A robust batch-to-batch optimization framework for pharmaceutical applications

Abstract

The study proposes a robust algorithm for batch-to-batch optimization in the presence of model-mismatch. Robustness is achieved by the implementation of the following features: i — the gradient correction step is modified to consider the gradients of the cost function and constraints at both final and intermediate points, ii — Economic Model Predictive Control is applied to mitigate the impact of unmeasured disturbances on the optimum, and iii — an optimal design of experiments is performed to expedite convergence. Significant improvements of the proposed algorithm in convergence to the process optimum and robustness to noise, unmeasured disturbances, and model error are demonstrated using a fed-batch fermentation for penicillin production.