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In this paper, we present a systematic approach to quantify the safe operating window of a proton exchange membrane water electrolysis (PEMWE) system considering energy intermittency and varying hydrogen demand. The PEMWE model has been developed based on first principles, with the polarization curve validated against a lab-scale experimental setup. The impact of key operational variables is investigated which include voltage, inlet temperature, and water flowrate (utilized for both feed and system cooling). Emphasis is given on operating temperature, a safety-critical variable, as its elevation can pose significant hydrogen safety risks within both the electrolyzer cells and the storage system. The impact of temperature on process safety is quantified via a risk index considering the fault probability and consequence severity. Process operability analysis is employed to assess the achievability of a safe and feasible region for design and operations. This analysis provides a comprehensive framework to optimize PEMWE systems for enhanced operational flexibility and robust performance with application to modular hydrogen production using renewable energy sources. 

Maintaining operational efficiency while ensuring safety is a longstanding challenge in industrial process control, particularly in high-risk environments. This paper presents a novel Dynamic Risk-Informed Explicit Model Predictive Control (R-eMPC) framework that integrates safety and operational objectives using probabilistic constraints and real-time risk assessments. Unlike traditional approaches, this framework dynamically adjusts safety thresholds based on Bayesian updates, ensuring a balanced trade-off between reliability and efficiency. The validation of this approach is illustrated through a case study on tank level control, a safety-critical process where maintaining the liquid level within predefined safety limits is paramount. The results demonstrate the frameworks capability to optimize performance while maintaining robust safety margins. By emphasizing adaptability and computational efficiency, this research provides a scalable solution for integrating safety into real-time control strategies for similar process systems.