A Data-Driven Adaptive Predictive Control Framework for Stabilizing Dissolved Oxygen and pH in Bioreactor Systems Under Temperature Disturbances

Maintaining stable dissolved oxygen (DO) and pH conditions is critical for reliable operation of bioreactor systems used in cell culture and bioprocess manufacturing. However, accurate regulation of DO and pH remains challenging due to nonlinear process dynamics and variations in operating conditions. In particular, temperature fluctuations can affect gas solubility, gas–liquid mass transfer, and CO2 buffering equilibrium, resulting in deviations in DO and pH. Existing control methods often rely on predefined mechanistic models or reactor-specific parameter identification, which may limit adaptability under changing operating conditions. This paper proposes a disturbance-compensated data-driven adaptive predictive control framework for DO and pH stabilization in bioreactor systems under dynamic temperature disturbances. Based on dynamic linearization, the proposed framework establishes an online input–output representation using measured gas composition, temperature disturbance, and environmental responses. An adaptive gain adjustment mechanism and pseudo-partial-derivative estimation method are developed to update the control relationship online without requiring an explicit process model or iterative optimization. Furthermore, temperature variations are incorporated as measurable disturbances to achieve real-time compensation of their effects on DO and pH dynamics. The proposed framework was evaluated through simulations and experiments using a 3 L bioreactor platform. Compared with a PID controller with temperature feedforward and conventional model-free adaptive predictive control, the proposed method reduced DO and pH tracking errors and improved recovery performance under temperature disturbances. The results demonstrate that the proposed data-driven adaptive predictive control strategy provides an effective approach for DO and pH stabilization in bioreactor systems under temperature-varying conditions.