课题基金 / 基金详情

Path Sampling and Dynamic Risk Analysis

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:
$35.0万
依托单位:
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
项目状态:
未结题
起止时间:
2022-08-15 至 2025-07-31

项目摘要

项目成果

Warren Seider的其他基金

相似基金

相关文献

中文摘要
翻译
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作用。事实上,为了最大限度地减少可能导致人员伤亡或重大环境破坏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常见和最广为人知的异常事件方面通常是成功的,但要实时检测罕见和意外异常事件的发生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之前的工作,采用了研究分子运动的计算策略,以揭示导致工厂关闭或由罕见和高度异常的安全事件导致的事故的条件。幸运的是,代价高昂的工厂关闭或危险事故很少发生,但因此,几乎没有(如果有的话)数据可以提醒工厂操作员有足够的时间采取安全措施来规避它们。这项提议寻求继续制定战略,以便在面对意外的异常事件时更可靠地设置警报和采取安全行动。这些战略有可能防止巨大的经济损失,防止严重伤害,并拯救生命。为了推进路径抽样策略,以识别不太可能的核电站关闭和事故,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。研究计划将从成熟的策略开始,例如动态风险分析(DRA-由该项目的PI在过去15年中开发),用于估计与众所周知的假定异常事件相关的故障概率。使用噪声表示罕见的非假定异常事件数组,将进行计算实验,以将提交者概率(预测故障路径的承诺概率的函数)与将为其创建警报阈值和安全系统的过程变量联系起来-这将使DRA的应用首次能够通过对非假定罕见事件的响应估计过程故障概率来在线评估安全系统的有效性。在这样做的过程中,研究人员将承担设计新的多变量实时报警系统,这不仅将通过消除假阴性来实现更安全的操作,还将减轻假阳性警报的滋扰。随着这些策略的开发,它们将使用PI的长期合作伙伴液化空气提供的历史操作数据,在著名的工业流程(例如,蒸汽-甲烷重整(SMR)生产氢气)上进行测试。这些数据对于理解几年来核电站关闭和事故的罕见途径是如何启动的至关重要--最终的安全系统响应只需几分钟到几个小时。逐渐地,这些策略的应用将从放热、连续搅拌的釜式反应器(CSTR)转移到更复杂的聚合反应器,最终将扩大到具有回收的综合化学过程。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。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
EAGER: GOALI: REAL-D Path-Sampling Algorithms to Understand Rare Safety Events and Improve Alarm Systems
  • 批准号:
    1839535
  • 项目类别:
    Standard Grant
  • 资助金额:
    $15.0万
  • 财政年份:
    2018
  • 负责人:
    Warren Seider
  • 依托单位:
GOALI: Collaborative Research: Model-Predictive Safety Systems for Predictive Detection of Operation Hazards
  • 批准号:
    1704833
  • 项目类别:
    Standard Grant
  • 资助金额:
    $10.77万
  • 财政年份:
    2017
  • 负责人:
    Warren Seider
  • 依托单位:
Collaborative Research: GOALI: Synergistic Improvement of Process Safety and Product Quality Using Process Databases
  • 批准号:
    1066475
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $35.2万
  • 财政年份:
    2011
  • 负责人:
    Warren Seider
  • 依托单位:
Dynamic Risk Assessment of Inherently Safe Chemical Processes: Using Accident Precursor Data
  • 批准号:
    0553941
  • 项目类别:
    Continuing grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2006
  • 负责人:
    Warren Seider
  • 依托单位:
海外基金