Distributed Model Predictive Control of an Amine Gas Sweetening Plant

This paper addresses the plant-wide control of the amine gas sweetening plant using distributed model predictive control. The plant is fed natural gas containing sour gases (hydrogen sulfide and carbon dioxide), which are removed by absorption in monoethanolamine solution. A plant decomposition algorithm based on modularity maximization for distributed parameter systems is used to obtain the optimal decomposition for distributed model predictive control. Comparisons are drawn among the performance and computational requirements of distributed, decentralized, and centralized model predictive controls.