Monitor, target, report

Ronauld Weeks, Honeywell Connected Industrial, outlines best practices for energy management in today’s connected process industry.

Almost all process industry facilities have begun to engage in energy management initiatives, yet sometimes the benefi ts from these initiatives can erode as fast as they are achieved.

This is primarily because things change, whether that is in the process, systems, fi delity of rigorous simulation models used, or aged assets. As a result, a degree of maintenance is required to maintain rigorous simulation models.

Different methods and technologies are important in decreasing the total cost of ownership and keeping energy management initiatives relevant. So why does energy performance monitoring, targeting and reporting have a higher propensity to fail in the process industries compared to other industries, such as building management?

Experts believe it is typically down to the process industries taking a complex approach to what is essentially a simple but repetitive task. In addition, chemical and petroleum engineers tend to take a rigorous simulation and precisely theoretical approach to solving these problems. These approaches were necessary when the world was not connected by sensors, and the need for data via simulated values, in the absence of real-time data, was necessary to supplement this knowledge gap. However, times have

changed. Workers are now 80 – 99% connected to their assets, and the remainder is gradually being added via new advances.

Honeywell persists in pushing rigorous simulation as the fi rst tool in its energy monitoring, targeting and reporting strategy. The company believes that it is time to leverage methods and tools that are easier to implement and maintain, and which provide the information needed to make faster, fl exible and more accurate directional decisions/actions.

Keep it simple and grow

For most pacesetter facilities addressing energy management, keeping it simple and being fl exible is key. It is possible to be nimble in a few steps: Collect data vigorously (historian and other data sources). Contextualise vehemently (common asset model). Cleanse and validate data (data scrubbing and quality). Collect events/modes that affect process behaviour (shifts, operating modes, range controllers, plans/workflows, etc). Utilise a surveillance engine which can exploit multiple methods of analysis, has an extensive equipment library for rotating and static equipment, and which can leverage templates to decrease total cost of ownership on equipment models-graphics-dashboards-calculations and can deploy fault models/notification workflows (analyse). Connect seamlessly to data sources using service oriented architectures (standards based connectivity/exchange). Utilise standards based reporting services (reporting services).

Figure 1. Five-step data processing model.

The rationale

Energy management is a major pillar in many process industry operations excellence strategies. The management of energy information is the most important component of any effective, continuous improvement energy management initiative. It is widely accepted by best-in-class facilities that monitoring, targeting and reporting (MT&R) alone on energy usage can lead to signifi cant energy reductions. Practical working knowledge and techniques such as MT&R – which includes computational methods of correlations, best operation base lining, and data mining and analytics on key drivers such as production, and uncontrollable variations such as weather – are the most effective and show greater longevity than rigorous simulation which erode as systems, conditions, personnel attrition and expertise retirement ensues.

Therefore, the question becomes: why is looking back, predicting conditions from historical information and utilising smaller, more focused, equipment-based simulation models so much more effective? The answer lies in the fact that energy used by any business varies as production processes do, volumes change, equipment ages and inputs vary. Determining the relationship of energy used, or which should be used, to key performance indicators (KPIs) allows facilities to do the following: Know how much better or worse they are compared to before. Understand energy trends which are seasonal in nature and operations cycles/modes effects, etc (this is in contrast to a theoretical rigorous simulation which normally does not account for weather, operation cycles/modes, or whether a standby pump or exchanger is running). Understand equipment residual life effect on total energy usage. Compare analyses and benchmark similar facilities. Identify and filter historically-reactive operation decisions which have a large effect on energy usage. Develop more insightful energy usage measures which have profound effects on a facility energy intensity index.

The strategy to long-term energy MT&R success lies in keeping it simple and evolving an energy accounting and auditing strategy towards a simple ‘measure, analyse and adjustment’ methodology.

It is important to start by understanding operations based on past data. It is therefore advisable to: Alert and notify for action (i.e. energy deviations, equipment health, precursive conditions) and investigate how this compares historically and based on the plan; not the theoretical optimum initially. If one is running to the plan of the business, the planners and field development should have optimised to the available constraints of one’s operations already.

Run large simulation optimisations offline whenever possible (at all times resist putting these online, as maintenance failure is common); these normally should only be used to support/validate comparative analysis of real time deviations and KPIs intermittently. This is an offline internal auditing exercise. Calculate KPIs, chart, track and report. This is 95% of the process then complete, at more than half the cost it would have taken using a rigorous simulation approach to energy management.

Solutions architecture should look simple. One should think about taking transformative steps rather than a ‘big bang’.

Figure 2. Energy business process flow.

Be guided by standards

An energy management (MT&R) initiative should support ISA95-Operations Performance implemented using a requirements decomposition similar to the one below on all relevant energy business processes. Furthermore, efforts in energy management should be guided by ISO 50001 and its associated audit standards for energy. In general, it is advisable to keep the following with respect to ISO 50001 in mind: ISO 50001 requires an organisation to monitor, measure and analyse the key characteristics of its operations that determine energy performance at planned intervals. Equipment used in monitoring and measurement of key characteristics should be calibrated to ensure data are accurate and repeatable. ISO 50001 requires an organisation to establish an energy baseline(s) for the measurement of the energy performance.

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Figure 3. Process optimisation at work.

Option 1 is the ‘Manual Benchmark of Energy’ performance baseline: the strategy outlined in this technology suggests that the user should apply a baseline on an asset, unit or plant by specifying a start and end time to consider as a reference. Use a baseline period and regression techniques to generate a target energy model. Monitor current performance along with baseline time period performance to compare and understand deviation. Option 2 is the ‘Use Design Benchmark’ model: the strategy outlined in this technology note recognises the need for rigorous equipment simulation on a supportive and smaller basis, but suggests leaving large plant- or field-wide simulations to a task conducted offline (or specifically on the supply side, not the demand side) and for intermittent auditing purposes if warranted. ISO 50001 requires an organisation to identify appropriate energy performance indicators to monitor and measure its energy performance.

Conclusion

Honeywell’s solution can help to improve maintenance lead time. The company’s monitoring approach leverages multiple methods with both principle-based effi ciency models and predictive analytics. This provides a comprehensive view of the asset’s performance rather than just relying on pure machine learning models. In turn, this can improve decision quality and increase lead time by 2 – 3 times, implementing the right action before a potential failure can cause downtime and secondary equipment damage

The solution also helps its customers as it can be deployed effi ciently, accelerating time to value. It leverages asset predictive analytics and performance digital twins, when compared with in-house or pure analytics based solutions.

Analytics models and workfl ows and asset library is built-in, thereby lowering implementation effort by 3000 man-hours on average, which is equivalent to approximately US$500 000 in a typical application of over 1000 modelled assets

The solution has also resulted in reduced OPEX via improved asset maintenance and performance optimisation. In addition to assisting with reliability-based maintenance programmes, the solution also enables performance and process optimisation using the same offering.

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