Dynamic Risk Assessment of Inherently Safe Chemical Processes: Using Accident Precursor Data

本质安全化学过程的动态风险评估：使用事故前兆数据

• 批准号: 0553941
• 负责人: Warren Seider
• 金额: --
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0553941&HistoricalAwards=false
• 关键词: Dynamic; Risk; Assessment; Inherently; Safe

ABSTRACTPI: Warren D. Seider and Ulku Oktem Institution: University of PennsylvaniaProposal Number: 0553941Title: Dynamic Risk Assessment of Inherently Safe Chemical Processes: Using Accident Precursor DataIntellectual Merit. Chemical engineers, researchers, the chemical process industries, and regulators have focused on improving the safety of chemical plants since the accidents at Flixborough, Seveso, and Bhopal. In addition, due to terrorism concerns since 9/11, additional security standards have been applied to the chemical and petrochemical industries. It is therefore desirable to have inherent safety and security, and dynamic risk assessment and reliability as vital ingredients in the planning, development, design, control, and operations of chemical plants.This research project aims to develop:(i) Inherently safer plant (reactor-separator-recycle) designs using game theory: Initially, design techniques using game theory will be extended to design inherently safer polymerization reaction processes. These techniques involve shifting the operating regimes from unstable, non-minimum phase behavior (with inverse response) towards stable, minimum phase behavior (without inverse response) at comparable profitability levels, thereby enhancing inherent safety. Designs are obtained that account for the tradeoffs between profitability, controllability, safety and/or product quality, and flexibility, by solving a multi-objective optimization problem using game theory. Previous work will be extended to include distillation columns, tubular reactors, and fluidized-catalytic reactors, among other process units.(ii) Plant-specific, dynamic risk assessment techniques using accident precursor data: The PIs have developed a mathematical model to estimate the failure probabilities of various critical accident scenarios, associated with a process unit given an abnormal event, using probability theory, including copulas and Bayesian analysis. The method will be extended for plant-wide analysis and tested with chemical industries, including Rohm & Haas (which participates in the Wharton Risk Management Centers Near-miss Management Project [NMMP]), and the incident databases RMP & NRC, with accidents being investigated by the Chemical Safety and Hazard Investigation Board (CSB). Broader Impacts. This approach provides a dynamic method to perform risk and vulnerability assessment of chemical plants considering the uncertainty as well as the variability in failure probabilities. The technique, using high-speed computers, will permit more thorough safety analyses, providing safer chemical plants. The models and software will be used by the chemical industries and in design courses at the University of Pennsylvania. These risk-assessment techniques should lead to more quantitative safety coverage in the PIs design textbook. Although the project focuses on failure probabilities of chemical plants, these techniques can be easily extended to other industries/organizations where precursors are important. The work is multidisciplinary in nature involving chemical engineers, risk analysts, epidemiologists, and statisticians.

摘要：沃伦·D. Seider和Ulku Oktem研究所：宾夕法尼亚大学提案编号：0553941题目：固有安全化学过程的动态风险评估：使用事故前兆数据。自弗利克斯伯勒、塞维索和博帕尔事故以来，化学工程师、研究人员、化学加工行业和监管机构一直致力于提高化工厂的安全性。此外，由于自9/11以来对恐怖主义的关切，化学和石化工业采用了额外的安全标准。 因此，在规划、开发、设计、控制和操作化工厂时，最好将固有安全性和安保以及动态风险评估和可靠性作为重要组成部分。本研究项目旨在开发：（i）使用博弈论进行固有安全性工厂（反应器-分离器-再循环）设计：最初，将扩展使用博弈论的设计技术，以设计固有安全性更高的聚合反应过程。 这些技术涉及在可比较的盈利水平下将操作状态从不稳定的、非最小相位行为（具有逆响应）转变为稳定的、最小相位行为（没有逆响应），从而增强固有安全性。 设计获得占盈利能力，可控性，安全性和/或产品质量，和灵活性之间的权衡，通过使用博弈论解决多目标优化问题。 以前的工作将扩展到包括蒸馏塔，管式反应器和流化催化反应器，以及其他工艺单元。(ii)使用事故前兆数据的工厂特定的动态风险评估技术：PI开发了一个数学模型，用于估计与给定异常事件的过程单元相关的各种关键事故场景的故障概率，使用概率论，包括Copula和贝叶斯分析。 该方法将扩展到工厂范围内的分析，并在化工行业进行测试，包括Rohm哈斯（参与沃顿风险管理中心的未遂事件管理项目[NMMP]）和事故数据库RMP NRC，事故由化学安全和危害调查委员会（CSB）进行调查。更广泛的影响。该方法提供了一种动态的方法来执行风险和脆弱性评估的化工厂考虑的不确定性以及故障概率的变化。 该技术使用高速计算机，将允许更彻底的安全分析，提供更安全的化工厂。 这些模型和软件将用于化学工业和宾夕法尼亚大学的设计课程。 这些风险评估技术应导致更多的定量安全范围内的PI设计教科书。 虽然该项目侧重于化工厂的故障概率，但这些技术可以很容易地推广到前体很重要的其他行业/组织。这项工作是多学科的性质，涉及化学工程师，风险分析师，流行病学家和统计学家。