Experimental Design in Simultaneous Identification and Optimization of Batch Processes under Model-Plant Mismatch. Issue 18 (2018) - Hille, Rubin; Budman, Hector M.
Abstract: Model-plant mismatch commonly arises from simplifications and assumptions during the development of first-principles models. Hence, when employing such models in iterative optimization schemes, structural mismatch may lead to inaccurate prediction of the necessary conditions of optimality. This results in convergence to a predicted optimum which does not coincide with the actual process optimum. The method of simultaneous identification and optimization aims to correct for errors in the predicted gradients of the cost and constraints by adapting the model parameters. In a former implementation of this approach, the gradients have been corrected only locally at the current operating point. To achieve a better prediction of the cost function over a wider range of input conditions, we propose to consider cost measurements from previous batch experiments combined with an optimal experimental design of future experiments. Using this approach, it is possible to achieve a better prediction, especially around the optimum, and to make the gradient correction step less susceptible to uncertainty in local gradient measurements. The improvements are illustrated using a simulated run-to-run optimization study of a cell-culture process.