A Novel Bioprocess Control Strategy Under Uncertainty via Operational Space Identification

Abstract Bioprocesses are critical for sustainable industrial development but face challenges from their inherent uncertainties that affect efficiency and scalability. This study in-troduces a worst-case operational space design framework, integrating symbolic optimization with scenario-based validation, to preemptively mitigate uncertainties so that key performance indicators are consistently satisfied even under adverse conditions. The methodology is demonstrated through two case studies: a lab-scale fermentation for astaxanthin production and a site-scale anaerobic digestion for biogas production. Process uncertainty is quantified through model parameter variations (2% - 13%) to reflect real-world scenarios. The results indicate that highly flexible operational spaces are identified in both case studies, with control variables able to vary by up to 25% and 15% in the respective systems, while consistently adhering to system constraints. Validation over 10,000 scenarios confirmed 0 violation per case study. The proposed framework delivers validated operational flexibility with modest computational requirements, making it practical for lab-to-site deployment.