Path Sampling and Dynamic Risk Analysis

路径采样和动态风险分析

• 批准号: 2220276
• 负责人: Warren Seider
• 金额: $ 35万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220276&HistoricalAwards=false
• 关键词: Path; Sampling; Dynamic; Risk; Analysis

Chemical manufacturing processes can pose serious hazards and so safety considerations play an important role in their design. Indeed, to minimize the risk of catastrophic accidents, which can result in loss of human life or major environmental damage, extensive instrumentation such as control systems, alarms, and safety interlocks, are routinely employed in chemical processes. While such efforts are generally successful in mitigating the most common and well-understood abnormal events, it is challenging to detect the onset and mitigate the effects of infrequent and unexpected abnormal events in real-time. Importantly, such rare safety events, which have not been considered in the plant design, can lead to the most severe consequences. This project extends the prior work of this research team in adapting computational strategies developed to study the motion of molecules to uncover conditions leading to plant shutdowns or accidents resulting from rare and highly abnormal safety events. Fortunately, expensive plant shutdowns or dangerous accidents rarely occur, but consequently, little (if any) data are available to alert plant operators in sufficient time to take safety actions to circumvent them. This proposal seeks to continue developing strategies for setting alarms and carrying out safety actions more reliably in the face of unanticipated abnormal events. These strategies have the potential to prevent large financial losses, prevent serious injuries, and save lives. To advance path-sampling strategies for identifying unlikely plant shutdowns and accidents, the research team will seek new strategies to set alarms and apply safety systems in a rigorous manner. The research program will begin with well-established strategies, such as dynamic risk-analysis (DRA – developed over the past 15 years by this project's PI), for estimating the failure probabilities associated with well-known postulated abnormal events. Using noise to represent an array of rare un-postulated abnormal events, computational experiments will be carried out to relate committor probabilities (functions predicting the probability of commitment to a path to failure) to process variables for which alarm thresholds and safety systems will be created - this will enable the application of DRA, for the first time, to evaluate online the effectiveness of the safety systems by estimating process failure probabilities in response to un-postulated rare events. In so doing, the researchers will undertake the design of new multi-variable real-time alarm systems, which not only will lead to safer operation by eliminating false-negatives, but also mitigate the nuisance of false-positive alarms. As these strategies are developed, they will be tested on well-known industrial processes (e.g., steam-methane reforming (SMR) to produce hydrogen) using historical operating data provided by the PI’s long-term collaborator, Air Liquide. These data are essential to understanding how, over several years, rare paths to plant shutdowns and accidents are initiated – with final safety system responses carried out in just minutes to few hours. Gradually, application of these strategies will move from exothermic, continuous-stirred tank reactors (CSTRs) to more complex polymerization reactors, and ultimately will scale-up to integrated chemical processes with recycle.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.

化学制造过程可能构成严重危害，因此安全考虑在其设计中起着重要作用。确实，为了最大程度地减少灾难性事故的风险，这可能导致人类生命或重大环境损失，广泛的仪器，例如控制系统，警报和安全互锁，是在化学过程中常规进行的。尽管这种努力通常在减轻最常见和充分理解的异常事件方面取得了成功，但要检测出发作并减轻实时异常异常事件的影响是挑战的。重要的是，植物设计中未考虑的罕见安全事件可能会导致最严重的后果。该项目扩展了该研究团队的先前工作，以调整开发的计算策略，以研究分子的运动，以发现导致工厂关闭或因罕见和高度异常安全事件而导致的事故。幸运的是，昂贵的工厂关闭或危险事故很少发生，但因此，很少有数据（如果有）可以在足够的时间内提醒工厂操作员以采取安全措施来规避它们。该提案旨在继续制定策略，以面对意外的异常事件，更可靠地制定警报和安全行动。这些策略有可能预防巨大的财务损失，防止严重伤害并挽救生命。为了促进识别意外的工厂关闭和事故的路径采样策略，研究团队将寻求新的策略来设置警报并以严格的方式应用安全系统。该研究计划将始于建立良好的策略，例如动态风险分析（DRA - 在过去的15年中由该项目的PI开发），用于估计与使用噪声代表一系列罕见的未验证异常事件相关的失败可能性，计算实验将在预测委员会的可能性上进行概率（可变性），以进行易于变化（可变性），以便将其函数变量（功能均可实现），以实现概率（功能）。将创建系统 - 这将使DRA的应用首次应用于在线评估安全系统的有效性，以估算未响应的罕见事件的过程故障可能性。这样一来，研究人员将对新的多变量实时警报系统进行设计，这不仅可以通过消除虚假阴性来实现安全操作，而且还可以减轻虚假阳性警报的滋扰。随着这些策略的制定，将使用PI的长期合作者Air Liquide提供的历史工作数据对知名的工业过程（例如蒸汽 - 甲烷改革（SMR）生产氢）进行测试。这些数据对于了解几年来启动停工和事故的罕见途径至关重要 - 最终的安全系统响应在短短几分钟到几个小时内就进行了。逐渐地，这些策略的应用将从放热，连续刺激的储罐反应堆（CSTR）转变为更复杂的聚合反应堆，并最终将扩展到与回收的集成化学过程。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是诚实的支持。