Collaborative Research: RETRO: Toward Safe and Smart Operations via REal-Time Risk-based Optimization

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Process safety management (PSM) aims to prevent the occurrence of hazardous events under abnormal process conditions, typically relying on passive protection mechanisms (e.g., pressure relief valves). The ongoing trends toward industrial digitalization and smart manufacturing have posed new challenges to PSM with substantially more complex, dynamic, and integrated process plants. Thus, there is an imperative need to unravel the link between safety-critical decision making and systems-based real-time operation which can proactively reduce chemical process safety losses. Toward this goal, this research project aims to create a paradigm shift by integrating online process safety monitoring, model-based abnormality prediction, and prognostic risk control. A unified theory, conceptual framework, and software prototype will be developed based on a fundamental understanding of process and safety system dynamics. These methodological developments will be demonstrated on a hydrogen fuel cell experimental prototype, which will serve as a concrete guide for next-generation smart PSM system designs for a broad range of manufacturing industries to circumvent the annual billion-dollar financial, societal, and environmental losses across the US due to process incidents. The project findings will be incorporated to course materials, online learning modules, and workshops tailored to undergraduate, graduate, and high school students. This project also will be used to recruit a diverse group of underrepresented and first-generation students by leveraging existing STEM programs at West Virginia University, Texas A&M University, and regional alliances.This project will develop an online process safety management strategy coupling offline computation of fit-for-purpose risk control with real-time optimization to simultaneously account for the interactions and tradeoffs of process safety, operability, and economics. The major pillars of planned research activities feature: (i) Statistical dynamic risk modeling, which explicitly considers the nonlinear physics-based interactions of safety-critical process variables; (ii) Risk-based multi-parametric model predictive control, which provides a dual-layer predictive safety management design with adjustable risk control and bounded process operation path; (iii) Error-tolerant process safety control, which offers theoretically guaranteed robustness against dynamic errors in model approximation, real-time measurement, and state estimation; and (iv) Fault-prognostic design, control, and real-time optimization, which addresses these multiple decision layers in a simultaneous manner via a single mixed-integer dynamic programming formulation. A key innovation of this project lies in a novel multi-parametric optimization-based representation to this multi-time-scale decision making problem, which results in a temporally scalable and self-adapting process safety management strategy allowing for efficient, agile, and flexible application in all types of process systems with fast, slow, or hybrid dynamics. The in silico methodological developments will be applied to a cyber-physical prototype system of lab-scale polymer electrolyte membrane hydrogen fuel cell to achieve optimal demand-driven power production with safe, healthy, and sustainable operations under market demand changes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.