A predictive modular approach to constraint satisfaction under uncertainty — with application to glycosylation in continuous monoclonal antibody biosimilar production

The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin and a constraint violation cost monitor to minimize the cost of violating soft constraints due to model uncertainty and disturbances. The module can be combined with any controller and is based on minimally modifying the controller output, in a least squares sense, such that constraints are satisfied within the considered horizon. The proposed method is computationally efficient and suitable for real-time applications. The effectiveness of the method is illustrated through a realistic case study of glycosylation constraint satisfaction in continuous monoclonal antibody biosimilar production using Chinese hamster ovary cells, employing a metabolic network model consisting of 23 extracellular metabolites and 126 reactions. In the case study, the average constraint-violation cost is reduced by more than 60% compared to the case without the proposed constraint-handling method. • A modular predictive approach to soft constraint handling under uncertainty. • Integrates a predictive filter with adaptive constraint margins. • Minimizes constraint violation cost with minimal impact on control performance. • Learns optimal constraint margins online from real-time process data. • Achieves 60% reduction in constraint violation costs in case study of mAb production.