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Abstract This study provides an experimental validation of a multiple‐input multiple‐output (MIMO) model predictive control (MPC) strategy, coupled with dynamic risk modeling, to address two critical aspects of proton exchange membrane water electrolysis (PEMWE) operation: (i) process safety, by mitigating temperature imbalances, and (ii) system performance, through precise hydrogen production control. A cyber‐physical platform was developed for real‐time monitoring, state‐space modeling and validation, risk metrics analysis, control implementation, and visualization. Open‐loop experiments revealed limitations in managing thermal gradients, underscoring the need for feedback operating strategies. The proposed closed‐loop MPC approach achieved precise tracking of hydrogen production while maintaining safety by ensuring temperature stability. Moreover, the dynamic risk metrics show how thermal risk evolves with temperature and offer guidance for decision‐making. These findings demonstrate the effectiveness of MIMO MPC in enhancing the operational safety and efficiency of PEMWE systems, providing a foundation for scalable and sustainable hydrogen production. 

Abstract Simultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed‐integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed‐loop implementation. We use multi‐parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base‐2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.