FEEDBACK-CONTROL OF CHEMICAL PROCESSES USING ONLINE OPTIMIZATION TECHNIQUES

The objective of this research is to develop tools for feedback control and sensitivity analysis of systems modelled by nonlinear differential-algebraic equations. Features of this novel approach include: direct use of nonlinear models without linearization, the ability to handle multiple inputs and outputs without pairing, and the ability to handle input and output constraints without complicated anti-reset windup logic. In the model-predictive control framework, an optimal control policy for the nominal plant is determined using a simultaneous optimization and model solution approach. The sensitivity of the optimal solution with respect to the model parameters is also computed. The feedback from the measurements is used to update the important process model parameters and output disturbances. The optimal profile is then recomputed for the updated model. Confidence intervals can also be placed on the optimal control profiles by considering second-order variations in the Lagrangian. This approach will be illustrated with several examples including: batch and continuous chemical reactors, and a batch crystallizer, which demonstrates the method on a nonlinear, distributed parameter system. © 1990.