Implementation of MPC Strategy in Reactive Separation Techniques and Its Benefits: A Demonstration with Natural Gas Sweetening Process

Abstract

Model Predictive Control (MPC) is a widely used method that has numerous applications in process industries. In the MPC group of controllers, a clear model is used directly for predicting future plant behavior and calculating corrective control action required to maintain the output at the desired set point value. It is well known that most chemical processes present inherent nonlinearities on account of disturbance, set-point changes.MPC variants based on nonlinear process models have produced stiff control of process along with improved handling of constraints, abnormal dynamics and time delays. One of the variants EMPC applied for various chemical processes have produced economic performance index over the horizon for achieving optimal output targets. In addition to that, adaptive MPC is better in handling the nonlinearity and time varying characteristics during run time by modifying model. The control of reactive separation process is difficult on account of process nonlinearity and interactions of vapor-liquid equilibrium with chemical reactions. Reactive separation is multi -input and multi-output(MIMO) system .In order to obtain the optimal performance, energy conservation and cost effectiveness of MIMO system , the application of optimal controller is inevitable. The application of optimal controller have exhibited better performance compared to tuned linear controller inspite of presence of unknown input delays. The mpc coupled with neural network have exhibited better controllability in case of reactive distillation process. This chapter will cover recent developments in MPC applicable to reactive separation techniques that consist of reactive distillation, reactive absorption, extractive reaction, reactive membrane separation which are used in applications such as LPG processing, natural gas sweetening process etc