A Safety-Aware Agentic LLM–Digital Twin Framework for Fuzzy Multi-Objective Control of Industrial Fed-Batch Bioreactors

This study develops a safety-aware supervisory framework for industrial fed-batch bioreactors by integrating an adaptive digital twin, fuzzy multi-objective optimization, Agentic LLM reasoning, and an independent Safety Governor within a closed-loop architecture. The digital twin continuously reconstructs process states and updates sensitive parameters, while α-cut fuzzy intervals preserve biological and operational uncertainty throughout prediction and optimization. The Agentic LLM coordinates prediction, optimization, process knowledge, and candidate evaluation, and supports context-dependent reevaluation and replanning as operating conditions evolve. The framework was evaluated using data from 12 industrial 72 h fed-batch runs, together with 600 uncertainty-aware simulated trajectories. Across three fully independent validation batches, the digital twin achieved an average NRMSE of 4.7%, MAPE of 4.4%, and R2 of 0.985. Closed-loop operation achieved a volumetric productivity of 0.347 g/L·h while maintaining a positive minimum dissolved-oxygen margin of 5 percentage points and zero hard-constraint violations. Across nominal and stressed scenarios, the Safety Governor preserved feasible operation with no residual hard-constraint violations, while the Agentic LLM successfully activated reevaluation and replanning following changes in process conditions. The ablation results further showed that the complete architecture provided the strongest overall balance among productivity, biological risk, robustness, and disturbance recovery. These findings demonstrate the value of combining adaptive process representation, uncertainty-aware optimization, agentic supervisory reasoning, and independent safety validation for reliable intelligent bioprocess operation.