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

合作研究：RETRO：通过实时基于风险的优化实现安全和智能运营

• 批准号: 2312458
• 负责人: Efstratios Pistikopoulos
• 金额: $ 19.27万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312458&HistoricalAwards=false
• 关键词: Collaborative; Research; RETRO; Toward; Safe

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.

工艺安全管理（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，压力缓解阀）。工业数字化和智能制造业的持续趋势为PSM提出了新的挑战，其更为复杂，动态和集成的过程工厂。因此，务必需要揭示安全至关重要的决策与基于系统的实时操作之间的联系，这可以主动降低化学过程的安全损失。为了实现这一目标，该研究项目旨在通过整合在线流程安全监控，基于模型的异常预测和预后风险控制来创造范式转变。统一的理论，概念框架和软件原型将基于对过程和安全系统动态的基本理解。这些方法论的发展将在氢燃料电池实验原型上展示，该原型将作为下一代智能PSM系统设计的具体指南，用于广泛的制造行业，以避免由于过程事件的造成的年度亿万美元金融，社会和环境损失。该项目的发现将纳入课程材料，在线学习模块和针对本科，研究生和高中生量身定制的讲习班。该项目还将通过利用德克萨斯州农工大学西弗吉尼亚大学的现有STEM计划以及区域联盟来招募一组代表性不足和第一代学生。该项目将在线过程安全管理策略耦合离线计算，以实时优化互动和交易的互动和交易，并互动，并对互动和贸易进行互动和贸易，并互动。计划研究活动的主要支柱是：（i）统计动态风险建模，该模型明确考虑了基于非线性物理学的安全 - 关键过程变量的相互作用； （ii）基于风险的多参数模型预测控制，该控制提供了可调节风险控制和有界过程操作路径的双层预测安全管理设计； （iii）易于误差的过程安全控制，理论上可以保证模型近似，实时测量和状态估计中的动态误差的鲁棒性； （iv）故障验证设计，控制和实时优化，该设计通过单个混合成分动态编程公式以同时方式解决这些多个决策层。该项目的关键创新在于对这个多时间尺度决策问题的新型多参数优化的表示，这导致了具有时间可扩展和自我调整的过程安全管理策略，允许在具有快速，慢速或混合动力学的所有类型的过程系统中有效，敏捷和灵活的应用。硅方法的发展将应用于实验室尺度聚合物电解膜氢燃料电池的网络物理原型系统，以实现最佳需求驱动的发电，并在市场需求下进行安全，健康且可持续的操作。该奖项反映了NSF的法定任务，反映了通过评估范围的Intelligia Merit and Intellitial and Intellitial and Intellitial and Intellitial的支持。