Collaborative Research: GOALI: Synergistic Improvement of Process Safety and Product Quality Using Process Databases

合作研究：GOALI：使用过程数据库协同改进过程安全和产品质量

• 批准号: 1066461
• 负责人: Masoud Soroush
• 金额: $ 20.12万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1066461&HistoricalAwards=false
• 关键词: Collaborative; Research; GOALI; Synergistic; Improvement

1066461SoroushIntellectual Merit The chemical and petroleum industries and regulators have been improving the safety of processing plants, especially with every new accident such as those in the Gulf of Mexico, Texas City, Flixborough, Seveso, and Bhopal. In addition, the EPA, the American Chem. Council, Sandia Natl. Lab., the U.S. Coast Guard, and the Dept. of Homeland Security, have added security standards to existing safety regulations [OSHA Process Safety Management (PSM), EPA Risk Management Plan (RMP)] that apply to the chemical and petroleum industries. In spite of these efforts, the industries have devoted less attention to accurate risk and vulnerability assessments compared to the aircraft, military, and nuclear industries. The potential for loss of human lives and economic losses that may jeopardize companies' existences, in addition to social and legal complications, have increased the desire to have inherent safety and security, and dynamic risk assessment and reliability as vital requirements in the planning, development, design, control, and operations of processing plants. The PIs have developed a mathematical model to estimate the failure probabilities of various critical accident scenarios associated with a chemical process given abnormal events and accident precursor data, using copulas and Bayesian analysis. They extended this model to utilize large distributed control system (DCS) and emergency shutdown (ESD) system databases, involving alarm data associated with an industrial fluid-catalytic-cracking unit. In so doing, they developed new methods for estimating performance indicators, carrying out alarm system analysis, and estimating leading indicators of shut-downs (trips) and accidents - to assist process operators and management in recognizing near-misses and making adjustments to prevent the occurrence of dangerous and costly incidents. In this research, they will introduce and study new methods for dynamic risk assessment of chemical plants and test their findings in collaboration with Air Liquide Research and Development in Newark, DE. The methods will be tested using DCS and ESD system databases during steady operation and startup. Initially, they will work exclusively with safety data. Gradually, they will utilize product-quality data to identify near-misses and prevent accidents more effectively; that is, to achieve improved process safety and product quality in a synergistic way. Among the research challenges that will be investigated are: (1) efficiently handling large and complex event trees associated with alarm databases, (2) systematically conducting near-miss utilization and management to develop leading indicators, (3) introducing and testing a new Bayesian analysis method using copulas, (4) developing a method of identification of special causes from available process information at each time instant, (5) developing a method of predicting possible near-future accidents from available process information at each time instant, (6) efficiently handling the alarms associated with highly correlated variables, and (7) introducing a computationally-efficient method for estimating profit losses associated with near-misses. Prototype software will be developed to test the new techniques and to perform company-wide dynamic risk analysis. The methods will be implemented and tested on several industrially important processes through simulations and in real-time at Air Liquide. Broader Impacts Potential impacts of the project are societal, economical, technological and educational, among others. The new methods will permit more thorough risk analyses utilizing large dynamic databases providing safer processing plants that more consistently produce on-specification products, thus increasing profits. The methods and software will be available to the process industries and in design and control courses at universities. These new risk-assessment techniques will lead to more quantitative safety coverage in future editions of the PI's design textbook. Although the project focuses on near-misses and failure probabilities in processing plants, these techniques can be easily utilized in other industries/organizations, such as the aviation, healthcare and nuclear industries. The work is multidisciplinary in nature involving chemical engineers, risk analysts, and statisticians. Several students will be trained in this project.

1066461 Soroush知识产权优点化学和石油行业和监管机构一直在提高加工厂的安全性，特别是在每一个新的事故，如墨西哥湾，得克萨斯城，弗利克斯伯勒，塞维索和博帕尔。此外，美国环保署，美国化学理事会，桑迪亚国家。实验室、美国海岸警卫队和国防部美国国土安全部（DHS）在适用于化学和石油行业的现有安全法规[OSHA过程安全管理（PSM）、EPA风险管理计划（RMP）]中增加了安全标准。尽管做出了这些努力，但与飞机、军事和核工业相比，工业界对准确的风险和脆弱性评估的关注较少。除了社会和法律的复杂性之外，可能危及公司生存的人命损失和经济损失的可能性已经增加了对具有固有安全性和安保性以及动态风险评估和可靠性作为加工厂的规划、开发、设计、控制和操作中的重要要求的期望。PI已经开发了一个数学模型，以估计与化学过程相关的各种关键事故场景的故障概率，给定异常事件和事故前兆数据，使用Copula和贝叶斯分析。他们扩展了该模型，以利用大型分布式控制系统（DCS）和紧急停车（ESD）系统数据库，包括与工业流体催化裂化装置相关的警报数据。在此过程中，他们开发了新的方法，用于评估性能指标、进行报警系统分析以及评估停机（跳闸）和事故的主要指标，以帮助流程操作员和管理层识别未遂事故并进行调整，以防止发生危险和代价高昂的事故。在这项研究中，他们将介绍和研究化工厂动态风险评估的新方法，并与德国纽瓦克的液化空气研究与开发部合作测试他们的研究结果。这些方法将在稳定运行和启动期间使用DCS和ESD系统数据库进行测试。最初，他们将专门处理安全数据。他们将逐步利用产品质量数据来识别未遂事故并更有效地预防事故;也就是说，以协同的方式提高过程安全和产品质量。将调查的研究挑战包括：（1）有效地处理与警报数据库相关联的大而复杂的事件树，（2）系统地进行未遂事故利用和管理以开发领先指标，（3）使用连接函数引入并测试新的贝叶斯分析方法，（4）开发在每个时刻从可用的过程信息识别特殊原因的方法，（5）开发一种从每个时刻的可用过程信息预测可能的近期事故的方法，（6）有效地处理与高度相关的变量相关联的警报，以及（7）引入一种用于估计与未遂事故相关联的利润损失的计算有效的方法。将开发原型软件来测试新技术，并进行全公司范围的动态风险分析。这些方法将在液化空气集团通过模拟和实时在几个重要的工业过程中实施和测试。该项目的潜在影响包括社会、经济、技术和教育等。新方法将允许利用大型动态数据库进行更彻底的风险分析，提供更安全的加工厂，更一致地生产符合规格的产品，从而增加利润。这些方法和软件将提供给过程工业和大学的设计和控制课程。这些新的风险评估技术将在PI设计教科书的未来版本中提供更多量化的安全覆盖范围。虽然该项目侧重于处理厂的未遂事故和故障概率，但这些技术可以很容易地用于其他行业/组织，如航空，医疗保健和核工业。这项工作是多学科的性质，涉及化学工程师，风险分析师和统计学家。一些学生将在这个项目中接受培训。