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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. 

This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation–based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning–and quantum computing–assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies. 

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.