Interplay between identification and optimization in run-to-run optimization schemes

The use of measurements to compensate for model uncertainty and disturbances has received increasing attention in the context of process optimization. The standard procedure consists of iteratively using the measurements for identifying the model parameters and the updated model for optimization. However, in the presence of model mismatch, this scheme suffers from lack of synergy between the identification and optimization problems. This paper investigates the performance of run-to-run optimization schemes and proposes to modify the objective function of the identification problem so as to include the cost function and the constraints of the optimization problem. The weights of the various terms in the extended objective function axe based on Lagrange multipliers. The performance improvement obtained with the proposed methodology is illustrated via the simulation of a semi-batch reaction system.