Machine Learning for Real-time Optimization of Bioprocessing Parameters: Applications and Improvements

Abstract

The biomanufacturing industry requires advanced optimization strategies to maintain product quality while maximizing production efficiency. We present Bio-MARL, a framework that integrates time-series prediction and multi-objective control for bioprocess optimization. Across datasets, productivity increased by 29.9% (CHO 28.5%, E. coli 34.2%, Yeast 26.9%). and batch success reached 94.8%. Resource consumption decreased by 20-25% depending on process type. The architecture weaves together LSTM and Transformer models for temporal prediction, multi-objective algorithms that handle real-world trade-offs, and predictive maintenance that reduces unplanned downtime by 43%. Three industrial datasets validate our approach: CHO cell cultures producing monoclonal antibodies, E. coli producing recombinant proteins, and yeast manufacturing enzymes at scale. The three datasets collectively demonstrate consistent improvements across yield, quality, and robustness. These results indicate that intelligent automation can materially improve biomanufacturing economics while strengthening supply-chain resilience.