Adaptive Optimizing Control of an Ideal Reactive Distillation Column

Control of reactive distillation (RD) systems is a challenging problem due to nonlinear dynamic and steady state behavior arising from complex interactions between reaction kinetics and vapor liquid equilibria. The focus of work reported in the literature on control of RD systems is on solving servo and regulatory control problems. However, when the unmeasured disturbances / system parameters drift from their nominal values, the operating performance of a RD system can potentially be improved if real time optimization (RTO) techniques are employed for deciding the optimum operating point on-line. In this work, an integrated RTO and adaptive nonlinear model predictive control (NMPC) approach has been proposed for operating an RD system in a economically optimal manner. At the core of the integrated scheme is a nonlinear Bayesian state and parameter estimator, which is used as a common link between the RTO and the NMPC components. Estimates of the drifting unmeasured disturbances / parameters generated by the state estimator are used to update the steady state model used for RTO and the dynamic model used for predictions. This facilitates relatively frequent application of RTO without having to wait for the system to reach steady state and makes the NMPC formulation adaptive. Efficacy of the proposed integrated optimizing control scheme is demonstrated by conducting simulation studies on an ideal RD column. The control problem under investigation is optimal inferential control of product concentrations in the face of drifting reactant flow disturbance. Analysis of the simulation results reveals that the proposed integrated approach is able to satisfactorily identify and track economically beneficial optimum operating point of the system.