Abstract This study presents a deep reinforcement learning (DRL)‐based strategy for the temperature control of a continuous ethanol fermentation bioreactor. Temperature is a critical operational variable that influences microbial growth, metabolic activity, and ethanol productivity, particularly in processes involving Saccharomyces cerevisiae . Owing to the nonlinear and strongly coupled dynamics of fermentation systems, conventional controllers often struggle to maintain tight temperature regulation under varying operating conditions, disturbances, and measurement uncertainties. To address these limitations, a deep deterministic policy gradient (DDPG) controller is developed and integrated with a detailed mechanistic model of the bioreactor. The controller is trained using the reactor temperature, error, and integral of error as observations, while the cooling‐water flow rate serves as the manipulated variable. Closed‐loop simulations demonstrate that the DDPG controller achieves fast and accurate set‐point tracking with minimal overshoot and significantly reduced control effort compared with the proportional integral derivative (PID) controller, fractional order PID (FO‐PID) controller, and model predictive control (MPC). Performance indices, including rise time, settling time, integral absolute error, and total variation, confirm that the DDPG controller consistently provides superior transient and steady‐state behaviours. Robustness tests incorporating sequential set‐point changes, inlet temperature disturbances, flow rate perturbations, and measurement noise further show that the learned policy maintains stable reactor operation while minimizing deviations in key biochemical states such as ethanol and biomass concentrations.
Parambil et al. (Sun,) studied this question.