Multivariable Adaptive Predictive Model Based Control of a Biodiesel Transesterification Reactor

Abstract

To control biodiesel reactors with complex and highly nonlinear dynamics, the controller must be able to handle multivariable problems as well as to adapt to time-varying dynamics. In this work, a multivariable adaptive predictive model based control (i.e., the centralized adaptive Generalized Predictive Control, GPC) strategy was simulated on a validated mechanistic transesterification model. The Recursive Least Squares (RLS) algorithm was used for process model adaptation in the GPC framework. Simulation results revealed the superiority of the proposed centralized adaptive predictive control scheme as compared to the decentralized conventional PID controllers in terms of set point tracking, process interactions handling and resultant controller moves. Good load disturbance rejection properties were also demonstrated by the proposed control scheme.