Chapters authored
Implementation of MPC Strategy in Reactive Separation Techniques and Its Benefits: A Demonstration with Natural Gas Sweetening Process
Model Predictive Control (MPC) is a widely used method that has numerous applications in process industries. In the MPC group of controllers, a clear process model is used directly for controlling, predicting future plant behavior, and calculating corrective control action required to maintain the output at the desired set point value. Most chemical processes exhibit inherent nonlinearities due to interactions among processes, disturbance, and set-point changes. MPC variants based on nonlinear process models have been a proven stiff control technique for process control along with improved handling of constraints, abnormal dynamics, and time delays. In addition to that, MPC is better in handling the nonlinearity and time-varying characteristics during run time by modifying model. The control of multi-input and multi-output reactive-separation process is difficult due to nonlinearity associated with the process and interactions of vapor-liquid equilibrium with chemical reactions. In order to obtain optimal performance, energy conservation, and cost-effectiveness in reactive-separation process, the application of optimal control technique is inevitable. This chapter addresses application of MPC and its benefits in reactive separation techniques, particularly in natural gas sweetening process. The recent application in MPC and its proven results for the above-mentioned reactive separation processes are discussed here.
Part of the book: Model Predictive Control