Optimization-based operational space design for effective bioprocess performance under uncertainty

Maintaining consistent product quality and yield in bioprocess operations is challenging due to uncertainties inherent in biological systems. Thus, robust strategies are essential to ensure key performance indicators (KPIs), such as product concentration and yield, are consistently met despite the uncertainties. Real-time feedback co Interntrol, though widely used, is often impractical due to its reliance on expensive sensors, rapid data processing, and high-speed control actions. This paper proposes a novel approach to address these challenges by identifying the operational space for control variables, ensuring KPI reliability without requiring real-time control. This operational space serves as a guideline such that, if we operate within this space, the KPIs can be reliably achieved, regardless of the considered uncertainties. Specifically, we reformulate the problem as an optimization task to maximize the operational space, subject to constraints imposed by process dynamics and performance criteria. Using CasADi for symbolic computation and IPOPT for optimization, a stage-wise procedure is implemented to enhance computational efficiency. Unlike surrogate-based methods, this approach directly preserves model fidelity and accommodates path constraints effectively. The method is validated in a case study of fermentation process, involving a time-varying control variable and seven uncertain model parameters. Results demonstrate the ability to define a reliable operational space that enables KPI adherence across parameter variations, proving the method�s efficiency and practical applicability.