Run-to-run optimization via control of generalized constraints

Run-to-run optimization methodologies exploit the repetitive nature of batc h processes to determine the optimal operating policy in the presence of un certainty. In this paper, a parsimonious parameterization of the inputs is used and the decision variables of the parameterization are updated on a ru n-to-run basis using a feedback control scheme which tracks signals that ar e invariant under uncertainty. In this run-to-run framework, terminal const raints of the optimization problem and cost sensitivities constitute the in variant signals. The methodology is conceived to improve the cost function from batch-to-batch without constraint violation. (C) 2001 Elsevier Science Ltd. All rights reserved.