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.

化学制造过程可能会造成严重的危害，因此安全考虑在其设计中发挥着重要作用。事实上，为了最大限度地减少灾难性事故的风险，这可能导致人类生命损失或重大环境破坏，在化学过程中通常采用大量的仪表，如控制系统，警报和安全联锁。虽然这些努力通常成功地减轻了最常见和众所周知的异常事件，但实时检测罕见和意外异常事件的发作并减轻其影响是具有挑战性的。重要的是，这种罕见的安全事件，在工厂设计中没有考虑到，可能会导致最严重的后果。该项目扩展了该研究团队先前的工作，即调整为研究分子运动而开发的计算策略，以揭示导致工厂关闭或罕见和高度异常安全事件导致的事故的条件。幸运的是，昂贵的工厂停工或危险事故很少发生，但因此，几乎没有（如果有的话）数据可以在足够的时间内提醒工厂操作员采取安全措施来规避它们。这项建议旨在继续制定战略，以便在遇到意外异常事件时更可靠地设置警报和采取安全行动。这些策略有可能防止巨大的经济损失，防止严重伤害，并挽救生命。为了推进路径采样策略以识别不太可能发生的工厂关闭和事故，研究团队将寻求新的策略来设置警报并严格应用安全系统。研究计划将从完善的策略开始开始，例如动态风险分析（由本项目的PI在过去15年中开发），用于估计与众所周知的假设异常事件相关的故障概率。使用噪音来表示一系列罕见的未假设的异常事件，将进行计算实验来关联提交者概率（预测进入故障路径的概率的函数）到将创建警报阈值和安全系统的过程变量-这将使得能够应用故障诊断，首次，在线评估安全系统的有效性，通过估计响应于未假设的罕见事件的过程故障概率。在此过程中，研究人员将设计新的多变量实时报警系统，这不仅可以通过消除假阴性来实现更安全的操作，还可以减轻假阳性报警的危害。随着这些策略的开发，它们将在众所周知的工业过程中进行测试（例如，蒸汽甲烷重整（SMR）制氢）的历史运行数据，该数据由PI的长期合作伙伴Air Liquide提供。这些数据对于了解几年来工厂关闭和事故的罕见路径是如何启动的至关重要-最终的安全系统响应在几分钟到几小时内完成。逐步地，这些策略的应用将从放热的连续搅拌釜式反应器（CSTR）转移到更复杂的聚合反应器，最终将扩大到具有再循环的综合化学过程。该奖项反映了NSF的法定使命，并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。