Daelim were signed different CO2 emissions compliance target up to build LNG tanks and marine facilidepending on its footprint value (related to ties. Farab got an EPC deal to construct the size of the vehicle). Generally, the larger some gas sweetening units. In a big step in the vehicle footprint, the higher the corre2008, a consortium of Pars International sponding vehicle CO2 emissions target. and Development Co., HuaFu EngineerTable 3 shows the projected fleet-wide ing Co. and Farab was enlisted to CO2 emission level requirements build liquefaction units. for cars under the proposed TABLE 3. The US EPA’s projected US fleet-wide With all the EPC deals flying emissions compliance levels under the proposed footprint-based approach. These around and money allocated, footprint-based CO2 standards (g/mi) and corresponding requirements are projected to where does this project stand? fuel economy (mpg) increase in stringency from 261 ILG announced in August 2009 grams per mile (g/mi) to 224 g/ 2012 2013 2014 2015 2016 mi between model year 2012 and that the project was 24% complete. While serious dents were Passenger cars (g/mi) model year 2016. Similarly, fleet261 253 246 235 224 made in necessary work on the Light trucks (g/mi) wide CO2 equivalent emission 352 341 332 317 302 port, utility and LNG storage Combined cars & trucks (g/mi) level requirements for trucks are 295 286 276 263 250 tank fronts, things have proprojected to increase in stringency Combined cars & trucks (mpg) 30.1 31.1 32.2 33.8 35.5 gressed at a woeful level regardfrom 352 g/mi to 302 g/mi. HP HYDROCARBON PROCESSING OCTOBER 2009

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VIEWPOINT ROBERT A. ASHWORTH, GUEST COLUMNIST bobashworth@earthlink.net

Ozone destruction major cause of warming!— Part 1 Some say human-made (anthropogenic) carbon dioxide (CO2) emissions caused the earth to warm. Others say there is no abnormality at all, that it is just natural warming. A greater than normal warming did occur in recent times, but no measurements confirm an increase in CO2 emissions had any discernible effect on global temperatures. There is, however, very strong evidence that anthropogenic emissions of chlorofluorocarbons (CFCs) were the major cause of the recent warming. CFCs have created unnatural upper-atmosphere cooling and lower-atmosphere plus earth warming based on these facts: • CFCs destroyed ozone in the lower stratosphere–upper troposphere causing these zones to cool 1.37°C from 1966 to 1998. • Mass and energy balances show that the energy absorbed in the lower stratosphere–upper troposphere was 1.71 x 1018 Btu more in 1966 than it was in 1998. • The loss of ozone in the upper atmosphere allowed more UV light to hit and warm the lower troposphere plus 10 in. of the earth (land + sea) by 0.48°C (1966 to 1998). • Greater ozone depletion in the polar regions caused them to warm some two and one-half times that of the average earth temperature (1.2°C vs. 0.48°C). This caused permafrost to melt, which is releasing methane at a very high rate. • There is a temperature anomaly in Antarctica. Signey Island, north of the South Pole, warmed like the rest of the polar regions. South at Vostok, there was a cooling effect. Increased radiation from Vostok (some 11,400 feet above sea level) to outer space is most likely the cause due to the large ozone hole there, especially since this phenomenon occurred over the same period that stratospheric ozone destruction took place. No empirical evidence for CO2 causing warming.

Recent empirical data show that atmospheric CO2 concentrations have no discernible effect on global temperature (Fig. 1).1 The land-sea temperature plot shown is from the United Kingdom’s Hadley Climate Research Unit. The CO2 plot is from the Mauna Loa Observatory in Hawaii. CO2 levels increased some 20 ppmv

Temperature, °C

1.0 390 0.8 385 0.6 380 0.4 375 0.2 3.70 0.0 365 Land sea temperature CO2 in atmosphere -0.2 360 -0.4 355 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Calendar year

FIG. 1

Earth temperature and CO2 concentration 1998–2008.

CO2, ppmv

No correlation between CO2 and earth temperature

over the past 10 years; however, global temperatures did not increase as predicted by the Intergovernmental Panel on Climate Change (IPCC) models—they fell! The earth’s temperature in January 1998 was some 0.6°C hotter compared to January 2008. Humans account for 2.9% of the total CO2 emitted to the atmosphere.2 If we eliminated all of the global anthropogenic CO2 from the atmosphere we would go back to the level we had in 2002. The yearly average temperature in 2002 was warmer than in 2007! Besides CO2 increasing in the atmosphere, atmospheric concentrations of methane have increased from preindustrial time (700 ppbv) to 1,745 ppbv in 1998.3 In 2000, methane concentrations leveled off at 1,755 ppbv and are slowly dropping. Two years earlier, stratospheric CFC concentrations leveled off and started to drop slowly; so the evidence suggests that methane emissions are tied to ozone depletion. Where is the methane coming from? A recent study showed that permafrost melting in North Siberia is releasing methane from thawing lakes that has been sequestered there since the Pleistocene era (10,000 to 1,000,000 years ago).4 Researchers estimate that methane carbon is being emitted at a rate some 100 times the rate of carbon released from burning fossil fuels. Methane (CH4) slowly converts to CO2 in the atmosphere. This extra methane release appears to be the major cause of increasing CO2 concentrations in the atmosphere. Part 2 of this editorial will be published in the November issue. HP 1

LITERATURE CITED D’Aleo, J. S., “Correlation Last Decade and This Century CO2 and Global Temperatures Not There,” http://icecap.us/images/uploads/Correlation_ Last_Decade.pdf. Intergovernmental Panel on Climate Change, Climate Change 2001: The Scientific Basis (Cambridge, UK) Cambridge University Press, 2001), Figure 3.1, p. 188. Houghton, J. T., et. al.,”Climate Change 2001: The Scientific Basis of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, UK, pp. 944, 2001. Walter, K. M., et al. “Methane bubbling from Siberian thaw lakes as a positive feedback to climate warming,” Nature 443, 71–75, Sept. 7, 2006.

The author is a chemical engineering graduate from West Virginia University (BS 1960) and has presented over 50 technical papers on fuels and environmental controls. Relating to the subject of global warming, he has written two papers, “CFC Destruction of Ozone – Major Cause of Recent Global Warming” and “No Evidence to Support Carbon Dioxide Causing Global Warming.” Mr. Ashworth is a member of the American Geophysical Union. He is a dissenter in the US Senate Minority Report: More Than 700 International Scientists Dissent Over Man-Made Global Warming Claims - Scientists Continue to Debunk “Consensus” in 2008 and 2009. Mr. Ashworth was one of 115 scientists who signed the Cato Institute newspaper advertisement to President-Elect Obama’s attention debunking CO2 causing global warming. In his present position as senior vice president—technology for ClearStack Combustion Houston, his fortes design, and Tim LloydCorp., Wright is HP’sTexas, European Editor are and conceptual has been active as amass reporter energy balances and analysis. Mr. Ashworth holdsindustry 16 US patents. ClearStack is and conference chairdata in the European downstream since 1997, before working commercialize two of hisreporter patents, for a three-stage oxidation press technique that which hetowas a feature writer and the UK broadsheet and BBC reduces sulfur dioxide, nitrogen oxides mercury a dry scrubber that removes radio. Mr. Wright lives in Sweden and isand founder of aand local climate and sustainability nitrogen initiative. and sulfur oxides from flue gas. In 2001, Governor Paul Patton commissioned him a Kentucky Colonel for his work on clean coal technology. HYDROCARBON PROCESSING OCTOBER 2009

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Knowledge is power. At URS, we believe that the more involved you are with a process from beginning to end, the better equipped you are to provide solutions. So when it comes to meeting today’s increasing demand for oil and gas, we bring proven experience with us — from oil sands, gas production, and petroleum reﬁning to constructing a facility, maintaining one, or developing an expansion plan. Which is why, in the Industrial & Commercial sector, more people are turning to us to get it done. We are URS.

POWER INFRASTRUCTURE FEDERAL INDUSTRIAL & COMMERCIAL

URSCORP.COM

Select 108 at www.HydrocarbonProcessing.com/RS

HPIN CONSTRUCTION BILLY THINNES, NEWS EDITOR BT@HydrocarbonProcessing.com

North America The Aker Solutions and IHI partnership has received a “signed notice of substantial completion” for the Cameron LNG liquefied natural gas receipt terminal near Lake Charles, Louisiana. The Cameron LNG terminal project combined Aker Solutions’ expertise in regasification engineering, construction management, commissioning and startup with IHI’s design and manufacturing knowledge of LNG storage and processing systems. The terminal is capable of processing 1.5 Bscf/d of natural gas. US Development Group LLC will begin construction on the West Colton rail terminal, a new ethanol hub located in the Inland Empire area of southern California. Construction of the facility will occur in two phases. The first phase, located in Rialto, California, will consist of a manifold transfer system that will begin receiving and offloading ethanol railcars in the fall of 2009. The second phase includes full unit train capability and ethanol storage. It will be located on an adjacent site in Colton, California, and is scheduled for completion in mid-2010. The Phase 1 facility will have the capacity to handle the current Colton area demand for ethanol plus that required to meet the 2010 mandated increase to a 10% blend. Dynamic Fuels has awarded Emerson Process Management the contract to digitally automate its commercial-scale renewable diesel plant. With Emerson’s digital architecture, Dynamic Fuels plans to use thousands of Emerson smart devices, systems, and predictive maintenance software. The $138 million facility in Geismar, Louisiana, which is scheduled to begin operations in early 2010, will use Syntroleum’s biofuels manufacturing process, with plans to convert animal fats and greases into 75 million gpy of renewable diesel fuel. With some plant modifications, the Geismar facility can also produce renewable jet fuel. Foster Wheeler USA Corp. has a detailed design contract with a confidential client for a chemicals project in the United States. The total installed cost for the project is estimated at $100 million. Foster

Wheeler’s contract value, which was not disclosed, was included in the company’s second-quarter 2009 results.

Technip’s operating center in Düsseldorf, Germany, will execute the contract, which is scheduled to be completed in the fourth quarter of 2010.

Europe Alfa Laval has received an order for compact heat exchangers from one of the major refineries in Russia. The order value is about SEK 110 million and delivery is scheduled for 2010. The Alfa Laval compact heat exchangers will be used for preheating the crude before it goes into one of the main distillation processes. Alfa Laval predicts its heat exchangers will allow the Russian refinery to reduce its consumption by 340 MW and its CO2 emissions by 850,000 tpy. Technip has an engineering, procurement and construction management contract with Shell for the first phase of Shell’s Connect project in Germany. By connecting two existing refineries in Godorf and Wesseling, Shell plans to create the largest refinery in Germany. It will be called the Rheinland refinery. TREND ANALYSIS FORECASTING Hydrocarbon Processing maintains an extensive database of historical HPI project information. Current project activity is published three times a year in the HPI Construction Boxscore. When a project is completed, it is removed from current listings and retained in a database. The database is a 35-year compilation of projects by type, operating company, licensor, engineering/constructor, location, etc. Many companies use the historical data for trending or sales forecasting. The historical information is available in comma-delimited or Excel® and can be custom sorted to suit your needs. The cost of the sort depends on the size and complexity of the sort you request and whether a customized program must be written. You can focus on a narrow request such as the history of a particular type of project or you can obtain the entire 35-year Boxscore database, or portions thereof. Simply send a clear description of the data you need and you will receive a prompt cost quotation. Contact: Lee Nichols P. O. Box 2608 Houston, Texas, 77252-2608 Fax: 713-525-4626 e-mail: Lee.Nichols@gulfpub.com.

Shell plans to build a new hydrodesulfurization plant at its Pernis refinery in The Netherlands. The plant, expected to come onstream in the second half of 2011, will increase cleaner-burning, low-sulfur fuels production at the 400,000-bpd refinery. At the peak of construction activity, about 1,300 extra people will work on the Shell Pernis site, in addition to the regular Shell workforce of 2,100. Shell is starting construction of a major new lubricants blending plant in Russia. The plant, which is being built in Torzhok, Russia, will have a capacity of 180,000 tpy. Commercial operation is expected to begin by the end of 2010. The plant will increase Shell’s ability to provide motor oils, transport oils and industrial lubricants to the Russian market. Initial plans for the plant call for it to use advanced operational and organizational technologies and to employ a Russian workforce of around 150.

Middle East KBR has a contract with ConocoPhillips and Saudi Aramco to provide detailed engineering and procurement services for the utilities package and the interconnecting systems and pipe racks for the companies’ joint Yanbu export refinery project. The project is under an engineering, procurement and construction tendering process for the final investment decision by the project sponsors, and consists of a 400,000-bpd, full-conversion refinery in Yanbu Industrial City, Saudi Arabia. This award is an extension of KBR’s current project management contract with ConocoPhillips and Saudi Aramco and it follows the completion of front-end engineering and design services for the Yanbu refinery by KBR. Fluor Corp. recently completed the multibillion-dollar Olefins II project for a joint venture of Dow Chemical Co., Petrochemical Industries Co., Bubyan Petrochemical and Qurain Petrochemical Industries in Shuaiba, Kuwait, approximately 25 miles south of Kuwait City. Fluor HYDROCARBON PROCESSING OCTOBER 2009

I 25

)DEAS FOR GROWTH )DEAS FOR SUSTAINABILITY

!MMONIA &ERTILIZER s 3YNGAS s (YDROGEN s 2ElNING

/RGANIC #HEMICALS s /LElNS s #OAL 'ASIlCATION s #ARBON #APTURE 3TORAGE

KBR Technology specializes in developing and licensing process technologies worldwide. From refining to ammonia, from chemicals to coal gasification, from olefins to syngas, KBR Technology helps you accelerate profitability and sustain growth. For more information, visit technology.kbr.com/HP or email technology@kbr.com © 2009 KBR All Rights Reserved K09124 10/09

Select 89 at www.HydrocarbonProcessing.com/RS

HPIN CONSTRUCTION began work on the project in July 2004 by providing overall management consultancy and front-end engineering and design work for utilities and infrastructure. The Olefins II project was completed in June 2009 with a safety record that achieved 42 million safe hours without a lost-time incident. Saudi Basic Industries Corp. (SABIC) and Mitsubishi Rayon Co. Ltd. (MRC) have agreed to start a joint venture in Saudi Arabia. The document signed by the companies outlines the principal terms of the proposed $1 billion joint venture including details related to structure, technology, marketing and feedstock supply. Startup is targeted for 2013. Under the agreement, SABIC and MRC will utilize Luciteâ&#x20AC;&#x2122;s ethylene-based process to manufacture methyl methacrylate monomer, with a design capacity of 250,000 metric tpy. Also, the joint venture company will manufacture polymethyl methacrylate, with a design capacity of 30,000 metric tpy. SABIC will be responsible for the supply of key raw materials such as ethylene and

methanol. SABIC and MRC will also explore the possibilities to produce other products.

Asia-Pacific Toyo Engineering Corp. has a contract with BASF-YPC Co. Ltd. for works related to the construction of a petrochemical plant in Nanjing, China. This project aims to meet the increasing demand in China by adding and expanding the capacity of 14 process units as well as the utility and offsite facilities. The completion of the project is expected in the second half of 2011. In implementing the project, Toyo will form an integrated project team with Fluor Corp. and Daelim Industrial Co. Ltd. to manage the project. Burckhardt Compression has an order from PetroChina LNG Jiangsu Co. Ltd. and PetroChina LNG Dalian Co. Ltd. to deliver a total of six labyrinth piston compressors for the LNG terminals in Rudong, Jiangsu Province, China, and in Dalian, Liaoning Province, China. The contractor responsible for the two projects is China Huanqiu Contracting & Engineering Corp.

Select 152 at www.HydrocarbonProcessing.com/RS

Each LNG terminal will operate three labyrinth piston compressors. The delivery will take place mid-2010. In a first phase, the Jiangsu LNG terminal will be built with 3.5 million tpy import capacity and is expected to be expanded to 6 million tpy in a second phase. PetroChina will use the new terminal to receive 3 million tons of LNG from Qatar. The Dalian LNG terminal is designed to receive 3 million tpy of LNG in the first phase. The volume is expected to rise to 6.5 million tpy in the second phase. Both LNG terminals are scheduled to be in operation in the first quarter of 2011. PT Pertamina plans to build a refinery in Banten Bay, Indonesia. It has established a joint venture with Oil Refining Industries Development Co. and Petrofield to achieve this goal. The refinery is expected to be in operation in 2014 and produce approximately 150,000 bpd. Tentative cost estimates price the refinery at $6 billion. When the refinery is finished, Iran is expected to supply half of the plantâ&#x20AC;&#x2122;s crude oil. HP

HYDROCARBON PROCESSING OCTOBER 2009

I 27

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4PMVUJPOT SBOHF GSPN CFBSJOH VQHSBEFT UP IJHIMZ BEWBODFE BTTFU NBOBHFNFOU BQQSPBDIFT BOE GSPN BVUPNBUJD MVCSJDBUJPO TZTUFNT UP UIF JOEVTUSZÂµT MFBEJOH DPOEJUJPO NPOJUPSJOH TZTUFNT CPUI IBOEIFME BOE POMJOF 'PS B OP QSFTTVSF EJTDVTTJPO PG ZPVS DIBMMFOHFT BOE IPX XF DBO IFMQ ZPV NFFU UIFN UBML UP ZPVS 4,' SFQSFTFOUBUJWF Select 54 at www.HydrocarbonProcessing.com/RS

HPI CONSTRUCTION BOXSCORE UPDATE Company

Plant Site

Project

Capacity Est. Cost Status Licensor

Engineering

Constructor

ACSA Fluor Shaw

ACSA Fluor

UNITED STATES Colorado Louisiana Louisiana Louisiana Ohio Texas Texas West Virginia Wyoming

Solix Biofuels Terra Industries Inc Marathon Petroleum Lion Copolymer Marathon Petroleum Air Products MarkWest Energy Partners MarkWest/Chesapeake/StatoilHydro Williams Energy

Coyote Gulch Donaldsonville Garyville Geismar Canton Corpus Christi Houston Majorsville Wamsutter

Biofuel Plant Ammonia Hydrotreater, Naphtha Maintenance Services Hydrotreater, Distillate Steam Methane Reformer Gas Fractionation Gas Processing, Cryogenic Gas Processing

3 Mgpy RE 1633 m-tpd 40 Mbpd None 18 Mbpd 30 MMscfd 37 Mbpd 120 MMscfd 350 MMcfd

Brandon

Urea

Porto Alegre Undisclosed Undisclosed Paramaribo

Compressor, frame maintenace Heat exchanger (1) Heat exchanger (2) Refinery EX

Mozyr Mozyr Mozyr Mozyr Mozyr Paris Thessaloniki Thessaloniki Nevinnomyssk Novomoskovsk Portovaya

Sulfur Recovery (1) Sulfur Recovery (2) Sulfur Recovery (3) Sulfur Recovery (4) Treater, Tail Gas Ammonia Crude Unit Diesel, ULSD Urea (2) Urea (4) Gas Dewpointing

U S U M U

2009 Solix Biofuels 2011 ACSA 2009 2012 2009 2010 S 2010 P 2010 U 2010

Burns & McDon

OPD

S 2012 UCSA

UCSA

U E E U

2009 2011 2011 2013

Burckhardt Compression Alfa Laval Alfa Laval Aker Solutions

CANADA Manitoba

Koch Nitrogen

280 m-tpd

LATIN AMERICA Brazil Brazil Brazil Surinam

Braskem SA Petrobras Petrobras Staatsolie

None None None 15 Mbpd

55 55

EUROPE Belarus Belarus Belarus Belarus Belarus France Greece Greece Russian Federation Russian Federation Russian Federation

Mozyr Refinery Mozyr Refinery Mozyr Refinery Mozyr Refinery Mozyr Refinery GNP SA Hellenic Petroleum SA Hellenic Petroleum SA Nevinnomyssk Novomoskovsk Azot Gazprom

RE TO BY RE RE

120 120 78 78 240 1380 26 9.6 1800 1150 6

tpd tpd tpd tpd tpd m-tpd Mbpsd Mbpsd m-tpd m-tpd Bcfd

F F F F F S U C E E E

2010 2010 2010 2010 2010 2011 2010 2009 2010 2010 2011

Siirtec Nigi Siirtec Nigi Siirtec Nigi Siirtec Nigi Siirtec Nigi ACSA ExxonMobil ExxonMobil UCSA UCSA GL UK

Siirtec Nigi Siirtec Nigi Siirtec Nigi Siirtec Nigi Siirtec Nigi ACSA Foster Wheeler Italiana Asprofos UCSA UCSA Siirtec Nigi

3.1 1 1 210 1150 2 1000 1350 50

Mm-tpy 3500 MMtpy 3500 MMtpy 3500 MMcfd m-tpd Mm-tpd m-tpd m-tpd kty 126

E E E E E F E E C

2013 2013 2013 2010 2011 2012 2011 2011 2009

Shell Stamicarbon Stamicarbon

Samsung Eng Samsung Eng Samsung Eng Clough ACSA MCSA ACSA ACSA Lanzhou Petrochemical

None None m-tpd None tpd tpd tpd MMtpy MMtpy None MMtpy MMtpy MMtpy MMtpy Mbpd

E E E E F F F F U U U U U U U

2011 2011 2010 2010 2009 2009 2009 2012 2010 2009 2010 2010 2010 2010 2009

Advanced Holdings Advanced Holdings ACSA Advanced Holdings Siirtec Nigi Siirtec Nigi Siirtec Nigi Indian Oil UOP Technip UOP UOP UOP UOP Fuji Oil Co Ltd

ACSA Bioteck A.E. UCSA UCSA

ASIA/PACIFIC Australia Australia Australia Australia Australia China China China China

Perdaman Perdaman Perdaman Apache/Santos JV Orica Australia Party Ltd Xuzhou Coal Mining Guodian Chifeng Chemical Co Shilien Chemical CNPC

Shotts Industrial Park Shotts Industrial Park Shotts Industrial Park Devil Creek Kooragang Island Baoji Chifeng City Huainan Lanzhou

China China China India India India India India India India India India India India Japan

PetroChina PetroChina Yankuang Group BRPL Mangalore Rfg & Petrochemicals Mangalore Rfg & Petrochemicals Mangalore Rfg & Petrochemicals Indian Oil Corp Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Fuji Oil Co Ltd

Liaoning Sichuan Urumqi Assam Mangalore Mangalore Mangalore Paradip Vadinar Vadinar Vadinar Vadinar Vadinar Vadinar Sodegaura

Coal Gasification (1) Urea (1) Urea (2) Gas Processing Ammonia (2) Methanol Ammonia Ammonia NBR - Nitrile Butadiene Acrylonitrile Rubber Processing Equipment Processing Equipment Ammonia Blending Treater, Tail Gas (1) Treater, Tail Gas (2) Treater, Tail Gas (3) Coker, Delayed Amine Regeneration Unit Hydrogen Hydrotreater, ATF Hydrotreater, Diesel 2 Hydrotreater, VGO (1) Sour Water Stripper Cracker, Thermal (1)

Sonatrach CNPC/Sonatrach CNPC/Sonatrach

Skikda Skikda Skikda

Coker, Delayed Storage, Butane Storage, Diesel

Shiraz Haifa Fahahil Stripping Plant Al Jubail Al Jubail Al Jubail Al Jubail Al Jubail Al Jubail Jubail Ind City Jubail Ind City Jubail Ind City Jubail Ind City Jubail Ind City

Urea Xylene, Para Glycol Regeneration Train (2) Air Separation Unit (2) Benzene CCR Methanol Nitrogen Train (2) Oxygen Train (2) Coker Controls, Process Cracker, Catalytic Hydrocracker Utilities

RE TO TO TO

RE 1000 185 185 185 4.1 7.8

1 3.8 6.4 2 30

350

ACSA MCSA ACSA ACSA Lanzhou Petrochemical

IKPT IKPT IKPT Clough ACSA MCSA ACSA ACSA

Advanced Holdings ACSA

ACSA

Siirtec Nigi Siirtec Nigi Siirtec Nigi FW Technip Technip Technip Technip Technip Technip Chiyoda

Jacobs Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Essar Oil Ltd Chiyoda

E 2012 Axens\GTC, Inc C 2009 C 2009

Samsung Eng CPECC CPECC

U U E E U E E E E E E E E E

PIDEC

ECC Staff Kentz E&C Samsung Constr Samsung Eng Samsung Eng MCSA Samsung Eng Samsung Eng Chiyoda\Samsung Eng

AFRICA Algeria Algeria Algeria

30 Mbpd None None

385 385

MIDDLE EAST Iran Israel Qatar Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia

PIDMCO GADIV Petrochemical Qatar Petroleum SABIC SATORP SATORP IBN SINA SABIC SABIC SATORP SATORP SATORP SATORP SATORP

3250 m-tpy 40 Mtpy None 3.6 Mtpd 140 Mm-tpy 67.3 Mbpd RE 3 Mm-tpd 3.6 Mtpy 3.6 Mtpy 103 Mbpd None None None None

EX RE

27 95 300 700

2011 2010 2011 2011 2013 2013 2012 2011 2011 2013 2013 2013 2013 2013

Toyo-Thai

APCI Axens Axens MCSA APCI APCI UOP\FW Technip Technip

WorleyParsons Samsung Eng Samsung Eng Samsung Eng MCSA Samsung Eng Samsung Eng Samsung Eng\Chiyoda Technip Technip Technip Technip

See http://www.HydrocarbonProcessing.com/bxsymbols for licensor, engineering and construction companiesâ&#x20AC;&#x2122; abbreviations, along with the complete update of the HPI Construction Boxscore. HYDROCARBON PROCESSING OCTOBER 2009

I 29

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PROCESS CONTROL AND INFORMATION SYSTEMS

SPECIALREPORT

Practical process control system metrics Here are several useful examples A. G. KERN, Tesoro Corp., Los Angeles, California

ontrol system metrics can be highly effective in managing a process control system. They help ensure overall system health and integrity, focus available technical support resources on the highest-priority areas and serve to put all stakeholders on the same page with regard to control system performance issues. The metrics approach can be applied to control systems of any vintage. New control systems normally don’t achieve peak integrity or functionality for several years. Metrics can accelerate the maturation process and go on to help sustain peak performance over the control system life. For older control systems, metrics can be used to assess their integrity and manage risk area improvement. Control system metrics monitor fundamental control system health, like measuring vital signs of a person. When they are within healthy limits, the system can be expected to continue to perform reliably, but when they fall to risky levels, further investigation and treatment are needed to bring risk back to acceptable levels. Control system metrics serve this role for control system health. Metrics answer suppositions such as, if our regulatory controls are sound, most control valves will be in automatic; if our safety systems are intact, few functions will be in bypass; and if our alarm management has been effective, our alarm rates will be within operable limits. Extensive engineering efforts go into these areas, but sanity checks such as these are often overlooked, setting the stage for unwanted incidents or performance headaches to reveal the gaps. Metrics reveal the gaps proactively by gauging success in fundamental ways, regardless of the engineering approach (proven or novel) employed. Once in place, metrics guard against long-term performance degradation, which is another concern affecting many control system aspects. Control system metrics, a.k.a. key performance indicators (KPIs), have been popular in recent years, but many initiatives have stalled due to some common mistakes. This article helps to avoid the mistakes, identifies guiding principles and provides several useful example metrics. Principles and mistakes. Control system metrics are about

the control system, not the people. Many efforts have derailed due to concerns about metrics reflecting on individual job performance. In practice, nearly all metrics have shared responsibility between engineering, operations and maintenance. And nearly all “below-target” results pose business challenges, not individual ones. By selecting fundamental metrics and tackling risk areas systematically, it stays about the control system, not the people.

■ Metrics answer suppositions such as, if

our regulatory controls are sound, most control valves will be in automatic; if our safety systems are intact, few functions will be in bypass; and if our alarm management has been effective, our alarm rates will be within operable limits. It’s all about control system health, not control loop performance. Control loop performance is only one of several metrics, and not an especially critical one at that, relative to system faults, safety functions, alarm rates, etc. But the common preoccupation with loop performance, especially when coupled with the first mistake about people, has killed many attempts to deploy online metrics before ever getting beyond this first one. Simple and objective techniques to address this metric are included in the discussion below. It’s about saving time and resources, not consuming them. Avoid deploying metrics that contribute little to control system improvement while saddling personnel with application support and manual data collection and reporting duties. Instead, deploy fully automated metrics utilizing existing DCS/historian capabilities. This is inherently robust and effective. In addition, automated and historized metrics provide their own benchmarks and trends provide essential insight for addressing shortfalls. Go gradually. Avoid attempts to engineer all the metrics before all the lessons have been learned. Start with one or two metrics to address the biggest concerns. Build on success with additional metrics while keeping the original ones in place to sustain longterm control system health. Overall, the strategy is to create a minimal set of metrics, with each one representing some fundamental aspect of control system integrity or functionality. The key metrics are: Control valves in automatic: Not to be confused with

control loop performance, this metric measures basic control valve asset utilization—are they under automatic control or are they in manual and, therefore, failing to earn a real-time return on their investment? Measuring higher levels of control, i.e., the troubleHYDROCARBON PROCESSING OCTOBER 2009

I 31

SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS

some control loop performance issue, has steeply diminishing returns, while this simple metric answers 90% of the question. This metric is traditionally implemented based on controller mode (in this case, of only controllers directly attached to valves), but is better implemented based on the actual output value—if it is changing, the valve is in automatic; if it is not changing, the valve is in manual. This approach is more generic, captures valves that spend time saturated and side-steps the sticky issue of “normal mode.”

applications that utilize flow data, whether directly (such as advanced control) or indirectly via the historian (such as LPs, simulations, efficiency monitoring and design or trouble-shooting activities). The concept is to implement mass balance equations around each vessel, plant and utility system. Although most facilities will claim to have a firm handle on their mass balances, an online, drilldown, robust application with good closure remains an industry rarity. This metric provides a positive path to this capability. All DCSs since 1980 have built-in pressure and temperature Mass balance closure: This metric amounts to “poor man’s compensation functions for flow. These can be used to improve data reconciliation,” plus it works. It serves to improve flow meterfrom an initial target closure of 3% to an optimum range of less ing performance across a facility and contributes to the integrity of than 1%, an achievement most facilities could be proud of, especially in a robust online format. TABLE 1. Summary of example metrics Where flow measurements are missing, they can sometimes be estimated (for examMetric Target Objectives ple, by a heat exchanger energy balance or a Control valves Target: >75% Indicates the condition and utilization of control valves and other finalvalve sizing analysis). Or, the missing flow in automatic control elements. can be calculated to close the mass balance. Optimum: >90% A high number indicates reliable performance, good asset utilization In these cases, the quality is set to “fair” or from control and operation standpoints and high return on investment. “poor” to bring attention to the missing A low number indicates unreliable or unsuitable control valves or poor measurement, which may be needed for regulatory control design. more rigorous offline data reconciliation or Safety functions Target: 0 Indicates reliability and availability of safety functions. loss-accounting calculations. in bypass Safety functions in bypass: This metric A high number indicates safety system degradation, usually due to doesn’t assure that safety systems are technifield instrument issues or conflicts between operating needs and cally correct or built according to best practice, safety system design. but it assures that the expected safety functions A low number indicates successful safety system deployment and are available. Safety functions have bypasses to confidence that expected safety functions are available. facilitate testing and repair, but often (though Mass balance Target: <3% Indicates overall integrity of flow meters and mass balance data, usually incorrectly) bypasses are also used durclosure both current and historical. ing nonroutine operations, such as startup, Optimum: <1% A low number indicates reliable flow instruments and reliable flow data shutdown or upsets. Or they may be kept in for all data users. bypass for long periods due to design or field A high number indicates poor flow monitoring and introduces error in instrument problems. This metric sheds light related applications. on such practices and helps address them. System faults Target: 0 Indicates control system hardware and communications interface It’s a growing industry practice to comhealth from a systems standpoint. pletely eliminate the use of bypasses except A low number indicates good control system reliability with low risk as intended (for testing or repair). This is of unexpected failures. being accomplished with a combination of A high number indicates control system reliability problems with risk more sophisticated safety function logic, to availability. startup permissives that (at least) minimize Disabled alarms Site specific Indicates integrity of alarm design, alarm management process, and of effective bypass time and more stringent expectations for successful alarm-driven operation under normal and administrative controls on the use of bypass abnormal conditions. switches. This metric provides necessary Alarm rate Consult industry On-target numbers indicate good alarm integrity and realistic information to manage these issues. guidelines expectation of successful alarm-driven operation. System faults: This metric, like safety Peak rate Off-target numbers indicate alarm design and management bypasses and mass balance closure, tends to shortcomings, with risk to alarm-driven operation during normal or be a historical problem area that responds abnormal conditions. well to visibility. Control system status disControl loop Target: >75% Indicates degree of success with advanced regulatory control and plays should be “all green”, but in many performance (if included) multivariable or other advanced control. control systems, old and new, users live with Optimum: >90% High numbers indicate successful advanced control programs. standing system alarms. Each one represents Low numbers indicate difficulty in deploying and maintaining a risk, desensitizes users to the possibility advanced controls. of compound faults and undermines the Smart field Target: Based Indicates percent of smart transmitters and valves. obvious maintenance mission of keeping the on annual goals system error free. This metric can serve as A high number indicates progress toward capture of productivity and the starting point for hardware and system reliability gains associated with a smart field. maintenance personnel on a daily basis. A low number indicates control system intelligence ends at the rack Alarm-driven operation: These metrics room with lost opportunity regarding improvements associated with are not alarm management, but they indicate if a smart field. your alarm management program is working. 32

I OCTOBER 2009 HYDROCARBON PROCESSING

PROCESS CONTROL AND INFORMATION SYSTEMS

SPECIALREPORT

After integrity of safety systems and regulatory controls, alarm man“MV utilization.”1 At this point, few MPC practitioners still attach agement represents the biggest opportunity for operational improvemuch credibility to “service factor.” HP ment (if done correctly) and the biggest risk to plant availability and LITERATURE CITED preventable incidents (if done incorrectly), because about half of 1 Kern, A. G., “Online monitoring of multivariable control utilization and console operation is alarm-driven (as opposed to procedure-driven). benefits,” Hydrocarbon Processing, October 2005. If the alarm system is unhealthy, operation is compromised. Alarm management has grown to include several schools of thought, but the number of disabled alarms and the alarm rate Allan Kern has 30 years of international process control experi(both hourly average and peak) continue to surface as common ence and is currently working as a lead control systems engineer at denominators in most discussions. Newer DCSs may (should) Tesoro Corporation’s refinery in Los Angeles, California, USA. Mr. do this counting for you, ideally making the data available as Kern is a licensed professional engineer, an ISA Senior Member and historizable tags (ditto for system faults as mentioned previously). a 1981 graduate of the University of Wyoming. A related metric worth considering is operator action rate, i.e., the hourly number of changes to mode, setpoint and outputs. Smart field: This metric indicates the extent to which control system intelligence has been extended beyond the rack room to the field in the form of smart valves and transmitters. Relying on “dumb” field devices or “dumb” communication interfaces is similar to relying on old analog or pneumatic control systems—it’s so last millennium. Lack of field smarts means control system intelligence ends at the rack room and does not extend to the field, with corresponding limitations on productivity, safety, availability and predictive maintenance capabilities. The metric is calculated as the percentage of smart transmitters and valves with smart interMSA Ultima® X Series faces—score 0% for a “dumb” device, 50% for Gas Monitors a smart device and 100% for a smart device on now available with HART Protocol. a smart interface. Separate metrics for transmit• More efficient ters and valves would be appropriate, as would asset management a metric for smart motor controls. This metric • More flexibility with digital may not apply to new construction that is built or analog capability 100% smart, but for existing plants, this metric helps measure and promote progress toward a • More compatibility with existing installed operations fully intelligent control system. Control loop performance: The past Ask about our new 10-year warranty two decades’ preoccupation with multivarion DuraSource™ Technology for able predictive control (MPC) has left most Ultima XIR and XI Gas Monitors. people, from engineers to managers, believFor your gas detection solutions, ing that control system health is embodied contact MSA at 1.800.MSA.INST. first in MPC “service factors” and second in anecdotal (and often disingenuous) trends of VISIT US ONLINE vagabond variables gone straight. The theme .COM of this article makes clear that there are many Visit us at ISA more fundamental and relevant concerns a manager or engineer should have. Booth #2350 That said, performance of higher-level controls is a real concern. For those who demand an additional gauge, (beyond “values in automatic”), a viable next-step metric is the percentage of controllers in cascade mode. This captures regulatory control utilization | G A S M O N I T O R S | S C B A | M U LT I G A S D E T E C T O R S | (cascade, ratio, etc.), advanced regulatory | HEAD/EYE/FACE PROTECTION | controls (or ARC, including overrides and 1.800.MSA.INST | www.MSANET.com/hydrocarbon.html “custom” or “complex” loops) and, of course, MPC. The success of MPC itself, a topic of special concern for many, is well reflected in

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PROCESS CONTROL AND INFORMATION SYSTEMS

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Agile supply chain planning Providing a common workspace improves data integration C. THOMAS and D. TONG, Chevron Corp., Houston, Texas; and D. JASPER and C. ACUFF, M3 Technology, Houston, Texas

istorically, refinery planning begins with setting constraints and targets, and optimizing an objective function in a linear programming (LP) model. The resulting optimized plan is made for short- and long-term timeframes (e.g., 30, 60, or 90 days). However, as we look beyond the refinery and include more of the supply chain, additional information is needed for building a better optimized plan. More perspectives are needed so each functional area can quickly interpret imbalances and participate in the optimization. At the same time, a good plan depends on the timing (i.e., how quickly does the plan come together before market changes decrease its value?). Building the best plan is not only an iterative process, but also time consuming. If you are like most companies, plan creation is becoming more sophisticated with more data requirements. Likewise, to create a good plan requires more time while the deadlines to publish are more aggressive each year.

• Decreasing the planning cycle (from monthly to weekly publishing) • Providing a common workspace for accomplishing the above tasks for multiple users. This will be referred to as the “planning workspace.”

■ The ability to assemble

and coordinate different types of data is a major

must not only manage planning in their own area, but also optimize according to changes made upstream or downstream. Providing functional perspectives that interact with each other makes rapid collaborative planning possible. A planning workspace provides multiple perspectives while sharing the same supply chain information (Fig. 1). Changes made in one area are automatically seen in the other areas according to the impact. Timely data feeds for many functional planners. This is the most com-

component of enabling agility. Multiple perspectives are needed.

Plan optimization must be moved from individual efforts to a joint collaborative process. Functional areas such as refinery and logistics planning, supply, sales planning and trading functions impact each other when the plan changes. Functions

mon constraint to speeding up a dataintensive planning process. Supply chain planning requires a robust quantity of data from multiple systems. They must be assembled and organized quickly for the functional planners. These data include: • Refinery scheduling static data (e.g., tankage/cavern capacities, pipeline/berth pumping capacity, pipeline linefills and process unit production capacity constraints)

What is agile supply chain planning? Agility is the ability to move

quickly, methodically and with ease to meet today’s supply chain challenges. Specifically it includes: • Responding to new business knowledge quickly (faster data integration and analysis tools) • Multiple perspectives based on the user’s role (working with the plan according to the functional area experts such as a refinery planner, an area logistics planner— terminal or distribution area, trader and sales manager • Seeing and validating the data quickly (user-friendly visual tools) • Performing visual analysis, “what-ifs” and case comparisons • Taking the results and synthesizing a new improved starting baseline for the LP optimization

Reﬁnery planner perspective t .BYJNJ[F -1 NBSHJOBM WBMVFT t 1MBOU NBJOUFOBODF QFSJPET t 1SPEVDUJPO BOE DPOTVNQUJPO t #MFOE TQFDJmDBUJPOT

Supply planning perspective t $SVEF CMFOE TQFDJmDBUJPOT t $SVEF GFFETUPDLT EFNBOE t 'PSFDBTUFE JOWFOUPSJFT t 'PSFDBTUFE SFDFJQUT TIJQNFOUT

Reﬁnery planning function

Supply planning function

Logistics management function

Planning workspace (shared perspectives)

Sales planning function

Trading function

Trade planning perspective t 1VSDIBTFT BOE TBMFT iCVZ PQUJPOTw t &YDIBOHFT CZ MPDBUJPOT BOE NBUFSJBM QPPM t 'PSFDBTUFE QSJDJOH t 5SBEJOH QBSUOFS PQQPSUVOJUJFT

FIG. 1

Logistics planning perspective t 5FSNJOBM BOE TUPSBHF BSFBT t *OWFOUPSZ JNCBMBODFT t 4VQQMZ EFNBOE JNCBMBODFT t %JTUSJCVUJPO QMBOT

Sales marketing perspective t %FNBOE GPSFDBTU t 1SJDJOH GPSFDBTU t 'PSFDBTUFE NBUFSJBMT JOWFOUPSJFT t 'PSFDBTUFE QPPM JOWFOUPSJFT HSPVQFE CZ MPDBUJPOT

Supply chain planning perspectives wheel.

HYDROCARBON PROCESSING OCTOBER 2009

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PROCESS CONTROL AND INFORMATION SYSTEMS

Linear program opt.

Receipt shipment nominations

Synthesis and optimized

Reﬁnery scheduling

Price forecast

Trading and exchange

Distribution Terminal costs inventories

Demand forecast

Web services

Integration depot

Database

LP planner

FIG. 2

Supply coordinator

Sales and marketing

Trading coordinator

Distribution planner

Area manager

Supply chain plan multiple data feeds.

• Refinery schedStep 1 Step 2 Step 3 uling dynamic data (e.g., scheduling Gather data Optimize using Review and improve forecasted run rates, from the enterprise the LP the supply chain plan forecasted production/consumption/ inventories and operFIG. 3 Summarized workflow process. ating setpoints) • Maintenance systems for forecasted asset/equipment downtimes for a supply chain optimization tool. It • Nominations from the enterprise must be user friendly, fast and feature rich. resource planning system or other ship- The user’s productivity with the tool will ment/receipt source systems ultimately determine whether the tool is • Trading opportunities successful or not. • Exchange management Planners, traders, distribution area • Current and forecasted material prices supervisors, etc., need to work and see their • Distribution constraints/costs information in many different groupings. • Export of synthesized supply chain For example, the traditional materials and plan data for starting a new or revised LP material pools (i.e., crudes and crude pools) plan including constraints, demands and as well as geographic groups such as supprices to the LP planning tool ply areas containing refineries, terminals, • Import from the LP planning tool the pipelines, etc. Time scales are also needed initial, revised or final LP planning solution to group results by monthly, weekly and tool. This contains the refinery production daily averages, or by a custom duration plan, trading plan and the supply plan for (e.g., 13 periods of 28 days each for finanthe terminals. cial business model comparison purposes). The ability to assemble and coordinate The planning workspace information different types of data is a major compo- sources also have their components such as nent of enabling agility. Fig. 2 shows the inventory forecast, price forecast, demand enterprise data being assembled by an inte- forecast, trading plan, exchange plan and gration depot for supporting the multiple forecasted stock transfers, etc. perspectives needed. Be more productive has been the management direction for some time, so A common portal into the plan- the portal must have robust features allowning information. A common portal ing rapid changes, analysis, reporting and is needed to work in. This enables person- building scenarios. For planning, making nel to validate, analyze and perform what- changes in the external source systems and ifs, and view the information in one place reimporting data to the workspace to per(even though it originated from many form analysis can be too time consuming sources). This is a mission-critical feature and doesn’t always make sense. A planSelect 154 at www.HydrocarbonProcessing.com/RS

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PROCESS CONTROL AND INFORMATION SYSTEMS

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Step 1

Step 2

Assemble and load supply chain data (automated)

Synthesis new LP case created (automated)

Supply chain data t /PNJOBUJPOT t 1SJDF GPSFDBTU t 3FmOFSZ TDIFEVMJOH t 5SBEJOH BOE FYDIBOHF t %JTUSJCVUJPO DPTUT t 5FSNJOBM JOWFOUPSJFT t %FNBOE GPSFDBTU

Develop improve LP case using optimizer Perform LP case analysis Assemble for supply chain analysis (automated)

FIG. 4

Detailed workflow process.

FIG. 5

Typical planning workspace screen.

Step 3 Review supply chain pain points

Perform what-if analysis

Edit and improve plans t 3FmOFSZ QSPEVDUJPO t %FNBOE GPSFDBTU t 1SJDF GPSFDBTU t 4DIFEVMFE JOWFOUPSZ t 4DIFEVMFE NPWFNFOUT t &YDIBOHF t 5SBEF t 4UPDL USBOTGFS t *OWFOUPSZ DPOTUSBJOUT Publish the plan (automated)

Make plan changes

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ning workspace allows users to change data immediately without reimporting while also providing the ability to save plans (or scenarios) of their work. Plan comparison allows multiple what-ifs to be analyzed side by side. Planning is an iterative process, so let’s not recreate the wheel. Historically the starting point is the LP planning system. The case or cases should not only be easily imported, but the planner’s improvements from the planning workspace should be synthesized into a new baseline case for the LP to start with when necessary. Planning workspace allows the user to export the

synthesized data directly back into the LP so it can be reoptimized. This may include revised constraints, targets, prices, refinery production targets, transfer costs or other changes for the refinery. Supply chain planning is a collaborative process, i.e., it’s not just for planners anymore. The tool should be multiuser enabled with change management to coordinate the planning efforts of many. Planners, traders and area supervisors can check-out and check-in a plan (or scenario) for detailed work that enables a collaborative process. Plans should be locked or shared with others. Reports (e.g.,

PROCESS CONTROL AND INFORMATION SYSTEMS supply demand balance, etc.) should be bookmarked to make easy reference for others to access. Role-based access should be available for a view only, planner and administrator user types.

process. The target is to decrease the overall time duration from 30 days to one week, while still allowing multiple iterations of plan improvements before the final plan is published.

What is the workflow process? The workflow process starts with gathering data and continues with an iterative improvement cycle to optimize the supply chain plan until it is published. A summarized workflow process is shown in Fig. 3. The first step uses automation to assemble data from many enterprise systems and creates cases for optimization. Step two is the optimization via LP tools. The LP receives information such as blend specifications, demands, constraints (for distribution and raw materials), costs, exchanges, operating parameters, prices, unit capacities, etc., from either step one or step three via an automated process call “synthesis.” Automating this step is a key component needed for speeding up the review and improvement processes. After optimization by the LP tool, the results consisting of the refinery consumption and production plan, the refinery blend specifications, the trading plan and the supply plan for the terminals are passed to the review and improvement process. Step three is the multiuser and a multiperspective area with robust tools for information analysis. Pain points are highlighted to identify problems quickly for the users (e.g., a pain point would be an inventory supply and demand imbalance for a particular material pool for a specific location). Once the pain points are removed and the plan is approved by the functional users, it can be published or the plan can be synthesized into a new LP case for reoptimization. Once optimization is completed, it can automatically be fed back into the workspace for the functional users to review and continue supply chain planning in their area. Depending on supply chain planning needs, steps two and three can occur over and over in an iterative process. How long does the overall cycle take? Assembling the supply chain data for the workspace and synthesis for the LP is automated. In addition, taking the LP optimized results back into the workspace is also automated. Use the automation mentioned and the planning workspace tools for the functional users, the entire planning process can be significantly reduced. Fig. 4 shows the detailed workflow with the concurrent processes happening in a planning workspace. Technology allows the automation of several steps to speed up the

What does a typical planning workspace screen look like? Fig.

5 shows a typical functional perspective for the Cedar Park location and its material pools (pools can be used to aggregate similar grades). Graphs and inventory calculations not only show the current values but can change automatically when values are changed by the user. Therefore, it supports rapid what-ifs and results are shown quickly. User changes are simulated within the planning workspace to show impact with other locations or against supply and demand constraints (e.g., receipts, shipments and inventory constraints).

■ The supply chain

planning workspace can help businesses plan better, to perform better, across the wide spectrum of procurement, manufacturing and distribution activities. Benefits from a better plan. The supply chain planning workspace can help businesses plan better, to perform better, across the wide spectrum of procurement, manufacturing and distribution activities. A planning workspace allows this process to be performed in a timely iterative fashion with an LP tool for optimization. This makes more time available for spot opportunities analysis (via procurement or trading) to increase potential profits. Manufacturing (i.e., refining) costs are reduced by aligning the refinery production forecast with demand and trading opportunities and reducing overall feedstock costs. A planning workspace can integrate with the refinery scheduling systems so that the production forecast as well as constraints, inventory and operating parameters (i.e., process unit run rates) can be easily shared. This helps refinery management sync up with procurement and distribution planning objectives. The make-buy-sell decision process is made easier by aggregating the right

SPECIALREPORT

information and enabling what-ifs to be modeled. Purchases, sales and production options can be better planned and aligned with the supply chain economics. Distribution costs associated with the various manufacturing, procurement or sales scenarios can be more closely evaluated. Specific changes throughout the supply chain can be included in the scenarios and their impact seen in the planning workspace. Alternatively, the plan can be reoptimized using the LP optimizer and fed back into the planning workspace for additional review. HP

Chad Thomas has been working with Chevron for the previous 11 years, holding various roles in the chemicals and downstream organizations. Currently his work includes improving, developing and deploying systems and processes used by Chevron’s downstream organizations for supply chain planning. Previous roles include refinery planning and optimization, refinery scheduling, and process design and engineering. Mr. Thomas graduated from Louisiana State University with a BS degree in chemical engineering.

David Tong has 17 years of experience in the IT field working in the energy industry. He has had various IT operations, planning and design roles throughout his career. Mr. Tong works at Chevron and is involved with designing, developing and deploying various IT solutions in network capacity planning and modeling, knowledge management, webcasting technology and downstream fuels supply chain planning. He has a BS degree in telecommunications/ network engineering from Texas A&M University.

David Jasper is the executive vice president of development at M3 Technology. He has over 30 years of software development experience in the oil, gas and chemical sector with a focus on enterprise architecture. Prior to M3 Technology, Mr. Jasper worked at Aspentech, Bonner & Moore, Exxon, Aramco Services and Shell Oil. He has a BS degree in computer science from the University of Idaho.

Craig Acuff is the business development director at M3 Technology. He has 22 years of experience in the refining and process industries. Mr. Acuff has been involved with implementing refinery systems for achieving business process improvements. He has worked for Texas Instruments, Grace Petroleum, Aspentech and Valero. He has a BS degree in mathematics from Oklahoma State University and a masters specializing degree in computer science from the University of Central Oklahoma. HYDROCARBON PROCESSING OCTOBER 2009

I 39

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PROCESS CONTROL AND INFORMATION SYSTEMS

SPECIALREPORT

Service-oriented architecture simplifies data source integration Here’s how the approach helps refinery scheduling and also contributes to business-wide SOA adoption K. SAMDANI, Infosys Consulting, Bangalore, India

he refinery scheduling system needs to interface with various heterogeneous data sources. Although the traditional pointto-point integration approach serves the needs, it presents several problems due to a variety of underlying platform technologies. Also, such a solution is not scalable. Applying the SOA approach helps automate the refinery scheduling process as well as contributes to building a business-wide services repository. This article compares the traditional integration approach for the refinery scheduling process with the SOA-based approach and presents a conceptual architecture along with the benefits thereof. It also aims at bringing out how the SOA approach for individual projects such as refinery scheduling contributes to overall strategic SOA adoption by the refining business.

Introduction. The modern integration approach suggests SOA for the whole organization for strategic business transformation. The Forrester survey1 indicates that broadly 70% of large businesses and nearly half of small to medium-sized businesses are into SOA and, more importantly, are by and large satisfied. Adopting the SOA approach for refinery scheduling helps in two ways: • The SOA approach develops a reusable scheduling services catalog that becomes part of an overall business-wide services repository. • The SOA approach provides open standards-based integration of the scheduling tool with a variety of data sources. The refinery scheduling process can be viewed as a set of reusable services. A service (for example, getting tank inventories) that is necessary for refinery scheduling can be used by other business processes such as yield accounting, plan vs actuals analysis, etc. Also, it can be used across other refineries. SOA also provides integration standards for interfacing with the various heterogeneous data sources such as historians, laboratory information management systems (LIMS), spreadsheets, etc. It eliminates platform dependence by using open standards-based service interface specifications. It enables organizing the scheduling process as various reusable services, thus contributing to developing a business-wide services repository.

mathematical engines to develop an end-to-end refinery schedule. These tools need data such as tank inventories, qualities, crude arrival and product dispatches, prices, etc. from heterogeneous data sources. Fig. 1 depicts the traditional approach of point-to-point interfaces between the scheduling tool and data sources. Although it serves the data needs of the scheduling tool, several problems associated with the traditional approach are: • Data source systems vary widely in underlying platform, integration capabilities, sophistication, etc. Some sources are spreadsheets/text files whereas others may expose Web services for retrieving data. • In case the legacy systems (providing data to the scheduling tool) are replaced by modern systems, the interfaces with such systems are required to be replaced. • Developing an interface for a new data source is almost a fresh effort. Reusability is minimal. • These interfaces are tightly coupled with source systems. Hence, changes in underlying platform technologies of the source systems mean a lot of rework. • In interconnected supply chains, data sources may be outside the organization. Supply chain partners have their own system

.txt/.csv ﬁles Spreadsheets

Historian/ RTDBMS

Component stream ﬂow/quality

Tank volume and service

Planned crude/ products receipt and dispatches

Reﬁnery scheduling system

Tank qualities LIMS

Prices

RDBMSs

Reﬁnery schedule Supply information Third party systems

Pricelist spreadsheet

Traditional approach to refinery scheduling. Refinery

scheduling is largely driven by a scheduling tool. The tool generates schedules by processing data from a variety of sources. Unlike in the past, current refinery scheduling tools employ advanced

FIG. 1

Traditional integration approach.

HYDROCARBON PROCESSING OCTOBER 2009

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SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS

upgrade/technology strategy roadmap. Any change to the underlying technology by them impacts the interface. • For multirefinery organizations, the development and global deployment costs are high since there is little reusability. • Maintenance/upgrade costs are high. • Typically, these are not in line with the overall enterprisewide IT strategy and hence, carry a lot of risks. Point-to-point interfaces are typically implemented quickly. They serve tactical short-term objectives. Having selected a scheduling tool, the scheduler expects to start using it as soon as possible for obvious benefits of generating optimal and/or feasible production schedules without considering integration-related issues.

Initiate scheduling

FIG. 2

Collect data

Audit data

Substep

Reﬁnery scheduling

Initiate scheduling

Collect data

Audit data

Generate schedule

Publish schedule

Construct Construct Connect Retrieve Unit of response request to source and Calculate measure message message system validate

Identify Look-up Receive Execute source and conversion data inputs conversion target UoMs table

Activity

FIG. 3

Publish schedule

High-level view of the scheduling business process.

Business process

Step

Generate schedule

Representative organization of refinery scheduling services.

Unit of measure service Validated data (from historian)

Receive data inputs

Identify source and target UoMs

Look-up conversion table

Execute conversion

Unit of measure service Validated data (from LIMS)

FIG. 4

Receive data inputs

Identify source and target UoMs

Look-up conversion table

Execute conversion

Reusability of unit of measure service (substep level).

I OCTOBER 2009 HYDROCARBON PROCESSING

However, businesses are taking a holistic view of integration needs of multiple applications across the organization and developing integration standards. They expect a robust integration solution that considers reusability, is globally deployable across multiple refineries and reduces total cost-of-ownership. SOA-based approach for scheduling. The principle

objective of the SOA approach is to eliminate platform dependence and organize the business as a set of reusable services. In the refinery scheduling process, the primary service is to generate the refinery schedule. Generating the refinery schedule is achieved by performing several services. SOA decouples these services from source or target systems. Thus, it enables using a service as and when needed by any other service or application. As shown in Fig. 2, the refinery scheduling business process can be viewed as made of multiple steps such as initiate scheduling, collect data, audit data, generate schedule and publish schedule. Fig. 3 presents an example of how the refinery scheduling business process can be organized as various services. These services are classified into coarse-grained (step) and fine-grained services (substep and activity). The refinery scheduling business process is divided into five key steps. The initiate scheduling step triggers the collect data step to get data from various sources such as historians, LIMS, spreadsheets, etc. The audit data step audits incoming data and shares it with generate schedule, that runs the scheduling engine and produces an optimal schedule. The publish schedule step publishes it. Each step has been further divided into substeps. For example, collect data uses various substeps: initiate, construct request message, connect to source system through to construct response message. Each substep can also be logically divided into activities. Fig. 3 provides an example of how the unit of measure conversion substep uses four activities. The SOA approach expects such detailed organization of coarse-grained as well as fine-grained services. This enables developing a services repository. It is essential to consider how services can be independent of source and target applications while developing the services repository, thereby increasing reusability of services. SOA enhances reusability of services. Systematic orga-

nization of services as depicted in Fig. 3 helps identify reusability and removes redundancies. Once organized, such a unique set of services representing the scheduling business process can be called by any application/business function within refinery operations as well as the broader Tank enterprise. To maximize reusability, services inventories (desired need to be developed in a manner such that UoM) multiple applications/other services can use them. Creating application-independent Construct services works very well for reusability. response Reusability of services can be demonstrated at multiple levels, i.e., at the substep or Tank step levels or across the business processes. qualities (desired Reusability at the substep level. Unit UoM) of measure is a substep within the collect data step. Fig. 4 demonstrates how the unit of measure service is used for converting input data units of measure from different data sources.

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PROCESS CONTROL AND INFORMATION SYSTEMS

Initiate tank inventory User inputs (via UI)

Collect data service (for tank inventories) Construct request message (use data aliases) Y

Validate inputs

Process Retrieve data (UoM and validate conversion, data calc.)

Connect to source system (historian) Y Connect established?

Construct response and upload

Y All data available?

Successful upload

N Audit data

Generate error message N

Validate inputs

Connect established? Y

User inputs (via UI)

Construct request message (use data aliases)

Initiate tank quality

FIG. 5

Connect to source system (LIMS)

Successful upload

Publish schedule

Y Process Retrieve data (UoM and validate conversion, data calc.)

Construct response and upload

Collect data service (for tank qualities)

Reusability of collect data service (step level).

The unit of measure service receives validated data from various sources (historian, LIMS, etc.). It is expected to convert the unit of measure as required by the scheduling system. It embeds four activities to convert units of source data to the desired units of measure. It does these activities in the same manner whether it receives tank inventories from the historian or tank qualities from the LIMS. Likewise, it can be used by any other service for converting units of measure of other data such as prices, crude arrival schedule, etc. Reusability at the step level. Collect data is one of the five steps of the scheduling business process. It receives its trigger from the initiate scheduling step to get data from various sources and provide it to the subsequent stepâ&#x20AC;&#x201D;audit data. Fig. 5 demonstrates how the collect data service is triggered by two initiate service triggers (initiate tank inventory and initiate tank quality triggers) to get data from the historian and LIMS. As shown in Fig. 5, the collect data service employs several substeps such as construct request message, connect to source system through to construct response message. Irrespective of the trigger (whether initiate tank inventory or initiate tank quality), the collect data service employs the construct request message service to receive inputs and build the request message. In a tank inventory trigger, the input is typically a date. For the tank quality trigger, the input is a date range. The collect data service abstracts such differences and enables reusability for any such trigger. Reusability across business processes. Fig. 6 demonstrates how the collect data service can be used for the refinery scheduling business process as well as the yield accounting business process. Both of these business processes expect tank inventories. The collect data service employs substeps to get tank inventories. There44

All data available? Y

Generate schedule

I OCTOBER 2009 HYDROCARBON PROCESSING

Scheduling business process Initiate scheduling

Collect data

Audit data

Generate schedule

Publish schedule

Initiate accounting

Collect data

Audit data

Reconcile data

Publish account

Yield accounting business process

FIG. 6

Reusability of collect data service (business process level).

fore, it is available to be invoked independent of the business process (whether the scheduling process or yield accounting). SOA-based scheduling tool integration. Refinery scheduling system integration with data sources is quite challenging since it presents a variety of platforms such as RDBMSs, historians, legacy systems, third-party systems (outside of the intranet), spreadsheets/files hosted on a local server, etc. SOA aims at eliminating platform dependence using open standards-based service interface specifications [such as Web services description language (WSDL2)] and messaging protocols (such as SOAP, REST, etc.). Fig. 7 presents the traditional approach as well as the conceptual SOA-based approach for integration solution architecture for refinery scheduling. As depicted in Fig. 7 and described in Fig. 1 earlier, the traditional approach provides point-to-point interfaces between data sources and the scheduling system. The conceptual SOA-based

PROCESS CONTROL AND INFORMATION SYSTEMS

Presentation

Scheduler

Services Historians/ RTDBMS

LIMS

Pricelist spreadsheet

Customers

Partner portal

Admin. UI

Messaging middleware and business process execution engine Business services t 4DIFEVMF HFOFSBUJPO t 5BOL JOWFOUPSJFT t $SVEF TDIFEVMFT

.txt/.csv ďŹ les

RTDBMSs )JTUPSJBOT

Traditional integration approach FIG. 7

Suppliers

Information services t %BUB USBOTGPSNBUJPO t %BUB BMJBTJOH t 1SPDFTT DBMDVMBUJPOT

Information services t 4PVSDF DPOOFDUJPO t 3FRVFTU SFTQPOTF t &SSPS NFTTBHFT

Third party systems Data

RDBMS

Administrator

Custom UI

Scheduling tool UI

Integration

Scheduling system

Control engineer

SPECIALREPORT

RDBMSs -*.4 FUD

Files "SSJWBM EJTQBUDI TDIFEVMFT FUD

Third-party systems

Conceptual SOA-based integration approach

Traditional and SOA-based integration approaches for refinery scheduling.

architecture presents four layers: presentation, integration, services and data. Key characteristics of each layer are: Presentation layer: This layer provides data visualization capability for all concerned stakeholders. The presentation tier aims at providing a common user interface for all users. It delivers contextually relevant information to different users such as schedulers, control engineers, managers, partners, etc. It enables centralized authentication, authorization and context information sharing with integrated applications. Integration layer: This layer manages scheduling process execution and integrates with various data sources. It provides features such as process management, servicesâ&#x20AC;&#x2122; orchestration, routing, security, logging and auditing. It can also help in enterprise-level integration needs. Several advanced technologies are available to enable the integration with a variety of data sources. Services layer: The principal component of the SOA-enabled solution is the services layer. This layer decouples business logic from the presentation and data layers. It hosts services. These services are aimed at delivering business functionality, data management functionality and technical integration capability. SOA helps organize these services in a systematic manner (Table 1). The service categories are logically derived based on overall servicesâ&#x20AC;&#x2122; organization. Data layer: This layer hosts data source systems. For example, RTDBMSs (historians) that provide tank inventories whereas typically the LIMS, which is an RDBMS-based system, provides quality data. Some of these systems provide direct database access while others expose Web services. Mapping a database to provide relationships among data from disparate sources is held within the data layer. The SOA-enabled solution simplifies and automates executing the scheduling process and streamlines it in following steps: â&#x20AC;˘ The scheduler triggers one or more services using the presentation layer. â&#x20AC;˘ The integration layer responds by calling necessary services

TABLE 1. High-level services categorization Service category

Service description

Business services

These services help achieve the business objective of generating a schedule. An example business service is collect data which retrieves inventory data from the historian.

Information services

These services provide specific data management capabilities. For example, unit of measure.

Technical services

These services provide technical features and capabilities. For example: generate error messages, connect to source system, etc.

and orchestrates the process execution. â&#x20AC;˘ Each service executes the desired functionality based on available inputs and provides outputs. Technology services enable connection services to data sources and retrieve data. Information services provide data management capabilities. Business services transform the data into desired output. â&#x20AC;˘ The scheduling engine processes uploaded data and generates the refinery schedule. To achieve the automation and simplified execution of the refinery scheduling process, the key enablers for SOA-based implementation are: â&#x20AC;˘ Defining services â&#x20AC;˘ Creating services repository â&#x20AC;˘ Enabling all (data sources and destination) systems to participate in SOA approach. These enablers lay the foundation for adopting SOA within the scheduling business process and overall refining business. Benefits of SOA-based integration. Adopting SOA for

the scheduling process is substantially beneficial over traditional approaches. SOA eliminates platform dependence by using open standards-based interface specifications. It enables connecting HYDROCARBON PROCESSING OCTOBER 2009

I 45

SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS

■ SOA adoption helps not only end-

to-end automation for the scheduling business process but also opens up opportunities for substantial benefits at the enterprise-level. to any data source system. It enables identifying and organizing the scheduling process as a set of reusable services. Each service can be monitored easily. It enables better control over the entire scheduling process leading to continuous improvements. Some of the benefits are: Greater reusability: The SOA approach develops a catalog of reusable services. For example: Connect to source system can be used by the collect data service to get tank inventories from a historian as well as to get tank qualities from a LIMS. Enterprise-wide applicability: SOA decouples business logic from the presentation and data layers. A service can be used by other services within refinery operations. Such reusability can be at various levels such as substep or step, or even at the business process level. For example: The collect data service can be used by refinery scheduling as well as yield accounting business process to get tank inventories. Reduced time-to-market: Reusability is just not within a refinery. It can be across refineries. For example, the tank quality service can be reused if all refineries have the same LIMS system. This favorably impacts the large-scale global roll-out programs.

Facilitates loose coupling: Due to loose coupling between applications and services, any changes in one application are isolated and do not impact other applications’ functionalities. There are no point-to-point connections. Reduces platform dependence: The SOA approach eliminates platform dependence. Virtually any data source application can be plugged into the architecture. Enhanced collaboration: SOA enables tremendous collaboration not only within the refinery but also with supply chain partners such as traders, third-party crude storage organizations, customers, etc. They get a view of necessary information over the Web-based portal enabled by SOA. Valero published a half-million-dollar savings in demurrage costs due to an enterprise-service-enabled application that improved visibility across business functions.3 Thus, SOA adoption helps not only end-to-end automation for the scheduling business process but also opens up opportunities for substantial benefits at the enterprise-level. HP 1 2 3

LITERATURE CITED Heffner, R., Vice President and Analyst for Forrester Research with ebizQ on “Current state of SOA adoption” in Oct. 2008. http://www.w3.org/TR/wsdl20/. Article in CIOInsight, July 2007.

Kailash Samdani works with Infosys Consulting. He has 16 years of experience in consulting and business development of IT-enabled advanced solutions to the hydrocarbon, chemicals and metal industries. He holds a B.E. (Hons) degree in chemical engineering from Birla Institute of Technology & Science, Pilani (India).

Dr A H Younger Chair in Hydrocarbon Processing The Schulich School of Engineering at the University of Calgary is pleased to invite applications for the newly established Dr A H Younger Chair in Hydrocarbon Processing in the Department of Chemical and Petroleum Engineering. This Chair was created by friends and colleagues in the memory of Dr. Andrew (Andy) H. Younger, who made significant contributions to innovation and teaching in the natural gas processing sector. The primary focus of the Chair will be to provide teaching and learning in the design and development related to hydrocarbon processing at the undergraduate and graduate levels as well as providing for professional development opportunities for industry. The broad goals will be to meet the growing need for process engineers through education, teaching and research, and to create and develop new and innovative designs for hydrocarbon processing. The Chairholder will be an academic or industry leader with an international reputation commensurate with a tenure-track appointment at the preferred rank of Full Professor and will possess strong teaching, supervisory, leadership and research skills. It is expected that the candidate will have extensive industry experience and/or industry interactions. The candidate will be expected to develop an appropriate research program with a scope which is aligned with the Chair objectives and be eligible for registration as a Professional Engineer in the Province of Alberta. The Schulich School of Engineering currently supports over 2,800 full-time undergraduate students, 1,000 graduate students, and more than 150 faculty members. We continue to earn national and international recognition as an academic leader in education, research and scholarship and offer some of the most innovative joint degree programs and specializations in the country. The Department of Chemical and Petroleum Engineering offers BSc degrees in Chemical Engineering and Oil & Gas Engineering, and postgraduate master's and doctoral degrees with specializations in Chemical, Petroleum, Energy & Environmental, and Biomedical Engineering. More detailed information is available at: www.schulich.ucalgary.ca/chemical. The University of Calgary (www.ucalgary.ca) is a comprehensive research university with close to 30,000 full-time equivalent students, including over 5,000 graduate students, and is an institution where leading innovation in energy and environment is an identified priority. The City of Calgary is located in the southern part of Alberta, one of the most dynamic and prosperous provinces in Canada, and is situated within an hour’s drive of Banff National Park and the Kananaskis wilderness areas, some of the most beautiful areas of the Canadian Rocky Mountains. Interested individuals are encouraged to view the full posting (Job #7844) at www.ucalgary.ca/hr/careers/careers_search. Applications will be considered as they are received, and will continue until the position is filled.

All qualified candidates are encouraged to apply; however, Canadian citizens and permanent residents of Canada will be given priority. The University of Calgary respects, appreciates and encourages diversity. 46

I OCTOBER 2009 HYDROCARBON PROCESSING

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PROCESS CONTROL AND INFORMATION SYSTEMS

SPECIALREPORT

Predicting octane numbers for gasoline blends using artificial neural networks The ANN models were more accurate than regression models E. PARANGHOOSHI and M. T. SADEGHI, Iran University of Science and Technology, Narmak, Tehran, Iran; and S. SHAFIEI, Sahand University of Technology, Tabriz, Iran

good model for gasoline blending is beneficial for operation and prediction of gasoline qualities. Since blending does not follow the ideal mixing rule in practice, artificial neural network (ANN) models have been developed to determine the research octane number (RON) of the gasoline blend produced in the Tabriz refinery. The developed ANN models use as input variables the volumetric amount of the six most commonly used fractions in gasoline production multiplied by their octane number. Then the optimum model was compared with a multiple regression model available in literature. Results show that the ANN model simulated gasoline blending better than the regression model as judged by the higher R 2 value (0.9812 vs. 0.9495), lower MSE value (0.0094 vs. 0.0294) and lower AARE value (0.910 vs. 0.1799). Introduction. Calculations of blend recipes are mostly empiri-

cal and heavily depend on production experience. Several blending models have been presented in the literature for calculating the blend octane rating. The first model in the literature is the ideal model, where the gasoline components blend in a linear model according to their volume fractions.1 The well-known in industry Ethyl RT-70 method, models the blending nonlinearity through the fuel sensitivity (RON-MON) and the olefins and the aromatics content of the component.2,3 An approach quite similar to the ethyl method was presented by Stewart. In this model, the nonlinearity was expressed through the olefins content of gasoline components.4 However, the blend octane numbers (BONs) were employed instead of the normal MONs and RONs, thus accounting for the model nonlinearity. The BONs are determined by the regression analysis, still based on user experience.5 A similar method that transforms the nonlinear blending octane numbers to linear blending quantities was presented by Rusin et al.6,7,8 Nonlinear models utilizing second-order terms have also been presented where the blend effect (nonlinearity) in the mixture octane rating is expressed by the set of interaction coefficients between components.7,8 In a similar approach, Zahed et al. presented a polynomial model predicting the blend RON in five components’ mixtures.7,8,9 Lately, an ANN model was used by Pasadakis and Murty for predicting octane rating of gasoline blends by employing the volume fractions of streams used for gasoline blending.8,9

Because each refinery’s feed and operating conditions are different, calculations for predicting gasoline blends octane number are unique for each refinery. In this work a new ANN-based prediction model for calculating the RON value of gasoline blends in the Tabriz refinery is presented. The model utilized as input variables the volumetric concentration of six streams weighted by their octane number used for gasoline production in the Tabriz refinery. The employed fractions were platformate, heavy and light naphtha from the hydrocracking unit, butane, additives (MTBE) and pyrolysis gasoline (PG). Experimental samples. The gasoline component samples and the blending recipes were collected from storage tanks in the Tabriz refinery. One hundred eighty-four sample sets were collected in this way during a period of 10 months, such that any variations in the stream compositions due to the different crude oil feedstocks or disturbances of the operating parameters could be accounted for. RON values were determined in a refinery laboratory according to ASTM D-2699 procedure. Data analysis. After collecting the data, outliers must be detected. Outlier detection is the most important task in data analysis. Outliers describe abnormal data behavior, i.e., data that deviate from natural data variability. Many methods have been proposed for univariate outlier detection. They are based on (robust) estimation of location and scatter, or on quintiles of the data. Moreover, by definition of most common rules, (mean ±2 standard deviation) outliers are identified.10 After outliers were eliminated, 173 sample sets were fed to the ANN. Artificial neural networks. ANNs are interconnected par-

allel systems consisting of simple processing elements: neurons.19 In this study, a feed-forward, multilayer perceptron (MLP)-type ANN was considered for predicting octane rating of gasoline blends (Fig. 1). MLP is the most widely used neural network architecture and consists of an input layer, one or more hidden layers and an output layer. Two-layer (ignoring the input nodes) feed-forward ANNs are common models in the literature.20 The neurons in the input layer receive input quantities and pass them on to the hidden-layer neurons without any computation. The hidden-layer neurons calculate their inputs by adding the weighted inputs received from each neuron in the previous HYDROCARBON PROCESSING OCTOBER 2009

I 49

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KTI Corporation 1990 Post Oak Blvd., Suite 1000, Houston, TX 77056 Tel: (281) 249-2400 Fax: (281) 249-2328 E-mail: sales@kticorp.com KTI - KOREA #612, Kolon Science Valley II, 811, Guro-dong, Guro-gu, Seoul, 152-050, Korea Tel: 82-2-850-7800 Fax: 82-2-850-7828 E-mail: BSKim@kti-korea.com Select 96 at www.HydrocarbonProcessing.com/RS

PROCESS CONTROL AND INFORMATION SYSTEMS Output layer

Hidden layer V11

V22

W11

W22

V1n

0.0016 y1

W1p Vpn

Wmp

tansig-tansig logsig-logsig logsig-purelin tansig-purelin

0.0014 Mean square error, MSE

Input layer

0.0012 0.0010 0.0008 0.0006 0.0004 0.0002 0.0000 0

FIG. 1

10 15 Number of neurons

Feed-forward, multilayer perceptron-type artificial neural networks. FIG. 2

layer. The connections between the neurons are called weights. Weights determine the input signal strength. The outputs of the hidden neurons are calculated by passing the sum of the weighted inputs received on through a nonlinear transfer or activation function. The output neurons perform the same operations as those of hidden neurons. The hyperbolic tangent and sigmoid transfer functions have been tested in hidden-layer and purelin networks. These transfer functions are:9

f (x ) = log sig = f (x ) = tan sig

1 1+ exp( x)

e x e x x

e +e f (x ) = purelin = x

Sigmoid

(1)

Hyperbolic tangent

(2)

Linear

(3)

The back-propagation algorithm, the most commonly used supervised learning algorithm in MLP, was utilized in this study. In the training procedure, the information is processed in the forward direction from the input layer to the hidden layer and then to the output layer (feed-forward) to obtain the network output. The desired output at each output neuron is compared with the network output and the difference or error is computed. The error function has the following form: E= 1

SPECIALREPORT

( y ANN yiexp )2 n i

(4)

i=1

The error is propagated in the backward direction from the output layer to the input layer (back-propagation) and it is minimized by adjusting the connection weights. Modeling. After the outliers were eliminated, 173 sample sets

remained. Specifications of data used in this study are presented in Table 1. A problem in the use of ANNs is over-training. This means that the model may describe with great accuracy the set of training data, whereas some large prediction errors may be observed when the model is tested in a new data set. To overcome this problem, the database was split into three subsets: training, crossvalidation and testing, each containing 103 (60%), 35 (20%), and 35 (20%) sets, respectively.19 To achieve fast convergence to minimal MSE, the input and output data were normalized within the range of ([–1 1]). As a result of normalization, all variables

Effect of the number of neurons/transfer functions on ANN model performance during the training phase.

TABLE 1. Gasoline components employed for the blend Platformate

HIN

Inputs* LIN

MTBE

Outputs C4 RON

Mean

39.021

16.349

3.790

2.952

4.851

1.414

86.6

Standard deviation

2.146

3.189

1.261

1.805

2.140

1.063

0.66

Minimum

33.889

18.419

1.165

85.0

Maximum

46.625

38.000

6.987

7.597

9.253

3.67

88.3

* Platformate: product of platforming unit HIN: heavy naphtha from hydrocracking unit LIN: light naphtha from hydrocracking unit PG: pyrolysis gasoline from Tabriz Petrochemical C4: butane

acquire the same order of magnitude (importance) during the learning process.9 Care has been taken to have representative data in the training set since an ANN performs better when predicting the output parameter within the training data limits. The different neural network architectures were tested to obtain the best performance. The network parameters to be optimized were the number of hidden layers, number of neurons in each hidden layer and type of transfer function. One and two hidden-layer networks with three to 20 processing elements were tested. For each case sigmoid and hyperbolic tangent transfer functions were tried. In BP networks, the number of hidden neurons determines how well a dataset can be learned. Too many hidden neurons will tend to memorize the problem, and thus do not generalize the input/output relationship. If the number of hidden neurons used is not enough, the network will generalize the relationship well but may not have enough “power” to learn the patterns well at a satisfactory precision. Figs. 2 and 3 show the ANN model performance for a different number of neurons and transfer functions in a hidden layer for the training and cross-validation phases. Other architectures with two hidden layers and various numbers of neurons were investigated. To investigate the number of epochs on ANN model performance, MSE for the cross-validation and training phases were determined against the number of epochs. It is clear from Fig. 4 that the MSE of the training data set also showed a decreasing HYDROCARBON PROCESSING OCTOBER 2009

I 51

SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS TABLE 2. Some other regression models

0.018

Mean square error, MSE

Model

tansig-tansig logsig-logsig logsig-purelin tansig-purelin

0.016 0.014 0.012

a x

i i

(6)

0.1427

1.847E-13

2.6297

0.9545

(7)

0.1419

2.481E-10

2.6156

0.9548

(8)

0.1432

-0.0001

2.6461

0.9542

(9)

0.2043

0.0089

5.0959

0.9119

(10)

4.1328

26.6746

2220.4201

i =1 7

Y = a0 +

0.010

a x

i i

i =1

0.008

0.006

Y = exp(a 0 +

0.004

a x ) i i

i =1 7

Y = a0 +

0.000 0

10 15 Number of neurons

a x

i i

i =1

20 7

Y =

Effect of the number of neurons/transfer functions on ANN model performance during the cross-validation phase.

FIG. 3

a x

i i

i =1

*This is the standard deviation of the residuals. **Residuals sum of squares

TABLE 3. Regression coefficients and some of their statistic parameters

0.040 Cross-validation Training

0.035 Mean square error, MSE

RSS**

Y = a0 +

0.002

Value

Regression variable results Deviation standard

t-ratio

82.9224

0.3976

208.5417

0.7867

0.025

0.0477

2.0248E-02

2.3568

0.0400

0.020

-0.0512

1.6334E-02

-3.1333

0.0323

0.1225

2.6973E-02

4.5430

0.0533

0.0690

1.3402E-02

5.1548

0.0265

0.010

0.0862

1.7618E-02

4.8959

0.0348

0.005

0.1923

2.1784E-02

8.8298

0.0431

0.9398

7.7264E-02

12.1630

0.1529

Variable

0.030

0.015

0.000 0

FIG. 4

200

400 600 Epoch number

800

The effect of the number of epochs on ANN model performance during training and cross-validation.

RON = a0 + ai (X i RON i )n i=1

I OCTOBER 2009 HYDROCARBON PROCESSING

95% (+/-)

1,000

trend with increasing epoch size but validation data showed the minimum. It means that there is over-training with the increase in the number of epochs.20 The epoch with minimum MSE in cross-validation is considered in selecting the final ANN model architecture. Regression. Regression analysis gives us the ability to summarize a collection of sampled data by fitting them to a model that will accurately describe the data. The basic idea behind regression analysis is to choose a method of measuring the agreement between your data and a regression model with a particular choice of variables.11 Literature abounds with several models to predict the octane rating of motor gasoline blends. Among these models, the regression model suggested by Zahed et al. was found to be suitable for predicting the RON of blended gasoline in a refinery. The model (Eq. 5) like the ANN model, involves six independent variables as inputs. These data were volumetric fraction weighted by the corresponding RONs of the feed components.

Std. error* Residual sum

(5)

Regression coefficients, a0, a1, ..., a6, n, were determined by regression analysis. The variables, X1, X2, ..., X6, are defined as volume fractions of component blends and RON1, RON2, ..., RON6 are research octane values of corresponding feed components. Software was used to estimate the regression coefficients. The Levenberg-Marquat algorithm was used in this software. Some other investigated models like Eq. 5 are shown in Table 2. In all models in Table 2:

xi = X i RON i

(11)

It is proved from Table 2 that Eq. 7 is more suitable than other equations for predicting RON by a regression model. Table 3 shows regression coefficients and some of their statistic parameters. T-ratio in Table 3 is the ratio of the estimated parameter value to the estimated parameter standard deviation. The larger the ratio, the more significant the parameter in the regression model. a t ratio = i (12) a i

where: ai is estimated parameter value a is estimated parameter standard deviation. i 95% (+/-) is the shown 95% confidence interval. This means that there is a 95% chance that the actual value of the parameter lies within the confidence interval.15

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SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS

89.0 R2 = 0.9812

Experimental Regression ANN

88.5 88.0

RON

Simulated RON

87.5

87.0 86.5 86.0

85.5 85.0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Observation

85 85

87 Experimental RON

89 FIG. 7

Comparison of ANN and regression models with experimental data.

Experimental and ANN-predicted data for the testing set.

FIG. 5

TABLE 4. Statistic measures for comparing ANN and regression models 89 Train

ANN Validation

Test

0.9972

0.9803

0.9812

0.9546

0.9495

MSE

1.273E-5

0.0093

0.0094

0.0190

0.0294

AARE

0.0032

0.0995

0.0910

0.1072

0.1799

R2 = 0.9495

Simulated RON

85 85

FIG. 6

87 Experimental RON

Experimental and regression-predicted data for the testing set.

Results and discussions. After several neural network architectures were investigated to establish a model for predicting octane number, an optimum ANN structure was selected with one hidden layer and nine neurons. A hyperbolic tangent was used in the hidden layer and a purelin transfer function in the output layer. Trained network performance was verified with a data set that was not used in the training phase. The regression model, after estimating the coefficients, was tested with a data set used for testing the ANN. Figs. 5 and 6 show simulated results with the ANN and regression models against experimental data. Performance of these models were compared using meansquare error, MSE, correlation coefficient, R 2, and average absolute relative error, AARE. MSE = 54

( yout y pred )2 n

I OCTOBER 2009 HYDROCARBON PROCESSING

(13)

Regression Coeff. Estimation Test

R 2 = 1

( yout y pred )2 ( yout y pred )2

(14)

AARE =

( yout y pred ) 1 100 n yout

(15)

where n is the number of data points, yout and ypred are measured and predicted RON values and ypred is the average of ypred. Fig. 7 shows a comparison of the ANN and regression models with experimental data. In Table 4, the statistical measures, which describe accuracy of both fits, are given. It is evident from Table 4 and Fig. 7 that the ANN gave better results than Eq. 7 as judged by the higher values of R 2 (0.9812 vs. 0.9495), lower values of MSE (0.0094 vs. 0.0294) and lower values of AARE (0.910 vs. 0.1799). HP ACKNOWLEDGMENT Financial support of the research and development division of Tabriz refinery is greatly acknowledged. Mr. Torabi, the head of the refinery research committee and process engineer Pourhasan, are highly appreciated for their help in collecting the data.

1 2

LITERATURE CITED Gary, J. H. and G. E Handwerk, Petroleum Refining Technology and Economics, Marcer Dekker, New York, 1994. Zhang, Y., D. Monder and J. F. Forbes, “Real-time optimization under parametric uncertainty a probability constrained approach,” Journal of Process Control, Vol. 12, 373–389, 2002. Healy, W. C., C. W. Maassen and R. T. Peterson, “A new approach to blending octanes,” Proc.24th Meeting of American Petroleum Institute’s Division of Refining, New York, 1959. Stewart, W. E., “Predict octanes for gasoline blends,” Petroleum Refiner 38 (1959) 135–139.

PROCESS INSIGHT Comparing Physical Solvents for Acid Gas Removal Physical solvents such as DEPG, NMP, Methanol, and Propylene Carbonate are often used to treat sour gas. These physical solvents differ from chemical solvents such as ethanolamines and hot potassium carbonate in a number of ways. The regeneration of chemical solvents is achieved by the application of heat whereas physical solvents can often be stripped of impurities by simply reducing the pressure. Physical solvents tend to be favored over chemical solvents when the concentration of acid gases or other impurities is very high and the operating pressure is high. Unlike chemical solvents, physical solvents are non-corrosive, requiring only carbon steel construction. A physical solvent’s capacity for absorbing acid gases increases signiﬁcantly as the temperature decreases, resulting in reduced circulation rate and associated operating costs.

PC (Propylene Carbonate) The Fluor Solvent process uses JEFFSOL® PC and is by Fluor Daniel, Inc. The light hydrocarbons in natural gas and hydrogen in synthesis gas are less soluble in PC than in the other solvents. PC cannot be used for selective H2S treating because it is unstable at the high temperature required to completely strip H2S from the rich solvent. The FLUOR Solvent process is generally limited to treating feed gases containing less than 20 ppmv; however, improved stripping with medium pressure ﬂash gas in a vacuum stripper allows treatment to 4 ppmv for gases containing up to 200 ppmv H2S. The operating temperature for PC is limited to a minimum of 0°F (-18°C) and a maximum of 149°F (65°C).

Gas Solubilities in Physical Solvents All of these physical solvents are more selective for acid gas than for the main constituent of the gas. Relative solubilities of some selected gases in solvents relative to carbon dioxide are presented in the following table. The solubility of hydrocarbons in physical solvents increases with the molecular weight of the hydrocarbon. Since heavy hydrocarbons tend to accumulate in the solvent, physical solvent processes are generally not economical for the treatment of hydrocarbon streams that contain a substantial amount of pentane-plus unless a stripping column with a reboiler is used.

Typical Physical Solvent Process

Gas Component

DEPG at 25°C

PC at 25°C

NMP at 25°C

MeOH at -25°C

DEPG (Dimethyl Ether of Polyethylene Glycol)

0.013

0.0078

0.0064

0.0054

DEPG is a mixture of dimethyl ethers of polyethylene glycol. Solvents containing DEPG are marketed by several companies including Coastal Chemical Company (as Coastal AGR®), Dow (Selexol™), and UOP (Selexol). DEPG can be used for selective H2S removal and can be conﬁgured to yield both a rich H2S feed to the Claus unit as well as bulk CO2 removal. DEPG is suitable for operation at temperatures up to 347°F (175°C). The minimum operating temperature is usually 0°F (-18°C).

Methane

0.066

0.038

0.072

0.051

Ethane

0.42

0.17

0.38

0.42

CO2

1.0

Propane

1.01

0.51

1.07

2.35

n-Butane

2.37

1.75

3.48

COS

2.30

1.88

2.72

3.92

MeOH (Methanol)

H 2S

8.82

3.29

10.2

7.06

The most common Methanol processes for acid gas removal are the Rectisol process (by Lurgi AG) and Ifpexol® process (by Prosernat). The main application for the Rectisol process is puriﬁcation of synthesis gases derived from the gasiﬁcation of heavy oil and coal rather than natural gas treating applications. The two-stage Ifpexol process can be used for natural gas applications. Methanol has a relatively high vapor pressure at normal process conditions, so deep refrigeration or special recovery methods are required to prevent high solvent losses. The process usually operates between -40°F and -80°F (-40°C and -62°C).

n-Hexane

11.0

13.5

42.7

Methyl Mercaptan

22.4

27.2

34.0

NMP (N-Methyl-2-Pyrrolidone) The Purisol Process uses NMP® and is marketed by Lurgi AG. The flow schemes used for this solvent are similar to those for DEPG. The process can be operated either at ambient temperature or with refrigeration down to about 5°F (-15°C). The Purisol process is particularly well suited to the purification of high-pressure, high CO2 synthesis gas for gas turbine integrated gasification combined cycle (IGCC) systems because of the high selectivity for H2S.

Choosing the Best Alternative A detailed analysis must be performed to determine the most economical choice of solvent based on the product requirements. Feed gas composition, minor components present, and limitations of the individual physical solvent processes are all important factors in the selection process. Engineers can easily investigate the available alternatives using a veriﬁed process simulator such as ProMax® which has been veriﬁed with plant operating data. For additional information about this topic, view the technical article “A Comparison of Physical Solvents for Acid Gas Removal” at http://www.bre.com/tabid/147/Default.aspx. For more information about ProMax, contact Bryan Research & Engineering or visit www.bre.com.

Bryan Research & Engineering, Inc. P.O. Box 4747 • Bryan, Texas USA • 77805 979-776-5220 • www.bre.com • sales@bre.com Select 113 at www.HydrocarbonProcessing.com/RS

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Our experienced team can jump on your tower service project and execute it reliably, safely and cost effectively. We provide all the specialized skills you need for tray/packing installation, tower maintenance and plant turnaround projects. We are not allied with any one manufacturer and can work with all types of separations products. We can completely retrofit your vessel from its external pipe flanges to even the most complex internals. No detail is overlooked. We remove and install trays and/or packing, provide tray support modifications, clad liner repair/installation, nozzles, installation and tower resections. We can help you creep your production by providing engineering services for pressure vessels and vessel modifications. Our design/analysis also includes calculations of induced loads and we provide AutoCad® drawings of all vessel/vessel modifications along with integrity analysis and re-rating. Our keen eye for quality includes inspections at all applicable stages by CCWI and API 510 qualified personnel.

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PROCESS CONTROL AND INFORMATION SYSTEMS 5

Meusinger, R. and R. Moros, “Determination of octane numbers of gasoline compounds from their chemical structure by 13C NMR spectroscopy and neural networks,” Fuel 80 (2001) 613±621. 6 Rusin, M. H., H. S. Chung and J. F. Marshall, “A ‘transformation’ method for calculating the research and motor octane numbers of gasoline blends,” Industrial and Engineering Chemistry Fundamentals 20 (1981) 195–204. 7 Singh, Aseema, “Modeling and model updating in a Real-Time optimization of gasoline blending,” Department of Chemical Engineering and Applied Chemistry, University of Torento. 8 Murty, B. S. N. and R. N. Rao, “Global optimization for prediction of gasoline of desired octane number and properties,” Fuel Processing Technology 85(2004) 1595–1602. 9 Zahed, A. H., S. A. Mullah and M. D. Bashir, “Predict octane number of gasoline blends,” Hydrocarbon Processing 5 (1993) 85–87. 10 Pasadakis, Nikos, Vassilis Gaganis and Charalambos Foteinopoulos, “Octane number prediction for gasoline blends,” Fuel Processing Technilogy 87 (2006) 505–509. 11 singh, A., J. F. Forbes, P. J. Vermeer and S. S. Woo, “Model-based real time optimization of automotive gasoline blending operation,” Journal of Process Control, vol. 10, pp. 43–58, 2000. 12 Filzmoser, P., “A multivariate outlier detection method,” Department of Statistic and Probability Theory. 13 Nguyen, Viet D., Raymond R. Tan, Yolanda Brondial and Tetsuo Fuchino, “Prediction of vapor–liquid equilibrium data for ternary systems using artiﬁcial neural networks,” Fluid Phase Equilibria 254 (2007) 188–197. 14 Inal, Fikret, “Artificial neural network predictions of polycyclic aromatic hydrocarbon formation in premixed n-heptane flames,” Fuel Processing Technology 87 (2006) 1031–1036. 15 Chegini, G. R., J. Khazaei, B. Ghobadian and A. M. Goudarzi,” Prediction of process and product parameters in an orange juice spray dryer using artiﬁcial neural networks,” Journal of Food Engineering 84 (2008) 534–543 16 Ramadhas, A. S., S. Jayaraj, C. Muraleedhahran and K. Padmakumari, “Artificial neural networks used for prediction of the cetane number of biodiesel,” Renewable Energy 31 (2006) 2524–2533. 17 DataFit software help. 18 Azamathulla, H. Md., M. C. Deo and P. B. Deolalikar, “Alternative neural

SPECIALREPORT

networks to estimate the scour below spillways,” Advances in Engineering Software (2007). 19 Yu, Wen and América Morales, “Gasoline Blending System Modeling via Static and Dynamic Neural Networks,” International Journal of Modelling and Simulation, vol. 24, no. 3, 2004p. 20 Himmelblau, David M., “Accounts of Experiences in the Application of Artificial Neural Networks in Chemical Engineering,” Ind. Eng. Chem. Res. 2008, 47, 5782–5796.

Elnaz Paranghooshi is a BSc graduate in chemical engineering from Tehran University, Iran, and is an MSc student at Iran University of Science and Technology (IUST). She began her engineering career in 2006 as an oil movement engineer in Tabriz refinery, Iran. Mrs. Paranghooshi is interested in modeling and simulation of refinery processes especially blending. She is an expert in using artificial neural networks and genetic algorithms.

Mohammad T. Sadeghi is a BSc graduate in chemical engineering from Sharif University of Technology, Tehran, Iran. He has an MSc degree from the Wollongong University, Australia and PhD from The University of Queensland, Brisbane, Australia. Dr Sadeghi is a lecturer in Iran University of Science and Technology (IUST). He is interested in modeling, simulation and optimization of chemical processes.

Sirous Shafiei – Khoroshahi got his chemical engineering BSc degree from Abadan Institute of Technology (AIT), Iran. He has his MSc degree from Shiraz University, Iran, and PhD from Toulouse institute of Technology, France. Dr Shafiei is employed at Sahand University of Technology and is interested in modeling, simulation and optimization of chemical processes.

H y d r o c a r b o n P r o c e s s i n g . c o m

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Benefits, User Experience and Engineering FOUNDATION Fieldbus Projects in the Hydrocarbon Processing Industry Hydrocarbon Processing and the Fieldbus Foundation are bringing together leading experts in the oil & gas industry to discuss the benefits, user experience and engineering FOUNDATION fieldbus projects in the second in its webcast series. Be a part of this exclusive event. Register at www.hydrocarbonprocessing.com How can you get the most from fieldbus standards? Get practical advice and insight from our expert panel. Rich Timoney, President and CEO, of the Fieldbus Foundation will lead the discussion on FOUNDATION fieldbus benefits for the oil & gas industry. He will provide an overview of the latest FOUNDATION fieldbus technology developments, including field device integration, field diagnostics, foundation for SIF, wireless & remote, as well as the FOUNDATION’s future plans. Also participating will be Dave Brown of Bechtel, who will be exploring Engineering Fieldbus Projects, Ravi Venkatramana, of Invensys who will be talking about supplier considerations and B.R. Mehta of Reliance Industries Ltd. who will give an end user perspective and results from Jamnagar Export Refinery Project (JERP). Discussion topics will include: • The Foundation’s latest advancements and its impact on suppliers and end users as it continues to take a leadership role in the deployment of the fieldbus standards. • Real-world examples of how end-users have been applying Foundation fieldbus to maximize benefits. • Best practices of how users can keep pace with industry requirements and protect their investments in its technology.

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PROCESS CONTROL AND INFORMATION SYSTEMS

SPECIALREPORT

Implementing and maintaining advanced process control on continuous catalytic reforming The primary benefit was an increase in reformate octane barrel yield from operating the plant at its economic constraints P. BANERJEE, Aspen Tech Middle East (ATME), Kuwait; and A. AL-MAJED and S. KAUSHAL,Kuwait National Petroleum Corporation (KNPC), Kuwait

dvanced process controllers (APCs) were implemented on two identical trains of continuous catalytic reforming (CCR) plants at the Mina Al-Ahmadi (MAA) refinery of Kuwait National Petroleum Corporation (KNPC) that paid off the project cost within a few months. Even though the CCR trains are identical, there were differences in the realized benefits reflecting their unique operating constraints. A fast-track project implementation methodology was adopted to accommodate several components of this APC project. The APC benefit was realized due to an increase in reformate octane-barrel yield resulting from operating the plant at its economic constraints. The reformate octane barrel yield increased due to an increase in throughput, improved heavy naphtha recovery, and an increase in reactor bed temperatures and reduction in reactor pressure. The controllers have a high online factor. To keep sustaining the controller benefits after its commissioning, certain APC parameters and key performance indicators (KPIs) are monitored that are also briefly discussed in this article. Introduction. Two identical trains of a catalytic naphtha

reforming plant of 18 kbpd capacity each became operational in 2004 at the KNPC MAA refinery. KNPC decided to implement the APC project after the plant was commissioned and stabilized at the design capacity to start getting the benefits early. Each train is comprised of a naphtha hydrotreater (NHT) plant followed by a stripper and splitter to separate out offgas, unstabilized naphtha and light naphtha (LNAP) from the hydrotreated naphtha and supply heavy naphtha (HNAP) as feed to the CCR Platforming unit. For simplicity, the NHT, stripper and splitter sections are together referred to as NHT in this article. In a hydrogen environment, HNAP is reformed to reformate in the presence of a moving catalytic bed in the Platformer unit. Catalyst from the Platformer reactor is continuously regenerated in a CCR–regenerator unit. The reformate product recovered from the debutanizer bottom is used as a gasoline blend component. The byproducts such as LNAP, hydrogen, LPG and fuel gas go back to the refinery.

On several occasions prior to the APC implementation, the plant tripped due to high temperature problems in the net gas compressors. This problem was effectively addressed through APC and it also stabilized the plant operation besides improving the reformate octane-barrel yield that paid off the project cost in a few months. APC project implementation. An automated stepper application was the workhorse for rapid deployment of the APC project on both trains.1 During the pretest phase of the project a preliminary manual step test was conducted to obtain a “seed-model” for the automated stepper that was used during the step test. It was important to obtain reasonably good initial-level models to manage the NHT inventory using the stepper application. The stepper application automatically perturbs the plant while maintaining the process variables within acceptable operator set limits. The initial few plant perturbations were comprised of long steps allowing the plant response to steady out to improve estimating the steady-state gains and obtain operator confidence on the stepper application. Subsequently the stepper was switched to a multitest1 mode whereby the plant is perturbed for several manipulated variables (MVs) using generalized binary noise (GBN)1 test signals at a relatively fast pace while maintaining the plant variability within acceptable limits. The stepper makes uncorrelated MV moves1 thereby not only reducing the step test duration but also providing better quality reliable models. It also aids in identifying a robust model. The combination of multitest and “sub-space”1 identification methodology results in a good quality reliable dynamic model that is characterized by tighter uncertainly bounds at all the frequencies. A robust multivariable model is controller relevant whose steady-state gain matrix is characterized by a smaller condition number. Condition number of the model matrix can be further improved through manual iterations using gain-ratio analysis or using optimization tools. Both CCR trains were sequentially step tested to develop separate models to reflect their unique operating characteristics. The use of an automated stepper helped to reduce step testing duration by about 50% compared to manual stepping. To meet the APC requirements all level loops in the NHT sections were HYDROCARBON PROCESSING OCTOBER 2009

I 59

SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS

broken and were managed using the stepper application. This relieved the operators and the project team from managing them manually during the step test. The final model curves get pretty much ready toward the end of the step test. Hence, the step test can be concluded by running the stepper in control mode to assess the quality of model predictions and initial control actions. It took even less time to step test the second parallel train since it started directly with the multitest using the final models of the first train as seed models. The controllers were commissioned after reviewing the models and simulating the controller performances. Partial least squares (PLS)-based algebraic steady-state inferential models such as LNAP 95%, HNAP 5% and reformate Rvp were deployed for predicting the product properties that are required to be controlled as controlled variables (CVs) by the APC. Controlling the inferential CVs to their desired limits greatly contributes to the economic benefits. The plant was manually tested for different steady-state conditions for developing inferential models. Rigorous kinetics-based proprietary steady-state online models for predicting the RON, heater duties, TMTs and catalyst coking were also deployed and integrated with the APC for controlling them as CVs. Such models can also be developed using commercially available offline kinetics modeling tools tuned to the plant

data. These models can then be deployed on line either through their online application if available or by further developing nonlinear regression-based inferential models. Custom screens were developed for the proprietary online and inferential models on the distributed control system (DCS) for operator interface. The screens display the model predictions and allow the operator to enter laboratory data for bias correction. Two examples of custom-developed DCS screenshots for the proprietary models and lab update for the inferential models are illustrated in Fig. 1. Sometimes developing the APCâ&#x20AC;&#x201C;DCS interface for the operators can be involved and time consuming. In this project, a commercial APCâ&#x20AC;&#x201C;DCS interfacing package was available that automatically generated the necessary interfaces and the APC operator screens on the DCS thereby saving a considerable amount of system engineering time. There are two servers, one supporting the APC and inferential applications and the other hosting the proprietary online models. Data communication between these servers with the DCS is via a dedicated gateway that is always a recommended practice to maintain robustness of the data communication. Each train has its own operating console and there is hardly any interaction between them. Hence, there is a dedicated APC for each train. Fig. 2 shows there are three controllers per train: NHT/Platformer, regenerator and debutanizer. The APC controller is divided into several subcontrollers for operational ease where each subcontroller typically represents a section of the plant. The NHT/Platformer controller is divided into four subcontrollers representing the NHT, stripper, splitter and platformer sections shown in Fig. 2. Overall controller objectives. The controllers are designed with the overall objective of maximizing the reformate octane barrel yield by operating the plant at its economic constraints. The benefits are realized by maximizing the recovery of HNAP, operating Platformer temperatures against a minimum RON, minimizing Platformer pressure and maximizing the reformate Rvp subject to the process constraints. The controller maximizes HNAP flow to the Platformer while balancing the inventories in the NHT, stripper and splitter. A single controller is designed for the NHT and Platformer sections since the NHT section manages the supply of HNAP feed of desired spec. to the Platformer. A separate controller strives to maintain a flat burn profile in the

Unstab. naphtha + offgas

Naphtha feed

NHT reactor system

NHT-plat APC

Naphtha stripper

Light naphtha

LPG + fuel gas

Naphtha splitter

Platformer reactor system

Debutanizer APC

HNAP

Reformate product Continuous catalytic regenerator system

Regenerator APC

FIG. 1

Examples of custom-developed DCS screenshots.

I OCTOBER 2009 HYDROCARBON PROCESSING

FIG. 2

Overview of APC boundaries.

N2 Air

www.customidee.com

mangiarotti. looking ahead

mangiarotti at a glance Fabrication experience since 1930 Fabricator of large, high pressure, high temp heat transfer equipment, and heavy wall, large diameter, & exotic material process equipment. Three fully staffed, equipped, modernized, & automated facilities. One of our facilities is located in Porto Nogaro within 600 meters of the docks. All design, fabrication & testing are performed in-house. Weld up to 300 mm thick. Heavy items 1500 tons plus. Very long items over 150 meters long. Experience with an array of Process Licensor Technologies. Engineer & design to internationally recognized codes & standards. Highly qualiﬁed with an array of construction materials and advanced fabrication techniques. Certiﬁcates & Licenses including ISO-9001 & ASME Stamps. Experience with a multitude of international EPCs & end users.

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info@mangiarotti.it www.mangiarotti.it

SPECIALREPORT

PROCESS CONTROL AND INFORMATION SYSTEMS

â&#x2013; A benefit of about 12 cents/barrel

at 2004 KNPC price levels was realized after implementing the APC that mainly manifested from an increase in the reformate octane-barrel yield and improvement in the recovery of the by-products. regenerator to enhance the catalyst life. A detailed description of the controller strategies is described next. Subcontrollers in the NHT section. The NHT reactor2

heats the sour naphtha feed coming from the upstream crude units and hydrotreats it in a fixed catalytic bed in the presence of hydrogen sourced from the Platformer section. The hydrotreating reaction decomposes organic sulfur and nitrogen components and removes organic metallic components2 that are detrimental for the Platformer catalyst. The hydrotreating reaction saturates the olefinic components thereby preventing certain operating problems in the Platformer reactors. In the NHT section APC takes feed as much as required to maintain the Platformer throughput. The NHT feed runs against the constraints of furnace firing, minimum hydrogen-to-feed ratio and a minimum reactor bed temperature while ensuring

K6EDG EG:HHJG: EGD8:HH 6C6ANO:G B>C>K6E DC"A>C: KE d[ <Vhda^cZ! 8gjYZ D^a VcY AE< 6HIB 9*&.&! *&--! +(,,! +(,-! +-.,! :C &(%&+"& '! >E(.)! )%.! )-& =^\]Zhi 6XXjgVXn Vaadlh WZhi edhh^WaZ 7aZcY^c\ id D[[^X^Va A^b^ih

adequate hydrotreating. The operator adjusts the lower limit of reactor bed temperature depending on sulfur analysis of the HNAP feed. Hydrotreated naphtha from the NHT goes to the stripper to strip off the H2S and unstabilized naphtha and the bottom flows to the naphtha splitter. The controller in the stripper section manipulates the reboiler steam to maintain a minimum reflux ratio to achieve adequate stripping as per the operating guidelines. The controller balances the input and output flow to maintain the stripper level. The stripper pressure is moved the least only to address the constraints. The naphtha splitter separates out LNAP from the stripper stabilized naphtha to obtain HNAP from the bottom to feed the Platformer reactor. It is important to maintain C6 components (benzene precursor) in HNAP below a specified limit to limit benzene below 1% in the reformate. C6 in HNAP is indirectly monitored by analyzing the 5% ASTM point. The controller maximizes the yield of HNAP by maintaining its 5% point just above a minimum limit to meet the Platformer feed spec. The controller maintains the LNAP 95% point above a minimum product spec. to indirectly control C7 in the LNAP. An inferential model is used for predicting the HNAP 5% and LNAP 95% points. The splitter column runs against the constraints of column pressure drop and valve openings while maintaining HNAP 5% and LNAP 95% points above their lower limits. The Platformer subcontroller dictates the HNAP flow; consequently the other subcontrollers manage the NHT intake and balance the inventories in the NHT, stripper and splitter sections. Fig. 3 shows that the splitter bottom level control improved by 80% after implementing the APC. Prior to APC, the naphtha splitter level used to swing to the alarm limits for which the operator was required to take large corrective actions for the NHT inventory and the HNAP feed. With APC only smaller corrections are required while maximizing HNAP feed to the Platformer subject to its constraints. Platformer subcontroller. The HNAP feed is preheated and mixed with hydrogen in a combined feed exchanger before entering the Platforming reactors. The Platforming reactions3,4,5 take place in a hydrogen-rich environment in the presence of a moving bed of catalyst passing through a series of four reactors. The heat of reaction is provided by separate natural-draft furnaces associated with each reactor. High temperature and low pressure favors the Platforming reactions3,4,5 such as dehydrogenation and isomerization of naphthenes and dehydrocyclization and isomerization of paraffins that increase the reformate RON. pre-APC

APC

Je id ' HVbeaZ HigZVbh 6jidbVi^X 8Va^WgVi^dc

;Vhi :Vhn BV^ciZcVcXZ LL

Naphtha splitter bottom level control FIG. 3 Select 158 at www.HydrocarbonProcessing.com/RS 62

Inventory control in naphtha splitter.

PROCESS CONTROL AND INFORMATION SYSTEMS Higher pressure favors hydrocracking, demethylation and aromatic dealkylation that end up in consuming more hydrogen. Depending on the Platformer conditions and feed composition, the proprietary online model makes the predictions that are used by the APC to make necessary MV moves. For example, Fig. 4 shows that APC maintains the H2/feed ratio almost toward its lower limit while maintaining coke on spent catalyst between its limits based on the coke predicted on the catalyst exiting the last reactor by the proprietary online model. The controller accords maximum priority to push the HNAP feed to the Platformer subject to the constraints such as the lower limit of H2/feed ratio and RON, heater temperatures and firing constraints. The reformate effluent from the last reactor is cooled, then compressed in a series of compressors. The compression section separates out hydrogen from the reformate. The recovered hydrogen is consumed by the NHT and Platformer reactors and the remaining hydrogen goes to the refinery header. Liquid reformate flows to the debutanizer column. The controller minimizes the reactor pressure on a priority basis to maximize the reforming conversion. Pressure minimization is done against the constraints of the upper limits of the net gas compressor maximum temperature and current and upper limit of predicted coke. The controller increases the recycle gas valve opening and the recycle gas compressor speed preferentially over reducing the feed for controlling the coke and H2/feed ratio. The controller balances the net gas compressor stages by maintaining compressor maximum temperature and current consumption below an upper

Steady-state coke

SPECIALREPORT

limit and avoids flaring. The controller optimizes the H2/feed ratio to trade off greater heat sink in the reactors, decreased coking, increased compressor load, reduced recycle H2 purity and increased reactor yield. Fig. 5 shows that prior to APC the net gas temperature often used to hit the maximum limit that sometimes led to a plant trip even though the inlet pressure was set high. With APC, the Platformer pressure was reduced yet consistently maintained the net gas maximum temperature below an upper limit. Based on the prevailing compressor constraints in train 1, the pressure was reduced by 0.1 kg/cm2 and 0.17 kg/cm2 in train 2. The controller manipulates the Platformer heater temperatures to maintain the RON prediction around its lower limit. The controller strives to maintain a flat inlet temperature profile across all four reactor beds by holding the temperature differences between the adjacent heaters close to zero with an allowance of ±1°C to allow the controller to attend to the heater constraints. The reactor inlet temperatures get reduced if any of the heater constraints such as the maximum TMT, convection temperatures, heater duties or the fuel gas pressure hit their upper limits. RON reduces with the reduction in the reactor inlet temperatures and the HNAP throughput can reduce to prevent RON from falling below its lower limit. To maintain the target RON, Fig. 6 shows that the APC increases the weighted average inlet temperature (WAIT): 4

WAIT = xiTi i=1

where xi and Ti are the percent of catalyst in the ith reactor and the inlet temperature respectively.

H2-to-feed ratio

LL LL FIG. 4

HIGH ACCURACY FLOW METERS FOR HIGH TEMPERATURES AND HIGH PRESSURES

Control of coke and H2/feed ratio in Platformer.

Pressure reduction due to APC

Platformer pressure

FIG. 5

pre-APC

Net gas compressor maximum temperature Platformer pressure

Pressure reduction due to APC in the Platformer.

APC

– – – – – – – – –

non-intrusive ultrasonic clamp-on technology for temperatures up to 750 °F independent of process pressure multi-beam for high accuracy wide turn down installation without process shut down no maintenance no pressure loss standard volume calculation

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11.01.2008 11:20:16 Uhr

PROCESS CONTROL AND INFORMATION SYSTEMS

WAIT

pre-APC

APC Convection temperature

Convection temperature APC

WAIT

FIG. 6

20°C

Temperature increase due to APC in Platformer.

FIG. 8

2nd temperature element (highest temperature)

Heater temperature pre-APC FIG. 7

APC

Temperature control due to APC in regenerator.

pre-APC

Temperature

Increase in WAIT due to APC

SPECIALREPORT

2 3 4 5 6 7 Catalyst ﬂow along the regenerator

Temperature profile control due to APC in the regenerator.

For train 1, APC could increase the WAIT by around 9.5°C and for train 2 it increased by around 6.5°C. Regenerator controller. Coke builds up on the catalyst as it slowly cycles through the reactor thereby deactivating its surface. The spent catalyst is regenerated in a CCR-regenerator3,5 in a number of steps where in one of the steps the coke in the catalyst gets burned leading to peaks in the catalyst temperature. It is desired to maintain a relatively flat temperature profile in the regenerator to prevent catalyst degeneration. The APC minimizes O2 content in the burn air while ensuring O2 controller output in the operable range. This helps in flattening the temperature profile and helps to reduce the temperature peaks in the burning zone to enhance the catalyst life. The controller ensures maximum coke burn rate without shifting the burn profile toward the bottom and maintains the air heater outlet bundle element temperature below the maximum limit. Fig. 7 shows that the APC could reduce peaks in the second temperature element (that indicates highest temperature) in the burn zone and it also shows that the heater temperature element is better controlled. Fig. 8 shows a flattening of the temperature profile in the peak burning region of the regenerator. Debutanizer controller. The reformate from the Platformer

compressor section goes to a debutanizer where the lighter LPG and off-gas is stripped off to obtain the final reformate product from the bottom. While the reformate RON gets determined in the Platformer reactor, its Rvp is controlled in the debutanizer. The controller maximizes the reformate Rvp while maintaining the overhead accumulator level to maximize the reformate yield. APC benefits. A benefit of about 12 cents/barrel at 2004

KNPC price levels was realized after implementing the APC that mainly manifested from an increase in the reformate octanebarrel yield and improvement in the recovery of the by-products. The increase in throughput and operating the NHT/Platformer units at their economic severity constraints helped to increase the reformate octane-barrel yield. The contributors to the benefit for both trains are summarized in Fig. 9. After implementing APC, the reformate yield increased by approximately 3% for both trains. However, the change in specific utility consumption was significantly different for both trains (Fig. 10) reflecting their unique operating constraints even though the Select 160 at www.HydrocarbonProcessing.com/RS 64

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SPECIALREPORT 9% Increase in train 1 byproducts

PROCESS CONTROL AND INFORMATION SYSTEMS

7% Increase in train 2 byproducts

Changes in speciﬁc production/consumption 10

40% Train 2 increase in reformate octane barrel

44% Train 1 increase in reformate octane barrel FIG. 9

Contributors to the overall benefit.

Percent

6 4 2

0.40%

0 -2 -4

Controller maintenance. KNPC is maintaining the controllers using an APC performance monitoring application. A high operator acceptance of the APC can be gauged by nearly 100% uptime for the CCR controllers since their commissioning in November 2005. Fig. 11 shows uptime for the main CCR APC for the past 22 months for one of the trains. Even the uptimes of the regenerator controllers have a very good track record given the fact that their operation is affected relatively more frequently due to reasons such as catalyst entrainment and choking. However, high uptime does not guarantee optimum controller performance hence, key performance indicators (KPIs) have been developed by KNPC MAA to help monitor the controller effectiveness.

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FIG. 10

6.90%

3% 2.90%

Reformate yield

trains are identical. The increase in specific utility consumption for train 2 did not reduce profitability much since the utility cost is less than 1% of the feed cost at the KNPC site.

7.50%

Train 1 Train 2

Speciﬁc FG consumption

-2.80% Speciﬁc steam consumption

Changes in yield and specific consumption.

After evaluating various APC KPIs available in some commercial APC monitoring packages and in the literature, KNPC MAA defined the KPIs namely the effective index (EI) and Kuwaiti dinar index (KDI) based on the APC utilization calculation proposed by A. G. Kern.6 KNPC MAA EI is then defined as APC utilization normalized for unit shutdown and upsets so as to reflect the true controller effectiveness. KDI is an online indication of the monetary benefits realized from APC using post-audit benefit analysis carried out after controller commissioning as a base case. It is assumed that post audit benefits will be realized if EI is 100%. EI = APC utilization / plant availability KDI = APC utilization x post audit KD value/100

HYDROCARBON PROCESSING is the leading monthly magazine for staying connected to the hydrocarbon processing industry. Published since 1922, HYDROCARBON PROCESSING provides operational and technical information to improve plant reliability, profitability, safety and end-product quality. The editors of HYDROCARBON PROCESSING bring you first-hand knowledge on the latest advances in technologies and technical articles to help you do your job more effectively. December 2009: Plant Design and Engineering • Project management • CAD/CAM • Laser scanning January 2010: Gas Processing Developments • Sulfur removal technologies • Liquefied natural gas (LNG) and gas-to-liquid (GTL) advances • Catalyst developments February 2010: Clean Fuels • Biofuels • Catalyst technologies • Sustainability As a paid subscriber you will receive, in addition to your 12 monthly issues, in print or digital:

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PROCESS CONTROL AND INFORMATION SYSTEMS 2

CCR APC controller online factor for a train

120

100 80

60 40

FIG. 11

Jun-08

Apr-08

Feb-08

Dec-07

Oct-07

Aug-07

June-07

Apr-07

Feb-07

Dec-06

Oct-06

Controller on-time for the past 22 months.

SPECIALREPORT

Processing, February 2006. Cabrera, C. N., “UOP Hydrotreating Technology,” Section 6.3, Handbook of Petroleum Refining Processes, editor, Robert A. Meyers, McGraw Hill Book Co., 1986. Weiszmann, J. A., “UOP Platforming Process,” Section 3.1, Handbook of Petroleum Refining Processes, editor, Robert A. Meyers, McGraw Hill Book Co., 1986. Conser, R. E., T. Wheeler and F.G. McWilliams, “Isomerization,” pp 723– 747, Chemical Processing Handbook, editor, John J. McKetta, Marcel Dekker Inc., 1993. UOP Website (http://www.uop.com) Kern, A. G., “Online monitoring of multivariable control utilization and benefits,” Hydrocarbon Processing, October 2005.

Pranob Banerjee is services manager with ATME Kuwait and heads the APC group. He is a chemical engineer with 20 years of industrial experience and holds a PhD degree in APC from the University of Alberta, Canada. Dr. Banerjee has APC implementation experience in refinery, LNG/NGL, fertilizer and petrochemical processes. Previously he worked with Engineers India Ltd and Reliance Industries Ltd in India.

These KPIs are helping to manage about 25 APCs operational at KNPCs MAA Refinery. HP Ahmad Al-Majed is a senior process control engineer at ACKNOWLEDGMENTS The authors thank their respective management for its support and thank Lamia Al-Khandari, Yousuf Al-Sairafi, Subhash Chander Singhal, operations, lab and process staff from KNPC for supporting the project during its different implementation phases; Anand Shah and Altaf Khan from ATME for building the APC–DCS interface and providing the maintenance support respectively and other previous implementation team members.

Kuwait National Petroleum Company’s Mina Al-Ahmadi Refinery. He has a BS degree in chemical engineering from Kuwait University and an MBA from Leeds University. Mr. Al-Majed has over 17 years’ experience in process engineering and APC in different refinery processes such as ARD, HCR, HP, VR, FCC and gas plants.

LITERATURE CITED Kalafatis, A., K. Patel, M. Harmse, Q. Zheng and M. Craik, “Multivariable step testing for MPC projects reduces crude unit testing time,” Hydrocarbon

Suresh Kaushal is lead process control engineer at Kuwait National Petroleum Company’s Mina Al-Ahmadi Refinery. He holds a BTech degree in chemical engineering from IIT Kanpur and has over 24 years’ experience in refinery DCS systems and APC implementation in CCR, HCR, ARD, NGOD, CDU, VR, PRU, FCC and gas plants.

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SAFETY/MAINTENANCE

Design and implement an effective equipment integrity management system Consider this “integrity-only-specific” innovative methodology M. SPAMPINATO, ERG Raffinerie Meditterranee S.p.A., Priolo, Italy; and F. NICOLÒ, TEFEN Venture Consulting Italy, Rome, Italy

“What more is there to say about integrity management (IM) in the industry? Are there any innovative approaches or ideas to be conceptualized and applied?”

e tend to think that, at first, even informed industry actors’ answers would be negative; after all, this was the first answer that came to our minds, too. The reason is that IM is one of those “hot industry issues,” especially in refineries, petrochemical plants and process industries in general, where literature and best practices are somewhat overwhelming. All major players are striving to reach IM “best-in-class” practices, and IM is more often than not on the industry’s management agenda. On the other hand, though, recent worldwide disastrous events, also in refineries where current best practices are applied, demonstrated that there is room for improvements. Incidents’ cause analyses and reports show that improvements are to be found both in the utilized conceptual approaches to IM and in their applications “in the field.” Moreover, IM improvement programs are somehow “heavy and long initiatives” in terms of their impacts on resources and time. So the next question that comes to mind is, “Is there a way to sharply focus and holistically systemize the IM application in a refinery site (or in a process site in general)—an approach that would not sacrifice its long-lasting effectiveness on the verge of efficiency?” FIG. 1 This business issue was at the heart of our work that led to the

conceptualization of the frameworks, ideas and practical tips reported in this article. In current international practices, equipment IM is usually included as part of a more general maintenance, safety and reliability system, also called the Asset Management System (AMS), that involves a wide variety of refinery activities requiring efforts in terms of money, time and human resources. Equipment integrity, nonetheless, is somehow a more stringent issue than reliability, especially for aged refineries/petrochemical sites, not only because of its clear economical implications, but also because of wide responsibilities stemming from increasingly stringent legislation and public requirements—responsibilities that will, more and more, require convincing reactions in reasonably short time frames. Starting from this assumption, we’ve focused our extensive research and assessment of current industry best practices with the goal of understanding how these issues were tackled. We’ve found that, while the current industry’s best practices do focus on

EIMS overall framework.

some of the most important IM elements, they still leave “unfocused” some other critical elements that are, in our opinion, necessary not only to sharpen the concept of IM, but also to design and then successfully apply an effective “self-updating” IM system in a complex and running refining site, especially one that is well far along in its life cycle (as most of the European and US refineries are). Based on these findings, we’ve strived to develop a “new” approach to IM systems, proposing an innovative methodology, “integrity-only-specific,” providing the key points and minimum requirements to develop an IM system or to check the capability of an imported one. The basic statement of integrity is that a plant’s management needs to state, assign and reinforce a maximum accepted risk level. This basic statement is then articulated in an “holistic management system” which, while leveraging on the most peculiar elements of current industry practices (just to name a few: RBI strategies, FMEA tools and RCA methodologies), is also meant to be embedded into the existing organization, capable of identifying, coordinating and leveraging on the organization’s resources to actively manage integrity, while minimizing the impact of the necessary effort to develop and manage it, and also triggering an autonomous and ongoing improvement process through the years. The methodology has been developed at the ERG Raffinerie Mediterranee S.p.A. Refinery, one of the largest and complex European supersites. HYDROCARBON PROCESSING OCTOBER 2009

I 69

SAFETY/MAINTENANCE General and specific definitions and concepts. Our work builds

and specifies some general industry-wide accepted concepts such as: asset integrity and asset integrity management. Based on this, we’ve then specified the concept of equipment integrity management. All those concepts are stated in the following: • Asset integrity is the ability of the asset to perform its required function effectively and efficiently at the assigned risk level. • Asset integrity management system (AIMS) is the means of ensuring that people, systems, processes and resources that affect asset integrity are in place, in use and fit for purpose over the entire asset life cycle.

• Equipment integrity management system (EIMS) is an AIMS that focuses only on refinery equipment. It is specifically deployed into the refinery operational perimeter, considering the appropriate industry practices, and is able to maintain and improve the assigned risk level. EIMS overall framework. The initial

effort in developing our EIMS has been developing an innovative, clear and comprehensive framework capable of providing a systematic view and guidelines to the further detailed design and onsite real implementation of the system’s components. In other words, we felt the need to codify the concept that EIMS is a system that con-

TABLE 1. Criteria for inclusion and exclusion What’s included in EIMS

Why

Equipment and machinery

Refinery’s iron equipment

Pipes

Elements impacting directly on integrity of iron equipment

Tanks

Elements ensuring iron equipment integrity and

Equipment

sustainability

Mechanical control systems (control systems for pressure, temperature and, when necessary, capacity) Equipment supports and instruments rooms

EIMS integrity perimeter. Perim-

DCS (Included when relevant) ESD What’s not included in EIMS

Why not

Emission systems

Elements not controlled by refinery

Process controls (process automation, excluding critical elements, i.e., valves)

Elements not impacting iron equipment integrity

Emergency elements or damange mitigation involved when it is an already-occurred integrity loss

Plant analyzer Laboratories Fire-fighting systems Environmental detection systems Building structures (office, warehouse and other civil buildings) Priolo serviz (an interface with EIMS msut be defined) SGS (334), SGA, 626, safety report Definition “Asset integrity perimeter” is defined as the set of physical assets whose integrity must be assured by the effective and continuous application of the EIMS Main concepts The EIMS asset integrity preimeter is defined on the “iron’s rule”: • “Safeguard iron refinery equipment and any other asset directly impacting this equipment” EIMS asset integrity perimeter does include critical equipment and machinery, pipes and tanks EIMS also includes those “non-iron” physical assets and systems whose integrity and functionality directly impact other included assets (equipment, machinery, pipes and tanks) EIMS must define an interface with third parties to ensure that its objectives will be integrity-compliant 70

I OCTOBER 2009 HYDROCARBON PROCESSING

trols a specific set of the assets’ integrity, regulating and properly managing an operational scope of activities throughout a given and specific management subsystems. The overall conceptual framework that articulates this concept, as shown in Fig. 1, is based on three “macro-elements”: • Equipment integrity perimeter is the set of critical assets whose integrity must be guaranteed by the EIMS through the equipment life cycle. • Operational integrity scope comprises all those refining operational activities that will be regulated and monitored because they impact equipment integrity. • Integrity management subsystems assure an effective equipment integrity perimeter across the defined “operational scope.” An effective and efficient EIMS implies the interaction and integration of these three main macro-elements. Although it might appear to be a methodological and theoretical approach, starting from this point, this framework provided our future work with the necessary overall view that, we feel, enabled us to develop a coherent yet comprehensive and detailed management system. eter identification of “what’s included” is the essential and preliminary action requiring a clear understanding of what affects integrity according to the assigned risk level (defined as the probability and consequences of damage). In other words, the perimeter is not an absolute definition of assets; it depends on management choices or external requirements. Understanding “what’s not included,” in our opinion, is at least as important as understanding what’s included, thus avoiding confusion and overlapping with other requirements or management systems. Perimeter definition is also based on the accurate analysis of what existing management systems are focused on and the site’s asset management to optimize EIMS compliance and sinergies with them. It’s suggested that you should not include in the EIMS perimeter such assets or issues as personnel safety systems, mitigation or protection devices, or other systems that respond to legal requirements (i.e., Seveso 2 law, environmental, etc.); being only requested for the EIMS to be compliant with them. Another driving principle that we’ve followed to define the EIMS perimeter is the “stepwise approach”—meaning to accurately choose those “physical assets” that urgently and primarily need to be controlled for their potential and direct impact on the overall

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SAFETY/MAINTENANCE level of integrity risk defined by management. This prioritizing approach is suggested also to calibrate priorities and resource (human and financial) availability. Table 1 exemplifies the approach for the perimeter’s definition. EIMS operational integrity scope.

Defining the equipment perimeter you want to cover is only the first step to putting in place an effective system to control the integrity risk level to be maintained under a defined risk level. Regulate and consistently execute all those operational activities that allow controlling the cited risk level. This brings us to the second “pillar” of our framework which is the operational integrity scope, defined as “all those refining operational activities that will be regulated and monitored because of their direct impact on the integrity of specific assets (EIMS asset integrity perimeter).”

In concrete terms, the EIMS operational integrity scope covers the activities potentially affecting integrity: process conditions, maintenance approach, predictive techniques, corrosion prevention, KPI management, etc. It’s very important to keep in mind that, to effectively cover the EIMS operational scope, it’s essential to actively pursue a balanced approach among process, technical and mechanical dimensions. Experience tells us that, despite a good approach regarding maintenance/mechanical techniques, out-of-control process conditions can heavily deteriorate integrity expectations. Process conditions under control require a clear understanding of potential damage mechanisms and, for that, the corrosion department plays an essential role; refineries, not including an internal corrosion department, should avoid using general settings on corrosion issues.

HAVER & BOECKER

The Solution Provider

TABLE 2. Framework for the EIMS operation scope Activity area

EIMS focus for asset integrity assurance

1. Risk Assessment and management Corrosion management (CCG)

Need to regulate, specify and cover all EIMS dimensions (organization, people, document and data management system, audit and review systems)

Risk-based inspection (RBI) strategy Equipment control and tracking Incident investigation analysis (RCA and FMEA) 2. Integrity-relevant maintenance Temporary maintenance management

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3. Management of change

The INTEGRA ®

Project/equipment mapping and updating 4. Integrity-relevant document management Prevention measures mapping and updating Laws and prescriptions mapping and updating EIMS procedure updating 5. Audit and review KPI management Management reviews 6. Equipment operational activities management 7. Equipment ordinary maintenance management

completely mounted filling plant in a dust-capsuled housing for poor-flow, floury, powdery and granuled bulk materials in paper-, PE- or PP valve bags

Maintenance integrity requirements for specific technical activities Definition of EIMS operational integrity scope All those refining operational activities that will be regulated and monitored because of their direct impact on the integrity of specific assets (EIMS asset integrity perimeter) Main concepts EIMS management subsystems will regulate and monitor all operational scope activities to guarantee an effective integrity management Operational scope will be used as a reference to implement all management subsystems (i.e., the procedural system will be mapped referring to operational scope activities) Select 161 at www.HydrocarbonProcessing.com/RS 䉴

HAVER & BOECKER, Germany Phone: +49 2522 30-271 E-mail: chemie@haverboecker.com

www.haverboecker.com

The designation ® indicates a registered trademark of HAVER & BOECKER OHG in Germany. Several indicated designations are registered trademarks also in other countries worldwide. M 921-E4

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SAFETY/MAINTENANCE

Strategy guidelines and objectives A set of strategic and business-driven guidelines that focus, drive and enable effective EIMS deployment and continuous improvement in the reďŹ nery

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Key performance indicators (KPIs) A â&#x20AC;&#x153;pyramidalâ&#x20AC;? set of performance, operational and management measures that can be used to effectively and continuously monitor and upgrade the overall system

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Â&#x2C6;Â&#x2C6;Â&#x2039;Â&#x2026;Â&#x2039;Â&#x2021;Â?Â&#x2013; Â&#x17D;Â&#x192;Â?Â&#x2013; Â&#x2019;Â&#x2021;Â&#x201D;Â&#x192;Â&#x2013;Â&#x2039;Â&#x2018;Â?Â&#x2022; Â&#x2C6;Â&#x2C6;Â&#x2021;Â&#x2026;Â&#x2013;Â&#x2039;Â&#x2DC;Â&#x2021; Â&#x2021;Â&#x2026;Â&#x160;Â?Â&#x2018;Â&#x17D;Â&#x2018;Â&#x2030;Â&#x203A; Â&#x2018;Â&#x17D;Â&#x2014;Â&#x2013;Â&#x2039;Â&#x2018;Â?Â&#x2022;

h^ Í&#x2014; Đ˝Ď ÍžĎŽĎ´ĎÍż Ď´ĎŽĎľÍ˛ĎŻĎŻĎŽĎŽ ĆľĆ&#x152;Ĺ˝Ć&#x2030;Ä&#x17E;Í&#x2014; Đ˝ĎŻĎ° ÍžĎľĎŻÍż ĎŻĎłĎąÍ˛ĎŻĎąĎŹĎŻ >Ä&#x201A;Ć&#x161;Ĺ?Ĺś ĹľÄ&#x17E;Ć&#x152;Ĺ?Ä?Ä&#x201A;Í&#x2014; Đ˝ĎąĎ° ÍžĎĎÍż Ď°ĎąĎąĎąÍ˛ĎąĎłĎŹĎŻ

Ĺ?ĹśÄ¨Ĺ˝ĆľĆ?Ä&#x201A;Î&#x203A;Ć?Ĺ˝Ć&#x161;Ä&#x17E;Ĺ?Ä?Ä&#x201A;Í&#x2DC;Ä?Ĺ˝Ĺľ Í&#x2013; Ç Ç Ç Í&#x2DC;Ć?Ĺ˝Ć&#x161;Ä&#x17E;Ĺ?Ä?Ä&#x201A;Í&#x2DC;Ä?Ĺ˝Ĺľ Select 162 at www.HydrocarbonProcessing.com/RS

Organization and people and culture The organizational, cultural and peoplerelated elements that allow effective EIMS deployment and continuous improvement in the reďŹ nery

Procedural system A coherent set of procedural guidelines aiming to ensure that all the activities in the â&#x20AC;&#x153;EIMS operational scopeâ&#x20AC;? are performed on and related to the assets included in the â&#x20AC;&#x153;EIMS asset integrity perimeterâ&#x20AC;? following the industry best practices that guarantee equipment integrity

Document and data management system A set of integrated information and decision support system that allows the effective management and continuous improvement of all the EIMS subsystems across operational scope

FIG. 2

DeďŹ nition Integrity management subsystems aim to ensure effective asset integrity perimeter across deďŹ ned â&#x20AC;&#x153;operational scopeâ&#x20AC;? and throughout the asset life cycle.

Main concepts All ďŹ ve management subsystems guarantee a continuous proactive system improvement. EIMS subsystems will not overlap with the reďŹ neryâ&#x20AC;&#x2122;s existing management system; on the contrary, their overall development philosophy will maximize synergies with the reďŹ neryâ&#x20AC;&#x2122;s organization and other quality systems. EIMS subsystem frameworks will also be used to develop a coherent and convergent EIMS roadmap to effectively manage the EIMS development program toward full implementation.

EIMS management subsystem framework.

Overall integrity tableau de board

Overall risk index

Main concepts t ,1* TZTUFN XJMM CF EFWFMPQFE UISPVHI GPVS NBJO ,1* UZQFT t 0WFSBMM JOUFHSJUZ UBCMFBV EF CPBSE XJMM b1 b2 ....bn c1 c2 ....cn d1 d2 ....dn Weights a1 a2 ....an CF EFWFMPQFE GSPN UIFTF ,1* UZQFT Field and operation integrity status monitoring Past and future status KPIs t ,1*T XJMM CF XFJHIUFE UP NFBTVSF TZOUIFUJD ,1* NVTU BMMPX JOEFYFT GPS FWFSZ NPOJUPSJOH PG DVSSFOU KPIs KPIs KPIs ,1* UZQF 1SPDFEVSBM 1FPQMF BOE PSHBOJ[BUJPOBM %PDT BOE EBUB NBOBHFNFOU JOUFHSJUZ TUBUVT BOE BMFSUT PG GVUVSF QPTTJCMF t 0WFSBMM SJTL JOEFY JO FGGFDUJWFOFTT CFIBWJPS JOUFHSJUZ TZTUFN JOUFHSJUZ DPWFSBHF UFSNT PG JOUFHSJUZ XJMM CF JOUFHSJUZ MPTTFT BMJHONFOU BOE DPNQMJBODF NFBTVSFE XFJHIUJOH %BUB BOE EPDVNFOUBUJPO ,1* TIBMM CF BSUJDVMBUFE ,1* NVTU FOTVSF JG QFPQMF ,1* UZQF TZOUIFUJD BOE PSHBOJ[BUJPOBM CFIBWJPS NBOBHFNFOU TZTUFN NVTU TP UP NFBTVSF SFBM JOEFYFT UP QSPWJEF B CF DPOTUBOUMZ NPOJUPSFE JO DPNQMJBODF FYFDVUJPO JT JOUFHSJUZ DPNQMJBOU PS OPU TZOUIFUJD NFBTVSF WT XIBU JT TUBUFE JO QSPDFEVSBM NPEFM

p2 p3 p4

*U TIBMM BMTP TVQQPSU FGGFDUJWF UBSHFU BDRVJTJUJPO BOE JODFOUJWFT NBOBHFNFOU

UFSNT PG EBUB BOE GVODUJPOT FGGFDUJWF DPWFSBHF BOE iPO mFMEw DPSSFDU TZTUFN T VUJMJ[BUJPO

Compliance KPIs &*.4 NBJO FMFNFOUT EFQMPZNFOU GVODUJPOJOH BOE DPOUJOVPVT JNQSPWFNFOU NVTU CF NFBTVSFE BHBJOTU &*.4 FYQFDUFE UBSHFUT

FIG. 3

EIMS management subsystem framework.

A â&#x20AC;&#x153;technical integrity team,â&#x20AC;? comprising corrosion, process, inspection and mechanical engineers, is strongly recommended to effectively link all these items. The adequate number of technical integrity team members depends on refinery complexity; they meet every month. The technical integrity teams report to the â&#x20AC;&#x153;integrity operational teamâ&#x20AC;? and an â&#x20AC;&#x153;integrity steering committee,â&#x20AC;? formed by refinery management, is in charge to monitor the EIMS activities. A proper database regarding the critical process conditions for integrity needs to be planned and applied; this can be a portion of the existing database provided that the data are selected for each unit by the integrity team and qualified as â&#x20AC;&#x153;integrity data.â&#x20AC;? Appropriate KPIs are necessary for alarms or alerts. Table 2 standardizes the approach to define and manage the operational scope.

EIMS subsystems. How to reach the

first-class integrity objectives is a matter of debate and many valid approaches are available. On the other hand, we think that all efforts should be done to identify the minimum subsystems to be developed and put under control by management to be able to feasibly design and develop an integrity system. In this context, and based on author experience, a successful EIMS implementation into a site such as a refinery requires five management â&#x20AC;&#x153;subsystemsâ&#x20AC;? to be designed, in place and mutually coherent. Itâ&#x20AC;&#x2122;s also important to highlight that a people culture approach is essential, especially self-updating procedures for developments, applications, revisions and improvements. Igniting and sustaining this â&#x20AC;&#x153;mobilization streamâ&#x20AC;? will require a stronger effort at the projectâ&#x20AC;&#x2122;s beginning,

SAFETY/MAINTENANCE

Roles and responsibilities t &*.4 XJMM SFRVJSF SPMFT BOE SFTQPOTJCJMJUJFT SFEFmOJUJPO GPS BMM GVODUJPOT JOWPMWFE t *U XJMM JNQBDU EJSFDUMZ QFSTPOOFM KPC EFTDSJQUJPOT t 3FWJTFE KPC EFTDSJQUJPOT XJMM SFRVJSF OFX DPNQFUFODJFT BOE TLJMMT UP CF EFWFMPQFE XJUI B USBJOJOH QSPHSBN

EIMS

Integrity teams t 5ISFF UZQFT PG JOUFHSJUZ UFBNT XJMM CF EFWFMPQFE UP HVBSBOUFF &*.4 t -FBEFSTIJQ JOUFHSJUZ UFBN t 0QFSBUJPOBM JOUFHSJUZ UFBN t 5FDIOJDBM JOUFHSJUZ UFBNT Integrity manager t 5&$1&3 T SFTQPOTJCMF XJMM CF UIF JOUFHSJUZ NBOBHFS XJUI UIF PWFSBMM SFTQPOTJCJMJUZ UP DPPSEJOBUF BMM &*.4 NBOBHFNFOU TVCTZTUFNT BOE QSPQPTF BDUJPOT BOE EJSFDUJPO UP DPOUJOVPVTMZ JNQSPWF UIF TZTUFN Integrity coordinator t &*.4 XJMM SFRVJSF UIF FTUBCMJTINFOU PG B OFX TUBGG PSHBOJ[BUJPOBM SPMF BT BO PQFSBUJPOBM BOE QJWPUBM FMFNFOU PG UIF PWFSBMM TZTUFN T NBOBHFNFOU

FIG. 4

EIMS impact on existing and new organizational dimension.

but this offers the advantage that procedures and other activities will be developed coherently with local cultures, experiences and resources. The integrity requirements have to match with the organization and job description. All the positions need to be reviewed to include integrity responsibilities, from operators up to the CEO, who will have the duty to assign the accepted risk level and to provide adequate resources (human and financial). Fig. 2 exemplifies the proposed EIMS management subsystem framework. While all the identified subsystems are equally important and must be designed, developed and managed in a coherent and integrated way, two of themâ&#x20AC;&#x201D;the â&#x20AC;&#x153;organization and people and cultureâ&#x20AC;? system, and the â&#x20AC;&#x153;key performance indicatorâ&#x20AC;? (KPI) systemâ&#x20AC;&#x201D;are based on what we consider a different and innovative way to look at two industry-wide discussed integrityrelated issues, specifically: â&#x20AC;˘ How do you effectively monitor the integrity of an asset perimeter? â&#x20AC;˘ How do you embed the necessary integrity-relevant organizational structure into the existing organization? The following paragraphs articulated these two issues that we consider of significant importance for overall EIMS effectiveness. EIMS KPIs. The EIMS KPI system is developed on some key ideas, some of which are taken from industry best practices. Others represent, in our opinion, key differentiating principles. Below, we summarize the EIMS KPI systemâ&#x20AC;&#x2122;s main characteristics:

â&#x20AC;˘ The KPI system produces an overall â&#x20AC;&#x153;integrity risk indexâ&#x20AC;? that represents the â&#x20AC;&#x153;weighted outlookâ&#x20AC;? of four main â&#x20AC;&#x153;integrity risk dimensions.â&#x20AC;? â&#x20AC;˘ Integrity risk dimensions can be classified into two different types: â&#x20AC;&#x153;field and operation integrity statusâ&#x20AC;? and â&#x20AC;&#x153;compliance KPI.â&#x20AC;? â&#x17E;¤ The first type of KPI must allow monitoring current integrity status and alerts of possible future integrity losses. â&#x17E;¤ The second type of KPI must allow measuring a real compliance execution versus what is stated in the fundamental management systems (especially regarding the procedural effectiveness, people and organizational behavior integrity alignment, and document and data management system integrity coverage and compliance). â&#x20AC;˘ KPIs are useless if not correctly balanced between a â&#x20AC;&#x153;lagging focus,â&#x20AC;? based on results and a â&#x20AC;&#x153;leading focus,â&#x20AC;? based on tendency. No general rules can be suggested, but the continuous experience is the right way. In Fig. 3, weâ&#x20AC;&#x2122;ve exemplified the EIMS KPI framework. EIMS organizational model. One of the key ideas of our approach is that the EIMS should be embedded in the existing organization, mainly because it needs to be a â&#x20AC;&#x153;self-updatingâ&#x20AC;? management system capable of evolving continuously and adapting to a changing environment and asset configuration. Frequently, in our research, weâ&#x20AC;&#x2122;ve found IM systems very well designed, built upon innovative and effective concepts, though having more often than expected one â&#x20AC;&#x153;missing point.â&#x20AC;? They were conceived and

Predictability. We know that unplanned shutdowns can bring you to a screeching haltâ&#x20AC;&#x201D;which means increased costs in downtime, overtime and expedited materials fees. What if you could save money on annual maintenance costs by planning shutdowns? Now, you can. The predictive capabilities of our control systems give you advance warning signs that enable you to identify potentially big problems well before an unplanned shutdown. Call us today or visit www.dresser-rand.com to discover how our Condition Monitoring experts can help you choose the best route to lower maintenance costs and increase proďŹ tability.

The Americas: (Intâ&#x20AC;&#x2122;l +1) 713-354-6100 ESA: (Intâ&#x20AC;&#x2122;l +33) 2-35-25-5225 Asia-PaciďŹ c: (Intâ&#x20AC;&#x2122;l +60) 3-2093-6633 info@dresser-rand.com

www.dresser-rand.com

Select 163 at www.HydrocarbonProcessing.com/RS

SAFETY/MAINTENANCE

3FmOFSZ EJSFDUPS ."/ 5&$1&3 5&$." 01&3"5*0/4 47*-$0

Leadership integrity team t 5P QFSJPEJDBMMZ EFmOF JOUFHSJUZ SFMBUFE UBSHFUT BOE HVJEFMJOFT GPS &*.4 CBTFE PO DVSSFOU &3(.FE PWFSBMM CVTJOFTT TUSBUFHZ t 5P TFU EJSFDUJPO BOE HVBSBOUFF DMFBS BOE TUSPOH DPNNFOU UPXBSE &*.4 UBSHFU t 5P BOOVBMMZ SFWJFX JOUFHSJUZ SFTVMUT BOE UP EFmOF BO BOOVBM JOUFHSJUZ BDUJPO QMBO t 1SFMJNJOBSZ BDUJPO QMBO t %BUB BOBMZTJT BOE FWBMVBUJPO t 4VHHFTUJPOT

t %FmOFE BDUJPO QMBO t 4USBUFHJD HVJEFMJOFT t 1SJPSJUJFT

Integrity manager Operational integrity team

5&$1&3 5&$." *5 03("/*;"5*0/

t 5P EJSFDUMZ NBOBHF BMM BDUJWJUJFT OFFEFE UP JNQMFNFOU BOE DPOUJOVPVTMZ JNQSPWF &*.4 t 5P BTTVSF UIBU BMM &*.4 NBOBHFNFOU TVCTZTUFNT BSF FGGFDUJWFMZ EFQMPZFE BOE QSPQFSMZ VQEBUFE t *OUFHSJUZ SFMFWBOU EBUB SFQPSUT

t "DUJPO QMBO JNQMFNFOUBUJPO t 3PMFT BOE SFTQPOTJCJMJUJFT

Integrity coordinator

*OTQFDUPS 5FBN MFBEFS Integrity coordinator .BJOUFOBODF FOHJOFFS t 5P BTTVSF UIBU BMM &*.4 HVJEFMJOFT BSF FGGFDUJWFMZ EFQMPZFE PO EBZ UP EBZ SFmOFSZ 1SPDFTT UFDIOJDJBO BDUJWJUJFT SFHBSEJOH UIFJS TQFDJmD TDPQF PG BDUJPO t 5IFSF XJMM CF UISFF UZQFT PG UFBNT GPS TQFDJmD BTTFUT QJQFT FRVJQNFOU BOE NBDIJOFSZ BOE UBOLT BOE JOTUSVNFOUT

FIG. 5

EIMS organizational framework.

Focus on development

Focus on design +3 m

+9 m

+1 yr

+1.5 yr

Focus on deployment

+2.5 yr

+3 yr

Procedural subsystem

KPI subsystem

KPI system KPI pilot monitoring Field and operation EIMS status KPIs design Procedural effectiveness Data and document ďŹ ne tuning management KPIs design Organizational system KPIs design system Procedural system People and organization ďŹ ne tuning ďŹ ne tuning behavioral KPIs design Enlarged Training operational continuous Development scope all of other execution Operational scope Operational scope procedures systems risk management technical procedures design Continuous Job description required alignment related procedures and guidelines design by EIMS communication design (FMEA, for integrity RBI, RCA, ...) awareness building Development Integrity team full of priority deployment (training, Operational scope Overall training ďŹ ne tuning,...) systems managerial procedures and communication and guidelines design Incentives SIP strategy and system framework action plan deďŹ nition integration Integrity Preliminary Integrity team Priority communication model design teams training systems programs plan execution Overall identiďŹ cation deďŹ nition feasibility and Change Activate study speciďŹ cation management integrity EIMS blueprint action plan manager and coordinator print approved

by management

FIG. 6

Organization and people and culture subsystem

EIMS roadmap.

designed either as â&#x20AC;&#x153;stand-aloneâ&#x20AC;? projects or as â&#x20AC;&#x153;big transformational programsâ&#x20AC;? requiring an enormous amount of resources. This was, in our opinion, almost a killing concern, especially in an organizational structure, such as a refinery supersite, where organizations are strictly rightsized, often fighting against resource scarceness. What we strived to conceive, instead, 76

Document and data management subsystem

I OCTOBER 2009 HYDROCARBON PROCESSING

was a â&#x20AC;&#x153;sound organizational approachâ&#x20AC;? that would allow the EIMS to be developed, deployed and continuously updated by the same existing organization, while not constituting a heavy burden that could very well â&#x20AC;&#x153;kill the initiativeâ&#x20AC;? from the beginning. We then came out with a light, flexible, yet focused EIMS organizational model that is articulated in Figs. 4 and 5.

EIMS roadmap. EIMS design and development is not a â&#x20AC;&#x153;quick hit.â&#x20AC;? Defining the overall framework and model, even though having done so in a coherent and detailed way, is only the beginning. It sets the direction and the target to achieve but not the way to get there. To get where we want to take our organization, with all the pieces effectively in place, a clear, well-thought-out and multiyear roadmap should be defined, agreed upon and communicated throughout the organization, from the top to the bottom. You need a visual, simple, yet comprehensive development map that highlights the main â&#x20AC;&#x153;development path dimensionsâ&#x20AC;?â&#x20AC;&#x201D; the phases through which the organization should go through and the main activities to be undertaken. The roadmap is then not only a â&#x20AC;&#x153;master plan;â&#x20AC;? itâ&#x20AC;&#x2122;s also a strategic communication and planning toolâ&#x20AC;&#x201D;a tool that can be used to visualize the macro and fundamental connection among the different development pathsâ&#x20AC;&#x2122; activities and dimensions. In the mentioned context, it is important to keep in mind that â&#x20AC;&#x153;choosing the right roadmap development dimensionsâ&#x20AC;? is the key success factor of the overall initiative. Development dimensions should be identified to clarify the interactions among different strategic initiatives along the roadmap and to highlight their timeline coherence. In our case, weâ&#x20AC;&#x2122;ve chosen the previously identified five management subsystems as the development dimensions because they represent the EIMSâ&#x20AC;&#x2122; backbone. In Fig. 6, weâ&#x20AC;&#x2122;ve exemplified the roadmap to effectively manage the design, development and deployment of our EIMS. HP

Martino Spampinato is the technical manager at ERG Raffinerie Mediterranee S.p.A. He has previous experience in refinery and power plant operations, aromatics production, planning, scheduling and process technology. Mr. Spampinato is a chemical engineer graduated from Padua University.

Fabio NicolĂ˛ is a director at TEFEN Venture Consulting Italy. He is an electrical engineer graduated from La Sapienza University, Rome. TEFEN is a worldwide management consulting company providing its services to leading global companies in different industries. Oil and gas is one of TEFENâ&#x20AC;&#x2122;s worldwide practices.

PROCESS LAB ANALYZERS

Fine tune accuracy in analytic measurement—Part 1 Understanding the root causes of time delay D. NORDSTROM and T. WATERS, Swagelok Company, Solon, Ohio

rocess measurements are instantaneous, but analyzer responses are not. From the tap to the analyzer, there is always a delay. Unfortunately, this time delay is often underestimated or not accounted for or understood. Time delay in a sample system is the most common cause of inappropriate results from process analyzers. In many cases, it is invisible to operators and technicians, who are focused on the necessity of making the sample suitable for the analyzer. It is not unusual for operators to assume that the analytical measurement is instantaneous. In fact, sample systems often fail to achieve the industry standard of a one minute response. As a general rule, it’s always best to minimize time delay, even for long cycle times, but delays extending beyond the industry standard are not necessarily a problem. The process engineer determines acceptable delay times based on process dynamics. Delays become an issue when they exceed a system designer’s expectations. A poor estimate or wrong assumption about time delay will necessarily result in inferior process control. The information discussed in this article is intended to enhance the understanding of the causes of time delay and to provide the tools required to calculate or approximate a delay within a reasonable margin of error. Some recommendations for reducing time delay will also be provided. The potential for delay exists in the following sections of an analytical instrumentation (AI) system: process line, tap and probe, field station, transport line, sample conditioning system, stream switching system, and analyzer (Fig. 1). It’s important to understand that time delay is cumulative. It consists of the total amount of time it takes for fluid to travel from the latest step in the process line to the analyzer, including time required for analysis in the analyzer. For example, if the gas chromatograph takes five minutes to analyze a sample, that five minutes must be added not only to the time delay in the sampling conditioning system and stream switching system but also to time delay in the transport line, field station, tap and probe. This subtotal must be added to the amount of time it takes for the fluid to travel from the latest step in the process line to the tap. It is the total amount of time from the latest step in the process line through to the analyzer that counts.

located upstream of sources of delay, such as drums, tanks, deadlegs, stagnant lines, or redundant or obsolete equipment. Further, the tap location should provide enough pressure to deliver the sample through the transport lines or fast loop without a pump, which is expensive and introduces another variable. In many cases, the analyzer engineer, technician or operator may not be able to dictate tap location. He or she may have to make do with an existing tap location, and, often, in addition, an existing analyzer shed location. If the tap is located far from the analyzer, a fast loop is recommended to quickly deliver fluid to the analyzer. If properly designed, flow in the fast loop will be much faster than flow in the analyzer lines. To calculate time delay in the transport lines, fast loop or process line, employ the formula: Fluid velocity = volume flowrate/line volume per unit length Time delay = line length/fluid velocity TABLE 1. Volume conversions for standard-sized tubing and pipe 1

⁄8-in. tube = 1 cc/ft or 2.5 cc/m

½-in. pipe = 60 cc/ft or 200 cc/m

¼-in. tube = 5 cc/ft or 17 cc/m

¾-in. pipe = 100 cc/ft or 333 cc/m

½-in. tube = 30 cc/ft or 100 cc/m 1-in. pipe = 150 cc/ft or 500 cc/m

Supply nozzle

Fast loop ﬁlter

Stream #2 #3

Switch Condition streams sample

Process analyzer

Calibration ﬂuid Return nozzle

Process line, tap location, fast loop and transport lines. Generally, from the standpoint of time delay, it is best to

locate the tap as close to the analyzer as possible, although there are other variables to consider. For example, the tap should be

Field station

FIG. 1

Sample disposal

Basic sections of an analytical instrumentation sampling system. HYDROCARBON PROCESSING OCTOBER 2009

I 77

PROCESS LAB ANALYZERS Table 1 contains volume per unit length for standard-sized tubing and pipe. Flowrate typically is measured, not calculated. Example 1—Time delay for liquid in transport line.

Consider a transport line with a flowrate of 5 l/min through 100 ft of ½-in. tubing. Flowrate = 5 l/min or 5,000 cm3/min Line volume per ft (½-in. tubing from Table 1) = 25 cm3/ft Liquid velocity = 5,000 cm3/min/25 cm3/ft Liquid velocity = 200 ft/min Time delay = 100 ft/200 ft/min Time delay = 0.5 min or 30 sec This transport line meets the general industry specification of a one-minute response. Example 2—Time delay for gas in transport line. The

formula for calculating time delay for a gas in any section of the line contains an additional variable for pressure. Gas is compressible. A larger or smaller amount of gas can be compressed into the same amount of space. Therefore, flowrate in a fixed volume (the tubing) will change with pressure. With higher pressure comes a slower flowrate. Gas velocity = (volume flowrate/line volume per unit length) x (pressure at flowmeter*/pressure in the process line) Time delay = line length/flow speed Consider the sample pulled from a process line at 285 psig, and transported through the same transport line as described in Example 1, with the flowmeter venting to atmospheric pressure (~15 psia). Pressure must be taken in absolute pressure, not atmospheric. So, a pressure gauge that reads 285 psig must be adjusted to 300 psia. Gas velocity = (5,000 cm3/min/25 cm3/ft) x (15 psia/300 psia) Gas velocity = 200 ft/min x (1/20) Gas velocity = 10 ft/min Time delay = 100 ft/10 ft/min Time delay = 10 min This same transport line design for a gas application does not meet the one-minute goal due to the process pressure of 285 psig. To overcome this condition, a regulator must be installed at the tap location to reduce the pressure within the transport line. The regulator is set to 15 psig or 30 psia for this example. Gas velocity = (5,000 cm3/min/25 cm3/ft) x (15 psia/30 psia) Gas velocity = 200 ft/min x (1/2) Gas velocity = 100 ft/min Time delay = 100 ft/100 ft/min Time delay = 1 min The transport line is now 10 times faster with the installation of a regulator at the process tap. It now meets the one-minute response specification.

moves the fastest and provides the cleanest, most representative sample. However, it should not be any longer than necessary. In addition, the probe must be strong enough to withstand the environment within the process line. It should not be too large because time delay is directly proportional to the internal volume. In many applications, a ½-in. pipe is used. Fluid velocity in the probe cannot be measured directly but it can be calculated. It is sometimes assumed—incorrectly—that velocity in the probe is approximately the same as in the transport lines. In some cases, the difference is quite dramatic because the tubing size or pipe is different. In addition, in the case of a gas, higher pressure in the probe as compared to the transport lines means slower flow. Remember, in the case of a gas, the higher the pressure, the slower the flow. One way to speed up flow in an AI system is to lower the pressure. To calculate the time delay in a probe, first determine fluid velocity in the probe. The formula for liquids is: Fluid velocity in probe = volume flowrate in process line/volume per unit length of probe Time delay = probe length/fluid velocity in probe Example 3—Flowrate for liquid in probe. For the

transport line explained previously, consider a probe that is made from 18 in. of ½-in. schedule 80 pipe. Flowrate process line = 5 l/min = 5,000 cm3/min Probe volume per ft (½-in. pipe) = 46 cm3/ft (from Table 1) Fluid velocity probe = 5,000 cm3/min/46 cm3/ft Fluid velocity probe = 109 ft/min Time delay = 1.5 ft/109 ft/min Time delay = 0.8 sec The time delay in this probe application of less than a second is very minimal. Combined with the outcome of Example 1, the total time delay for the liquid sample is 30.8 sec, which is within the industry standard of one minute. Example 4—Flowrate for gas in probe. Many times, the gas pressure within a probe is much higher than the pressure within the transport line since it cannot be adjusted until it reaches a regulator in the field station. The formula for a gas sample in a probe is the same as for a liquid sample but with an additional variable to account for pressure. Gas velocity in probe = (volume flowrate in process line/volume per unit length of probe) x (pressure at the flowmeter**/pressure in probe***).

Using the same inputs from Example 3, the result is: Gas velocity in probe = (5,000 cm3/min / 46 cm3/ft) x (15 psia /300 psia) Gas velocity in probe = (109 ft/min) x (1/20) Gas velocity in probe = 5.45 ft/min Time delay = 1.5 ft / 5.45 ft/min Time delay = 16.5 sec

source of time delay is the probe. The larger the probe’s volume, the greater the time delay. Volume will be affected by probe length and width. The probe should be long enough to reach to the “middle third” of the process line diameter, where the stream

Using this probe in conjunction with the transport line from Example 2, response of one minute and 16.5 sec is the result with the regulator in the field station. Since the probe is before the regulator, pressure in the probe cannot be controlled. If a one-minute response is desired, a smaller probe must be employed and/or the size of the transport line must be reduced in length or diameter.

* Pressure must be taken at the same place where flowrate is measured. The flowmeter is usually positioned near the disposal.

** Flowmeter in the transport line. *** Pressure in the probe is the same as pressure in the process line.

Probe. In most analytical instrumentation systems, another

I OCTOBER 2009 HYDROCARBON PROCESSING

PROCESS LAB ANALYZERS

To analyzer

Process line Fast loop

Fast loop ﬁlter

Analyzer

To vent

Bypass loop

To process line FIG. 2

The vaporizing regulator is located after the fast-loop filter. A second liquid fast loop eliminates the long delay that normally occurs on the liquid side of the vaporizing regulator.

Stream 4 FIG. 3

Stream 3

Inlet

Stream 2

Stream 1

A cascading DBB configuration eliminates deadlegs by enabling the fluid stream to pass through the second block valve of the adjacent stream or streams.

Stream 1

Field station. In the case of gas, a field station is employed as

a means of reducing pressure in the transport lines or fast loop. Time delay in the transport lines is reduced in direct proportion to the reduction in absolute pressure. At half the pressure, the result is half the time delay. The field station is located as close to the tap as possible. The sooner the pressure is dropped, the better. With a liquid sample, a regulator in the field station is not employed. It is better to keep liquids at high pressure to avoid the formation of bubbles. If a liquid sample is analyzed as a gas, a vaporizing regulator may be used at the field station. A vaporizing regulator will cause considerable time delay. As the fluid changes from liquid to gas, volume will increase dramatically. The rate of increase will depend on the liquid’s molecular weight. Typically, the measured vapor flow after the regulator will be >300 times the liquid flow before the vaporizing regulator. For example, with a vapor flow of 500 cm3/min, the liquid flow may be less than 2 cm3/min. Therefore, the liquid will take 25 minutes to travel through 10 ft of ¼-in. tubing. To reduce this time, we must reduce the tube volume that precedes the regulator. For example, with 1 ft of ⅛-in. tubing, it would only take 30 sec for the liquid to reach the regulator. However, the time delay in the probe must be added. A narrower probe offers a faster response. Another means of attaining a faster response is to place the regulator closer to the analyzer location. In Fig. 2, the regulator is located after the fast-loop filter with a second liquid fast loop ensuring that good liquid flow continues to the vaporizing regulator. The objective is to minimize slow-moving liquid volume going to a vaporizing regulator. Stream watching. From a time-delay perspective, stream

switching assemblies must work fast, quickly purging old sample material while moving the new stream to the analyzer. Double block-and-bleed (DBB) valve configurations, which are available in conventional components or miniature, modular designs, provide a means of switching streams with minimal deadlegs and cross-stream contamination from leaking valves. A traditional DBB configuration is the cascading DBB (Fig. 3), which eliminates deadlegs by enabling the fluid stream to pass through the second block valve of the adjacent stream or streams. Deadlegs following the second block valve are purged each time the stream switches. One problem with the DBB cas-

To analyzer

Stream 2

Stream 3

To vent FIG. 4

The DBB configuration with an integrated flow loop improves on the cascading DBB configuration by providing consistent pressure drop for all fluid streams and consistent delivery times.

cading configuration concerns the tortuous flow path which leads to pressure drop and slower flow. Pressure drop may be estimated by looking up the product’s Cv, which measures the resistance to flow. The lower the Cv, the greater the pressure drops, resulting in a lower flowrate. In the DBB cascading configuration, the primary stream— Stream 1 in Fig. 3—does not cause excessive pressure drop. But, Stream 2, Stream 3 and so on create increasing amounts of pressure drop, resulting in much longer travel times to the outlet due to the lower flowrates. The result is inconsistent delivery times from the different streams, making it difficult to set consistent purge times and analysis times for all streams. The DBB configuration with an integrated flow loop (Fig. 4) enables all the advantages of the DBB cascading configuration while ensuring minimal pressure drop consistently across all streams. The Cv for each stream—and, therefore, the delivery time for each stream—will be the same. Converting Cv to an estimated time delay is a complex process, requiring a computer program or physical product testing. In many cases, it may be enough to shop for components with the highest Cv. A component with a Cv of 0.3 will cause one-third the pressure drop of one with a Cv of 0.1. HYDROCARBON PROCESSING OCTOBER 2009

I 79

PROCESS LAB ANALYZERS Other components employed in the sampling conditioning systems, such as filters, knock-out pots and coalescing filters, may cause significant time delays because they allow incoming samples to mix with old samples. To clear out a filter or knock-out pot to where 95% of the old sample is gone requires three times the volume of the component. That’s assuming that the inlet and outlet are adjacent, as illustrated in Fig. 5. Consider a filter with an inlet and outlet configured as in Fig. 5. If the flowrate is 100 cm3/min and the filter’s volume is 100 cm3, it will take three minutes to ensure that 95% of the old sample has been flushed out. To ensure an accurate sample, three minutes must be added to the time-delay calculation for this AI system. These same formulas may be applied to mixing volumes in the process line.

Mixing volume with adjacent inlet and outlet.

FIG. 5

Sampling conditioning system. The sampling conditioning

system prepares the sample for analysis by filtering it, by ensuring it is in the right phase, and by adjusting pressure, flow and temperature. The components employed are numerous, including gauges, regulators, variable area flowmeters, flow controllers, check valves, control valves and ball valves. These are relatively small components. Frequently, miniature modular components are employed. These are top-mounted components manufactured to the ANSI/ISA 76.00.02 standard, according to the New Sampling/Sensor Initiative (NeSSI). Similar to stream-switching valves, the critical matter here is not internal volume so much as pressure drop. When choosing components compare the Cv provided by the manufacturer.

Hydrogen is the future, we can sense it!™

PROCESS HYDROGEN ANALYZING SYSTEMS

Improve Process Control and Enhance Efﬁciency

Analyzer. As a rule of thumb, samples will take five to 10 minutes to travel through a gas chromatograph. Infrared and ultraviolet analyzers work much faster, completing analyses within seconds. The amount of time required for the analyzer to process a sample should be known to the operator, technician or engineer. That time is added to the estimates discussed previously for the total time delay from the tap through the analyzer. The total time delay as calculated with the tools described should provide an estimate within a reasonable margin of error. Remember, the total time from the latest step in the process line to the analyzer is what matters, and all the components making up this distance must also be added to the total. What was discussed previously, should alert operators to any incorrect assumptions about the sample time, particularly concerning typical trouble spots, such as the probe or a vaporizing regulator in the field station. It should enable operators, in collaboration with their fluid system provider or consultant, to make intelligent choices about components and configurations as concerns location of the tap, fast-loop set up, appropriate tubing diameters, and stream switching configurations. Time delay is an issue that deserves the operator’s close scrutiny. Easy or unexamined assumptions will undermine the operator’s hard work and render the expensive analyzer itself useless. HP Next month: Part 2. Calibration is an important process. Information on proper calibration will include system design, limitations, validation and controlling atmospheric changes.

ive effect Costall t s in , to buy in ! a t in a and m

Doug Nordstrom is the marketing manager for analytical

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t t

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t t

CO & H2S tolerant

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Select 169 at www.HydrocarbonProcessing.com/RS 80

instrumentation for Swagelok Company, focusing his efforts on advancing the company’s involvement in sample-handling systems. He previously worked in new product development for Swagelok and earned a number of Swagelok patents in products including the SSV and MPC. Mr. Nordstrom graduated with a BS degree in mechanical engineering from Case Western Reserve University and an MS degree in business administration from Kent State University.

• • •

Tony Waters has 45 years of experience with process analyzers and their sampling systems. He has worked in engineering and marketing roles for an analyzer manufacturer, an end user and a systems integrator. Mr. Waters founded three companies that provide specialized analyzer services to the process industries and is also an expert in the application of process analyzers in refineries and chemical plants. He is particularly well known for presenting process analyzer training courses in Asia, Europe and the Middle East, as well as North and South America.

PROCESS DEVELOPMENTS

Consider advanced multi-promoted catalysts to optimize reformers Improved catalyst systems strike a new balance to increase yields with greater selectivity for end-products P.-Y. LE GOFF, Axens, Rueil-Malmaison, France

atalytic reforming accounts for a large share (28%) of the worldâ&#x20AC;&#x2122;s gasoline production. It is the most important source of aromatics for the petrochemical industry. Reforming is also a major source of refinery-based hydrogen, for which demand is growing rapidly due to escalating hydrotreatment needs. Although the state of the art has advanced remarkably improvements in catalyst selectivity, activity and stability have significant impact on refinery economics. The incentive to improve reforming catalyst formulations is as high as ever. Catalyst development continues to be a major activity among catalyst suppliers.

Ways to improve catalyst performance. As the difference between feed cost and product value has enormous leverage in continuous catalyst regeneration (CCR) reformer economics, improvements in catalyst selectivity to produce better yields greatly enhance the unitâ&#x20AC;&#x2122;s profitability. Two parameters can be controlled for better selectivity: catalyst-chloride content and promoter-metal interaction. Chloride content. Controlling the chloride content is a function carried out in the operating unit. Lowering the chlorine content reduces the effect of the catalystâ&#x20AC;&#x2122;s acid-site function. In doing so, both liquefied petroleum gas (LPG) production and corrosion in the unit are reduced. For a chloride content reduction of 0.1 wt%, an empirical increase of 0.3 wt% in C5+ product is expected. This said, a minimum chloride content of 0.8 wt% is needed to stabilize the catalyst and, especially in the case of CCR reforming catalysts, to catalyze the extension of alkylcyclopentane rings to cyclohexane or alkylcyclohexane rings. The reaction paths are representative of the desired and undesirable results (see Fig. 1). A deficit in acid-site function produces a full metallic mechanism (path 1) resulting in an alkylcyclopentadiene, which forms coke. As the catalyst becomes fouled with coke, a vicious cycle follows: catalyst activity diminishes and higher operating temperatures are required to maintain conversion. The higher temperatures impede catalyst selectivity, resulting in poorer yields, and the catalyst must be regenerated more often. If regenerator capacity is attained, the regenerator becomes a bottleneck. The second and favored reaction pathway shows that, with optimum chloride content on the catalyst, cyclohexane or alkylcyclohexane is formed, preferably to the alkylcyclopentadiene. This allows ring dehydrogenation to the desired aromatic compound. Coke production is minimized as the aromatic is more stable than the alkylcyclopentadiene.

Research and development efforts to improve catalyst selectivity and activity by changing the promoter-metal interaction without compromising stability were successfully achieved. Thus, a new CCR catalyst generation has been developed comprising of a high-density family and a low-density family. These new promoters were chosen to reduce the coke precursor interactions and polyaromatics adsorption on the metallic cluster sites,1 while reducing the hydrogenolysis activity of platinum. High-density family. Results from bench-scale and micropilot plant testing indicated that adding a single promoter such as germanium was not enough to provide the targeted high selectivity and stability. Indeed, the selectivity enhancement attributed to the new promoter in the bi-promoted catalyst was offset by diminished stability. Tuning the dehydrocyclization and dehydrogenation activities was inseparable from degradation in coking activity. It was found that the new CCR catalysts benefited significantly from using a combination of different promoters, which controlled the increase in the activity for the desired reactions while minimizing coke precursor formation. Figs. 2 and 3 illustrate these findings. The bi-promoted catalyst (in blue) did not maintain the same selectivity increase over the previous generation catalyst (in black) as that obtained within the first few hours of the test (Fig. 2). The new catalyst (in burgundy) showed it was stable relative to that of the previous generation.

Coke (1)

M M A R

M = metallic site A = acidic site R = H or alkyl FIG. 1

R M

(2)

Alkylcyclopentane reaction paths for metallic and acidic sites. HYDROCARBON PROCESSING OCTOBER 2009

I 81

PROCESS DEVELOPMENTS 3.0

+ 3.0 + 2.5

New CCR catalyst

New generation CCR catalyst

+ 2.0

C5+ yield, wt%

C5+, wt%

2.0

+ 1.5

+ 1.0

1.0 Previous generation

Old CCR catalyst 25

+ 0.5

Bi-promoted only 20 FIG. 2

80 100 Time, hours

120

140

160

Relative C5+ yield, wt% and stability are improved compared with the previous generation and the bi-promoted catalyst.

FIG. 4

65 85 Time, hours

105

125

Relative C5+ yields for low-density catalysts with respect to time.

0.4 New CCR catalyst 0.3 H2, wt%

Relative reactor temperature, °C

60 0.2

Bi-promoted only

0.1

40 New 30 generation

Previous generation

20 FIG. 5 10 0 20

100

120

140

160

RON FIG. 3

Long-term stability of the new generation catalyst is improved compared with that of the bi-promoted catalyst.

TABLE 1. Comparison of CCR catalyst to previous generation and bi-promoted catalysts Yields, wt%

Previous generation

Bi-promoted only

New generation

Hydrogen

Base

Base + 0.1

Base + 0.1 Base + 0.8

Base

Base + 0.5

Aromatics

Base

Base + 0.6

Base + 0.7

Temp

Base (120 h)

Base + 20°C (100 h)

Base (120 h)

Coke

Base

+ 10%

–20%

Pressure = 3.5 barg, RON = 104, WHSV = 2.5/h, H2/HC = 4 mol/mol

The tests were performed in a multi-reactor unit allowing simultaneous testing of four catalysts in small amounts under realistic operating conditions. The effluent analysis was monitored by online gas chromatography, from which product yields and the research octane number (RON) were calculated. Tests were conducted at 3.5 barg, constant WHSV and a RON of 104. The temperature was automatically adjusted to maintain the targeted RON. The C5+ components yield is reported in Fig. 2 as a function of time while activity is represented by the temperature increase needed to maintain RON at 104 (Fig. 3). Combining the effect of different promoters in the new catalyst does not alter the C5+ yield obtained with the bi-promoted catalyst but provides 82

I OCTOBER 2009 HYDROCARBON PROCESSING

Old CCR catalyst

65 85 Time, hours

105

125

Relative hydrogen yields for low-density catalysts with respect to time.

extended stability as illustrated in Fig. 3. Table 1 summarizes the performance of these catalysts. The higher stability while maintaining activity was achieved with a significant reduction in platinum (Pt) inventory. Moreover, as seen in Table 1, the new catalyst produces less coke. This gives the refiner added flexibility to consider processing thermally cracked naphthas, such as coker naphtha or when units are operated at higher severity: higher feedrates, lower recycle ratios or higher octane numbers. Thermally cracked naphthas are known to produce higher amounts of coke than straight-run (SR) naphtha. For a unit designed to process SR naphtha as a feed, the reduced coke production will allow a refiner to treat thermally cracked naphtha without having to revamp the regenerator’s coke burning capacity, as shown in Table 1. Given the expected gains in either aromatics or C5+ yields, and the different hydrogen partial pressures corresponding to operating pressure, four high-density catalysts were developed for the new CCR family.2 Low-density family. Based on the results obtained with the high-density family, a new multi-promoted catalyst on a lowdensity carrier was developed. Figs. 4 and 5 show that the multipromoted low-density CCR catalyst outperforms the industrially proven CCR catalyst. Tested using same system as that used for the high-density family, conditions for the new multi-promoted CCR catalyst and previous-generation CCR catalysts were: 8 barg, H2/HC = 3 mol/mol, WHSV = 2.6 h–1, RON = 100. The choice of multi-promoter doping for the low-density catalysts was again guided by the quest for higher C5+ and H2 yields.

Select 106 at www.HydrocarbonProcessing.com/RS

PROCESS DEVELOPMENTS +2.5 0.75 wt% chloride content + 2.0

50 0.75 wt% chloride content

C5+ yield, wt%

Relative temperature, °C

+ 1.5

1 wt% chloride content

+ 1.0

20 + 0.5

1 wt% chloride content

10 0

0 0

40 60 Time, hours

FIG. 7 FIG. 6

100

The low-chloride content of the catalyst negatively impacts the catalyst stability.

40 60 Time, hours

100

The low-chloride catalyst shows a gain in selectivity initially but after the selectivity diminishes significantly over time.

As pilot testing was performed at compa- TABLE 2. Product values for new The promoters have also shown effects rable chloride contents, the higher yield is not and old generation CCR catalysts, $/ton on support acidity, as they bring their own linked to chloride effects. The weight percent acidity or basicity. Controlling the support 2,100 yield gains of 1.5 for C5+ and 0.16 for H2 H2 acidity helped to inhibit cracking reactions C5+ 520 yields are linked directly to the doping. that are responsible for the lack of selectivity The high-density and low-density cata- Fuel gas on liquid effluents. 300 lyst performance is explained mainly by a LPG 450 Activity adjustment. It is well known modification of the Pt particle electronic that varying the catalyst’s chloride content density, which was confirmed by infrared will change, for a given set of operating conditions, its yields and (IR) spectroscopy. This affects the adsorption energies of the activity. As a rule of thumb, a variation of 0.1 wt % chloride content different compounds on the particles—in particular those of on the catalyst will change the activity by 2°C and change the C5+ aromatic compounds, leading to a change in selectivity.

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PROCESS DEVELOPMENTS yield by 0.3 wt%–0.4 wt %. When seeking high yields, such an approach has intrinsic limitations. Too-low chloride levels strongly diminish catalyst stability as the ring extension of the alkylcyclopentane to an alkylcyclohexane is not efficient. Two catalysts with different chloride loadings were tested in the same unit as previously described. The test conditions are 3.5 barg, RON = 104, WHSV = 2.5/h, H2/HC = 4. The temperature increase needed to maintain a constant RON of 104 as a function of time is shown in Fig. 6. The low-chloride content curve shows that the catalyst’s activity reduces (higher reactor temperature required) while its stability diminishes with time as compared to the higher-chloride content catalyst (red curve). The low-chloride-content catalyst presents a gain in C5+ yields at shorter onstream times as shown in Fig. 7. This is explained by the low acidity on the surface that favors reforming reactions over acidic cracking. However, the low-chloride catalyst lacks stability; consequently, yield improvements do not last. With the new catalyst for which carrier acidity has been optimized, the trade-off between activity and selectivity was changed. A new set of empirical rules was established after testing the new CCR catalyst family with different chloride loadings. For a variation of 0.1 wt% chloride content on the catalyst, the activity will change by 4°C to 5°C, while the C5+ yield will only change by 0.15 wt%. Therefore, these new catalysts offer greater flexibility to the refinery seeking activity improvement but with almost no reduction in selectivity. Moreover, as the hydrothermal stability of the carrier was drastically improved, the high initial chloride retention will be maintained throughout regeneration.

carrying out extensive pilot testing, new multi-promoted CCR catalyst families were developed.2 The main benefits of these new formulations are: higher yields at constant chloride content with higher profitability; greater stability that enables more flexibility for operations and lower operating costs; lower Pt inventory; and reduced utility consumption due to lower recycle ratios and higher chloride retention. The low-coke production tendency of these new catalysts is a key factor when considering the changing nature of reformer feeds. For example, the amount of coke produced from thermally cracked naphtha is higher than that from SR naphtha. Considering the large number of new coker projects underway, using multipromoted catalysts will allow existing CCR reformers to accept these new feeds without modifying the regenerator design. HP 1 2

LITERATURE CITED Goda, A. M., M. Neurock, M. A. Barteau and J. G. Chen, Surface Science, Vol. 602, pp. 2513–2523, 2008. www.axens.net—AR & CR Series Catalysts.

Pierre-Yves Le Goff is Axens’ senior technical manager for reforming and aromizing replacement catalysts. He is also project leader in the development of reforming catalysts in conjunction with IFP. Dr. Le Goff started his professional career as a research engineer at Rhodia where he worked mainly in the field of inorganic chemistry, specializing in catalyst support design. He was also involved in process development. Dr. Le Goff holds an engineering degree from the Ecole de Chimie de Mulhouse, an MBA from Université de la Sorbonne in Paris, and a PhD from the Université de Haute Alsace.

Economics. Based on these improvements, comparative economic studies were done to determine the profit one can expect using the new CCR catalyst instead of the previous catalyst generation. One such study was based on a 40,000-bpsd CCR reformer having a total catalyst inventory of 120 tons. The assumptions were that the investment, feedrate and recycle ratio were identical for both catalyst systems. Therefore, the operating cost differences between the new and older CCR catalysts are equal, and the main cost difference between the catalyst systems was due to the lower amount of Pt. For the C5+ product, the higher H2 and C5+ yields were taken into account together with the lower fuel gas and LPG production. Table 2 lists the product values. The differential profitability (Δprofit) is discounted over a seven-year period using Eq. 1:

profit Discounted =

(Profit per year)

year =1

(1+ i / 100) year

(1)

Where: C1

Difference in platinum costs between initial catalyst load and replacement catalyst i Discount rate; 10% N Number of years taken into account. With these assumptions, the discounted profit was found to be almost $21 million higher using the new CCR catalysts, or almost $2 more per ton of feed. The payback time for the new CCR catalyst is estimated to be less than one year even without taking into account the savings received from its lower Pt content. Catalyst options. Based on a better understanding of the

links between catalyst properties and performance, and after Select 168 at www.HydrocarbonProcessing.com/RS 85

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HYDROCARBON PROCESSING OCTOBER 2009

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Bill Wageneck, Publisher 2 Greenway Plaza, Suite 1020 Houston, Texas, 77046 USA P.O. Box 2608 Houston, Texas 77252-2608 USA Phone: +1 (713) 529-4301, Fax: +1 (713) 520-4433 E-mail: Bill.Wageneck@GulfPub.com www.HydrocarbonProcessing.com

SALES OFFICES—NORTH AMERICA IL, LA, MO, OK, TX Josh Mayer 5930 Royal Lane, Suite 201, Dallas, TX 75230 Phone: +1 (972) 816-6745, Fax: +1 (972) 767-4442 E-mail: Josh.Mayer@GulfPub.com

AK, AL, AR, AZ, CA, CO, FL, GA, HI, IA, ID, IN, KS, KY, MI, MN, MS, MT, ND, NE, NM, NV, OR, SD, TN, TX, UT, WA, WI, WY, WESTERN CANADA Laura Kane 2 Greenway Plaza, Suite 1020, Houston, Texas, 77046 Phone: +1 (713) 520-4449, Fax: +1 (713) 520-4459 E-mail: Laura.Kane@GulfPub.com

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DATA PRODUCTS AND CLASSIFIED SALES Lee Nichols

SALES OFFICES—EUROPE

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FRANCE, GREECE, NORTH AFRICA, MIDDLE EAST, SPAIN, PORTUGAL, SOUTHERN BELGIUM, LUXEMBOURG, SWITZERLAND, GERMANY, AUSTRIA, TURKEY Catherine Watkins 30 rue Paul Vaillant Couturier 78114 Magny-les-Hameaux, France Tél.: +33 (0)1 30 47 92 51, Fax: +33 (0)1 30 47 92 40 E-mail: Watkins@GulfPub.com

AUSTRALIA – Perth Brian Arnold Phone: +61 (8) 9332-9839, Fax: +61 (8) 9313-6442 E-mail: Australia@GulfPub.com

ITALY, EASTERN EUROPE Fabio Potestá Mediapoint & Communications SRL Corte Lambruschini - Corso Buenos Aires, 8 5° Piano - Interno 7 16129 Genova - Italy Phone: +39 (010) 570-4948, Fax: +39 (010) 553-0088 E-mail: Fabio.Potesta@GulfPub.com RUSSIA/FSU Lilia Fedotova Anik International & Co. Ltd. 10/2 Build. 1,B. Kharitonyevskii Lane 103062 Moscow, Russia Phone: +7 (495) 628-10-333 E-mail: Lilia.Fedotova@GulfPub.com UNITED KINGDOM/SCANDINAVIA, NORTHERN BELGIUM, THE NETHERLANDS Peter Gilmore 57 Keyes House Dolphin Square London SW1V 3NA United Kingdom Phone: +44 (0) 20 7834 5559, Fax: +44 (0) 20 7834 0600 E-mail: Peter.Gilmore@GulfPub.com

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REPRINTS Phone: +1 (713) 525-4633 E-mail: EditorialReprints@GulfPub.com

HPI MARKETPLACE EQUIPMENT

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FREE Product and Service Information — OCTOBER 2009 HOW TO USE THE INDEX: The FIRST NUMBER after the company name is the page on which an This information must be proadvertisement appears. The SECOND NUMBER, appearing in parentheses, after the company name, vided to process your request: is the READER SERVICE NUMBER. There are several ways readers can obtain information: PRIMARY DIVISION OF INDUSTRY 1. The quickest way to request information from an advertiser or about an editorial item is to go to www. HydrocarbonProcessing.com/RS. If you follow the instructions on the screen your request will be forwarded for immediate action. 2. Go online to the advertiser's Website listed below. 3. Circle the Reader Service Number below and fax this page to +1 (416) 620-9790. Include your name, company, complete address, phone number, fax number and e-mail address, and check the box on the right for your division of industry and job title. Name ________________________________________________________

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䊐-Company Official, Manager 䊐-Engineer or Consultant 䊐-Supt. or Asst. 䊐-Foreman or Asst. 䊐-Chemist 䊐-Purchasing Agt.

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Page

RS#

Company Website

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International Exposition Co . . . . . . .71

Linde Process Plants . . . . . . . . . . . .22

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KTI Corporation . . . . . . . . . . . . . . . .53

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HP Marketplace . . . . . . . . . . . . . 86-88

KBC Advanced Technologies Inc . . . .14

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KTI Corporation . . . . . . . . . . . . . . . .50

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(92)

MBI Leasing LLC . . . . . . . . . . . . . . .20 (100) MBI Global . . . . . . . . . . . . . . . . . . .20

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www.info.hotims.com/27223-113

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Circulation . . . . . . . . . . . . . . . . . .66

ARC's Collaborative Mfg . . . . . . . . .83 (106)

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Altair Strickland. . . . . . . . . . . . . . . .56 (109)

Axens . . . . . . . . . . . . . . . . . . . . . . .92

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URS . . . . . . . . . . . . . . . . . . . . . . . .24 (108) www.info.hotims.com/27223-108

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HPIN AUTOMATION SAFETY JOHN CUSIMANO, GUEST COLUMNIST jcusimano@exida.com

Integrating security into the safety lifecycle Thanks to IEC 61511 (ISA S84.00.01-2004), many companies have come a long way in adopting safety system design best practices. Safety concepts promoted by this standard and safety lifecycle management tools provided by suppliers have aided users in following a very systematic approach to addressing safety. In short, industry has made major strides in adopting a disciplined approach to identifying and mitigating safety risks in their facilities. Control-system cyber security: A new threat. Unfor-

tunately, we are now faced with a “new” threat to our facilities. There is mounting concern that industrial automation and control systems could be intentionally attacked and disabled or manipulated via “cyber” connections. Government officials are particularly concerned about industries considered part of our “critical infrastructure” such as the HPI, and they view control-system cyber vulnerabilities as a threat to national security. These disruptions could be nuisance trips (e.g., blackouts) or worse, an event like the one depicted in US television show’s episode 7 of “24”. Because it was developed before cyber security was a concern, IEC 61511 does not provide much guidance on this topic. However, the safety lifecycle methodology, with some adaptation, can be extended to address cyber security threats. After all, the safety lifecycle process was designed to systematically identify and to quantify risk; to evaluate effectiveness of existing safeguards; to design and validate solutions to close the gap between the identified risk and the corporation’s tolerable risk; and to put measures in place to safely operate and maintain the system for its lifetime. Isn’t that exactly what we want to do with cyber security? Using the safety lifecycle for cyber-security. So, in theory, we should be able to borrow best practices and concepts from the safety domain and apply them to the security domain. The challenge is to: • Understand the differences between safety and security • Respond by adding appropriate steps to the lifecycle that properly identify security threats • Evaluate the effectiveness of countermeasures • Implement effective ongoing security management.

analysis. In this step, the goal is to identify “the bad things that could happen,” “how bad it would be if it were to happen” and “how it could happen.” In the language of hazard and operability (HAZOP) analysis, these are deviations, consequences and causes. I recommend considering control-system failures, including security failures during the HAZOP, but not taking the time during the HAZOP to dive into details. For example, if a deviation could be caused by a control-system failure, simply write down something like “control system failure” and, if possible, the failure type as one of the causes, and then move on. Each of these causes can be further analyzed by the appropriate team of experts in a later phase of the lifecycle. Suppose the team identified the deviation “no agitation” when performing a HAZOP on a chemical reactor. There are a number of possible causes for an agitator to fail, such as mechanical failures, loss of power, motor/drive failures or control-system failures. Specifically, the control system would fail in a manner in which it turned off the agitator. Of course, there are a number of failures that could do this, such as software, hardware and security failures. These need to be further analyzed, but not during the HAZOP review. The HAZOP team should record something like “control output failure” and proceed to the next deviation. A variety of techniques can be used to further analyze the control-system failures identified in the HAZOP including failure modes and effects analysis (FMEA) and control HAZOP (CHAZOP). These methods, along with threat modeling, can and should also be used to further analyze security failures as well. I believe the key to an effective and efficient process is to treat control-system security failures much like any other potential control-system failure. Once they are identified, they can be analyzed in a systematic manner to determine how they could happen and also to determine if there are safeguards in place to prevent them from occurring or to mitigate the situation should they occur. There is far more involved in integrating security into the safety lifecycle than can be covered in this column. But, hopefully, I got you thinking a little about the possibilities and benefits of looking at them concurrently. HP

Differences between safety and security. While there

are many similarities, there are also some major differences between safety and security. One of the biggest is that safety analysis does not generally take into account malicious intent. For example, we don’t typically consider sabotage when performing a failure analysis on a piece of equipment. However, there are well-known techniques for evaluating security threats, such as threat modeling, that simply need to be worked into the overall process. An integrated safety and security lifecycle model should start with the same first step as the safety lifecycle – hazard 90

I OCTOBER 2009 HYDROCARBON PROCESSING

The author is director of exida’s security services division. A process automation safety systems expert with more than 20 years of experience, he leads a team devoted to improving the security of control systems for companies worldwide. Prior to joining exida, he led market development for Siemens’ process automation and safety products and held various product marketing positions at Moore Products Co. Mr. Cusimano started his career at Eastman Kodak Co., where he implemented and managed automation projects. He has a BS degree in electrical and computer engineering from Clarkson University. Mr. Cusimano holds a CFSE certification.

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