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imulation tools are used extensively in a wide range of activities, from plant design, unit revamps and troubleshooting, to plant optimisation and unit monitoring. Confidence and trust in a simulator are usually gained by running simulations against measured plant-performance data. But what happens when measurement and simulation do not align? Can we learn anything from such experiences? In the first installment of this two part article, two case studies are used as examples, each one initiated by disparity between simulation and plant data too large to be attributed to just modeling and measurement errors. Examples are defective equipment and contaminated solvents. In the second part of this article, poor temperature control, and a problem with the model itself will be discussed. Each case compares simulation results with measured performance metrics, and each one uses a combination of data and simulation to deduce logically a diagnosis and to resolve the issue. In each case there is some gross deficiency either in a measured parameter, in the integrity of one or more pieces of equipment, in faulty or overly-simplistic thinking, or in the simulator itself.
Case study 1 Following an internals and solvent change out, a pair of fuel gas treaters in a US West Coast refinery were unable to reach the treated gas H2S level that the simulator they were using (modified ideal stages) said should be achieved. The sulfur emissions from the units were on the cusp of exceeding the permit limit, meaning that corrective action was imperative. A consultant was approached for advice. Because the two treaters are so similar, this article will focus on only one of them.
The treater originally used trays to treat the gas using diethanolamine (DEA) which resulted in gas comfortably below 4 ppmv H2S. To improve throughput, trays were replaced with random packing (#2 Minirings) which lowered pressure drop and increased tower capacity. To take advantage of its lower required energy for solvent regeneration, N-methyldiethanolamine (MDEA) was substituted for DEA. Simulation of post-change-out conditions showed that both treaters were capable of producing treated gas below 1 ppmv of H2S. However, performance tests after the revamp showed the treater was actually achieving only 26 ppmv H2S – far higher than expected – and contributing to the plant now pushing emissions limits. The consultant surmised that the liquid residence time on the packing was not nearly long enough to achieve treat, and he made the recommendation to put the original 17 trays back into the column. When the switch back was made, a repeat of the performance test surprisingly showed that column performance was unchanged – it was still producing 26 ppmv H2S in the treated gas. The reason this happened is explained below. When the trayed and packed cases were run in Optimized Gas Treating’s (OMT) ProTreat® mass transfer rate-based simulator using MDEA, the predicted H2S content of the treated gas for both internals types was calculated to be 0.64 ppmv, but the measured value was a factor of 40 higher. This is too large to be anything other than an error either in the basic data fed to the model or in the model itself. What turned out to have been overlooked was the real solvent analysis, i.e. incomplete data was used in the model. It was not realised that heat stable salts (HSS) have a profound effect on treating, primarily by affecting vapour-liquid HYDROCARBON 45
ENGINEERING
May 2021
