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: 1066475
• 负责人: Warren Seider
• 金额: $ 35.2万
• 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: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1066475&HistoricalAwards=false
• 关键词: Collaborative; Research; GOALI; Synergistic; Improvement

1066475SeiderIntellectual 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 PIs 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.

1066475SeiderIntlectual值得化学和石油行业和监管机构，一直在提高加工厂的安全性，尤其是在每一次新事故中，例如墨西哥湾，德克萨斯城，弗里克斯伯勒，塞维索和博帕尔的事故。此外，EPA，美国化学。理事会，桑迪亚·纳特（Sandia Natl）。实验室，美国海岸警卫队和国土安全部，已将安全标准添加到现有的安全法规[OSHA流程安全管理（PSM），EPA风险管理计划（RMP）]中，该法规适用于化学和石油行业。尽管做出了这些努力，但与飞机，军事和核工业相比，这些行业对准确的风险和脆弱性评估的关注较少。除社会和法律上的并发症外，可能危及公司存在的人类生命和经济损失的潜力增加了对拥有固有安全性和安全性的愿望，以及作为处理工厂计划，开发，设计，控制和运营的重要要求，并具有动态的风险评估和可靠性。 PIS开发了一种数学模型，以使用COPULAS和BAYESIAN分析来估计与化学过程异常事件和事故前体数据相关的各种关键事故情景的失败概率。他们扩展了该模型，以利用大型分布式控制系统（DC）和紧急关闭（ESD）系统数据库，涉及与工业流体催化裂缝单元相关的警报数据。这样，他们开发了用于估计绩效指标，进行警报系统分析的新方法，并估算了关闭（Trips）和事故的主要指标，以协助过程操作员和管理层识别近乎失误并进行调整以防止发生危险且昂贵的事件。在这项研究中，他们将介绍和研究新方法，以与液体液化厂合作测试化学植物的动态风险评估，并与DE纽瓦克的空中研究和开发合作测试。在稳定的操作和启动过程中，将使用DCS和ESD系统数据库对该方法进行测试。最初，他们将专门与安全数据合作。逐渐地，他们将利用产品质量数据来识别近乎缺点并更有效地防止事故。也就是说，以协同的方式实现改进的过程安全性和产品质量。 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每次瞬间，（6）有效地处理与高度相关变量相关的警报，以及（7）引入一种计算效率的方法，以估计与近使人相关的损益。将开发原型软件来测试新技术并执行公司范围内的动态风险分析。这些方法将通过模拟和实时在空气中实施几个重要的工艺进行实施和测试。该项目的潜在影响更广泛的影响是社会，经济，技术和教育等。这些新方法将利用大型动态数据库提供更彻底的风险分析，从而提供更安全的加工厂，从而更稳定地生产出规范产品，从而增加利润。该方法和软件将用于工艺行业以及大学的设计和控制课程。这些新的风险评估技术将在PIS设计教科书的未来版本中导致更定量的安全覆盖范围。尽管该项目侧重于加工厂的近乎缺乏症和失败概率，但这些技术可以轻松地用于其他行业/组织，例如航空，医疗保健和核行业。这项工作本质上是涉及化学工程师，风险分析师和统计学家的多学科。将在这个项目中对几个学生进行培训。