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

化学制造过程可能会带来严重的危险，因此安全考虑在其设计中起着重要的作用。为了最大限度地减少可能导致人员伤亡或重大环境影响的灾难性事故的风险，化学过程中经常使用控制系统、警报和安全联锁等广泛的仪器设备。虽然这种努力在减轻最可能的不良事件方面是成功的，但实时预测和减轻罕见不良事件的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。该探索性研究项目是宾夕法尼亚大学的一个研究团队与工业合作伙伴Air Liquide的合作项目，旨在通过过程建模、化工厂数据和路径采样算法的组合来了解导致化工制造过程中罕见安全事件的路径，并利用这一理解设计新型多变量警报系统，用于实时监控和预防此类事件。为了将过程模型与路径采样算法(如正向通量采样)相结合，将首先使用一个简单的放热连续搅拌釜式反应器(CSTR)模型。然后，将研究液化空气真实复杂的蒸汽-甲烷重整(SMR)过程的最新模型，以获得一系列罕见的事故轨迹。通过分析这些轨迹，将确定事故频率和持续时间，以及可能在引发事故中发挥重要作用的次要过程变量。在最有可能的罕见事件轨迹附近，将利用液化空气历史学家数据库中的险些失手数据获得预测，以寻找实际的植物数据。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，又有可靠的模型可用，例如在预测极端天气事件方面。这项研究将与教育和外联工作相结合。首席调查员将把研究结果纳入他广泛采用的教科书《产品和工艺设计原理》的后续版本中，这本教科书已被全球40,000多名学生使用。最后，有一项计划是开发一个实践推广计划，指导来自当地大学的少数族裔学生，以鼓励他们参与STEM教育。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.