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EAGER: GOALI: REAL-D Path-Sampling Algorithms to Understand Rare Safety Events and Improve Alarm Systems

EAGER：GOALI：用于了解罕见安全事件并改进警报系统的 REAL-D 路径采样算法

基本信息

批准号：
1839535
负责人：
Warren Seider
金额：
$ 15万
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：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839535&HistoricalAwards=false
关键词：
EAGER GOALI REAL Path Sampling

项目摘要

Chemical manufacturing processes can pose serious hazards, so safety considerations play an important role in their design. To minimize the risk of catastrophic accidents, which can result in loss of human life or major environmental impacts, extensive instrumentation such as control systems, alarms, and safety interlocks, are routinely employed in chemical processes. While such efforts are successful in mitigating the most likely adverse events, it is challenging to anticipate and mitigate the effects of infrequent adverse events in real-time. Importantly, such rare safety events, which have not been considered in plant design, can lead to the most severe consequences. The proposed exploratory research project is a collaboration between a research team from the University of Pennsylvania and an industrial partner, Air Liquide, and aims to understand the pathways leading to rare safety events in chemical manufacturing processes through a combination of process modeling, chemical plant data, and path-sampling algorithms, and to use this understanding to design novel multi-variable alarm systems for the real-time monitoring and prevention of such incidents.To combine process models with path-sampling algorithms, such as Forward Flux Sampling, a simple exothermic continuous stirred-tank reactor (CSTR) model will be employed first. Then, a state-of-the-art model for Air Liquide's realistically complex steam-methane reforming (SMR) process will be studied to obtain an ensemble of rare accident trajectories. By analyzing these trajectories, accident frequencies and durations will be determined, along with secondary process variables, which are likely to play an important role in precipitating accidents. In the vicinity of the most likely rare-event trajectories, actual plant data will be sought with projections obtained using near-miss data in Air Liquide historian databases. The proposed research could lead to the adoption of path-sampling methods to predict rare events in other fields, which are both data-rich and have reliable models available, e.g., in forecasting extreme weather events. The research will be integrated with educational and outreach efforts. The Principal Investigator will incorporate the results of the research in subsequent editions of his widely adopted textbook, "Product and Process Design Principles", which has been used by over 40,000 students worldwide. Finally, there is a plan to develop a hands-on outreach program to mentor underrepresented minority students from local colleges to encourage participation in STEM education.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。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的环境影响，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。虽然这些努力成功地减轻了最可能的不良事件，但实时预测和减轻罕见不良事件的影响具有挑战性。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。拟议的探索性研究项目是宾夕法尼亚大学的一个研究小组与工业合作伙伴液化空气公司之间的合作，旨在通过过程建模，化工厂数据和路径采样算法的组合，了解导致化学制造过程中罕见安全事件的途径。并利用这种理解来设计新颖的多变量报警系统，用于实时监控和预防此类事件。将联合收割机过程模型与路径采样算法相结合，例如前向通量采样，首先采用简单的放热连续搅拌釜反应器（CSTR）模型。然后，将研究液化空气公司现实复杂的蒸汽甲烷重整（SMR）过程的最先进模型，以获得罕见事故轨迹的集合。通过分析这些轨迹，事故频率和持续时间将被确定，沿着的二次过程变量，这可能会在促成事故中发挥重要作用。在最有可能的罕见事件轨迹附近，将通过使用液化空气历史数据库中的未遂事故数据获得的预测来寻找实际工厂数据。拟议的研究可能导致采用路径抽样方法来预测其他领域的罕见事件，这些领域既有丰富的数据，又有可靠的模型，例如，极端天气事件的预测。这项研究将与教育和外联工作相结合。首席研究员将把研究结果纳入其广泛采用的教科书“产品和工艺设计原理”的后续版本中，该教科书已被全球40，000多名学生使用。最后，还有一项计划是制定一个实践推广计划，指导当地大学中代表性不足的少数民族学生，鼓励他们参与STEM教育。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。