A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of all kinds of uncertainties. This assumption is not satisfied in some cases and the standard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solution model. The proposed approach is illustrated via a bioreactor in penicillin fermentation process.

To deal with uncertainties from process disturbances, model mismatch and measurement noise, a novel two-level NCO-tracking scheme is proposed. The outer level is used to update solution model using online measures and estimations, while the inner level is a standard NCO-tracking. A trigger unit with a parameter variation index is used to decide which level should be triggered to calculate the optimal input profiles. With this structure, the two-level NCO-tracking scheme can achieve optimality even if the uncertainty is large to change the solution model, which is assumed to be invariant in the traditional NOC-tracking scheme.Figure optionsDownload as PowerPoint slide