An economic predictive controller with gradient correction for perfusion and continuous fermentations

The performance of economic model-based optimization algorithms for bio-processes is significantly limited by model-plant mismatch that is pervasive in such processes due to their complexity and nonlinear behavior. Such a mismatch causes model-based optimizers to drive the process to sub-optimal operating conditions. This work presents an economic model predictive control (eMPC) framework designed to address model–plant mismatch in continuous and perfusion bio-processes. The methodology combines recursive parameter estimation with a gradient correction step, ensuring convergence toward the true plant optimum even in the presence of structural errors and noise. To improve robustness and reduce tuning effort, event-triggered execution and adaptive selection of the gradient history length are introduced. The framework is validated through two case studies: continuous penicillin fermentation and CHO cell perfusion. In the penicillin case, gradient correction outperforms conventional eMPC, achieving less than 3.7% deviation from the true optimum. In the CHO case, the proposed method improves stability and increases total obtained productivity by 75% compared to conventional eMPC. These results demonstrate the robustness and industrial relevance of the proposed gradient-correction based framework.