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Improving Gas Plant Throughput and Robustness with MPC

by | Oct 20, 2008 | Industry, Oil & Gas, Services, Consulting & Training

Jim Cahill

Jim Cahill

Chief Blogger, Social Marketing Leader

A great presentation at the recent Emerson Exchange was one that discussed the results of applying model predictive control (MPC) in a challenging gas plant process. Model predictive control has been available for decades and used in very large applications such as refinery process units, but its use in smaller applications found in the oil and gas sector is relatively new.

This plant was limited by its field compression capability, and throughput could be increased if they could reduce the differential pressure (DP)–mainly by lowering the inlet pressure to the gas plant. Swings in gas flows were introducing disturbances to downstream equipment such as carbon dioxide removal trains, causing them to trip. This caused further disturbances and in turn caused the operators to run these CO2 removal trains very conservatively to minimize the risk of cascading train trips. The result was that overall throughput was reduced–directly impacting the bottom line financial performance of the plant.

Sarah Perkins and Andrew Taylor of ProSys Engineering, based in Australia, were called in to work with the gas plant’s engineering staff to develop control strategies to maximize throughput while minimizing upset conditions and their cascading effect on other process units.

They saw three areas where advanced process control, specifically MPC, could be applied to meet the overall objective of maximizing plant throughput capacity. These included minimizing the differential pressure across the CO2 removal trains, minimizing the liquid recovery plant (LRP) inlet pressure (to reduce overall plant inlet pressure), and using the plant’s incoming pipeline as a surge vessel to eliminate spikes in inlet pressure which might trip the reciprocating compressors.

Let’s dig in a little deeper in one of these areas–CO2 removal trains. This gas plant had a number of these trains in parallel. The objective of the model predictive control strategy was to maximize the flow through the train either to a specified high limit or to valve saturation–whichever constraint was active first. Satisfying this objective effectively minimizes the differential pressure across each CO2 removal train.

For each train, they designed and implemented a dedicated DeltaV PredictPro MPC control block running in their DeltaV controllers. To minimize the differential pressure, the goal was to maximize the butterfly valve opening coupled with the need to quickly cut back the flow in case another CO2 removal train trips. With this MPC-based flow controller, the butterfly valves were linearized where the output was expressed as a % of flow capacity, instead of a % of valve position. Each butterfly valve had different flow characteristics, so each MPC flow controller was individually characterized.

The feed gas flow was the manipulated variable; the constraints were setpoint (SP) minus process variable (PV) error, and an operator-entered maximum flow rate. These constraints help to detect trip conditions and honor process limits like flow rates at which foaming begins to occur. In abnormal situations, the maximum flow rates are set to current flow rates to allow the operators a chance to make decisions about redistributing the flow rates.

The team made custom graphics for the operators to see the CO2 removal trains on a single view, to quickly recognize patterns of abnormal situations and to take manual corrective action.

The payback on increased throughput was less than two months and even more throughput will occur when all of the reciprocating compressors are reconfigured for the reduced operating discharge pressure.

With MPC available at the DCS controller level, it can be applied to many smaller and mid-size applications in oil & gas and other industries. Engineers like Andrew and Sarah are helping process manufacturers solve challenging problems like these that also deliver fast financial results.


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The opinions expressed here are the personal opinions of the authors. Content published here is not read or approved by Emerson before it is posted and does not necessarily represent the views and opinions of Emerson.