Robust Algorithms for Simultaneous Model Identification and Optimization in the Presence of Model-Plant Mismatch

In the presence of model-plant mismatch, the set of parameter estimates obtained using standard model identification procedures cannot accurately predict the gradients of the optimization problem. Hence, a “two-step” approach involving an identification followed by an optimization step, performed repeatedly, often fails to converge to the true process optimum. In our recent work, we proposed a new identification procedure that progressively corrects the model for structural error such that the updated set of parameter estimates simultaneously satisfies both identification and optimization objectives with a guaranteed convergence to the true optimum. In this paper, we expanded our previous methodology in two directions: first, a new model correction based on a higher order approximation of model is proposed, and second, the effect of model uncertainties is considered explicitly by formulating a robust optimization problem at each iteration. The resulting improvements are then illustrated using a simulated study of fed-batch penicillin production process.