Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance

A multivariable model predictive control (MMPC) is proposed to improve a control performance in Gas dehydration process. The FOPDT models are used to build an MMPC derived from the selected controlled variables (CV) and manipulated variables (MV). A set point (SP) tracking is used to test the control performance, with proportional-integral controller (PI) as a comparison. As an indicator of the control performance is the integral of square error (ISE). The result is a TITO (two-inputs two-outputs) MMPC, with sweet gas flow rate and heat duty of heater as MVs, and feed pressure and heater temperature as CVs, respectively. In the SP tracking test, MMPC showed better control performance than the PI controller with 11.29% performance improvement (pressure control) and 16.39% (temperature control)