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

过程安全管理（PSM）旨在防止在异常过程条件下发生危险事件，通常依赖于被动保护机制（例如，减压阀）。工业数字化和智能制造的持续发展趋势给PSM带来了新的挑战，因为PSM拥有更加复杂、动态和集成的过程工厂。因此，迫切需要解开安全关键决策和基于系统的实时操作之间的联系，这可以主动减少化学过程的安全损失。为了实现这一目标，本研究项目旨在通过整合在线过程安全监控，基于模型的异常预测和预后风险控制来实现范式转变。统一的理论，概念框架和软件原型将开发过程和安全系统动力学的基本理解的基础上。这些方法的发展将在氢燃料电池实验原型上得到展示，该原型将作为下一代智能PSM系统设计的具体指南，用于广泛的制造业，以规避由于过程事件而导致的美国每年数十亿美元的财务，社会和环境损失。该项目的研究结果将被纳入课程材料，在线学习模块，并为本科生，研究生和高中学生量身定制的研讨会。该项目还将利用西弗吉尼亚大学、德克萨斯农工大学和区域联盟现有的STEM项目，招募代表性不足的第一代学生&。该项目将开发一种在线过程安全管理策略，将符合目的的风险控制离线计算与实时优化相结合，同时考虑过程安全性、可操作性、和经济学。计划的研究活动的主要支柱功能：（i）统计动态风险建模，明确考虑安全关键过程变量的基于非线性物理的相互作用;（ii）基于风险的多参数模型预测控制，提供具有可调风险控制和有界过程操作路径的双层预测安全管理设计;（三）容错过程安全控制，在理论上保证对模型近似、实时测量和状态估计中的动态误差的鲁棒性;以及（iv）故障预测设计、控制和实时优化，其通过单个混合整数动态规划公式以同时的方式解决这些多个决策层。该项目的一个关键创新在于一种新的基于多参数优化的多时间尺度决策问题的表示，这导致了一种时间可扩展和自适应的过程安全管理策略，可以在具有快速，慢速或混合动态的所有类型的过程系统中实现高效，敏捷和灵活的应用。该研究成果将应用于实验室规模的聚合物电解质膜氢燃料电池的网络物理原型系统，以实现在市场需求变化下安全、健康和可持续的需求驱动型最佳发电。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.