A systematic approach for identifying drivers of critical safety and establishing their hierarchy

IF 1.9 4区 工程技术 Q3 ENGINEERING, CHEMICAL
Mohammad Zaid Kamil, Faisal Khan, Paul Amyotte
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引用次数: 0

Abstract

Learning from incidents is a crucial step in preventing and mitigating adverse events. Incident databases offer valuable insights for safety management improvements by cause and contributing factors. However, extracting meaningful information from incident investigation reports poses a significant challenge. This study introduces a data-driven methodology to assess drivers of critical safety (DCS), which is essential for enhancing the safety of the process industries and protecting workers and the environment. Natural language processing (NLP) can offer automated, actionable insights from incident investigation reports. This automation is important in identifying DCS from incident reports to ensure proactive prevention and effective mitigation of risks, thereby protecting assets, workers, and the environment. Based on lagging safety indicators (causes or contributing factors), we aim to develop leading safety improvements to enhance the safety management system. A crucial step involves developing a DCS hierarchy to assess their role within the overall safety management framework. This hierarchy quantifies the driving and dependence power of each driver. The former refers to the number of drivers affected by each driver, while the latter determines the number of drivers impacted by each driver. This hierarchy facilitates resource allocation and determines each driver's effectiveness in safety management. The tool is developed and trained using the publicly available CSB database, a comprehensive source of incident investigation data. To further verify the model's effectiveness, it is tested and verified on an unseen database of 26 release incidents released by CSB in January 2025. The model successfully identifies the DCS responsible for each incident.

Abstract Image

一个系统的方法来识别关键安全的驱动程序和建立他们的层次
从事件中吸取教训是预防和减轻不良事件的关键步骤。事件数据库根据原因和促成因素为安全管理改进提供了有价值的见解。然而,从事件调查报告中提取有意义的信息是一项重大挑战。本研究介绍了一种数据驱动的方法来评估关键安全(DCS)的驱动因素,这对于提高过程工业的安全性和保护工人和环境至关重要。自然语言处理(NLP)可以从事件调查报告中提供自动化的、可操作的见解。这种自动化对于从事件报告中识别DCS非常重要,可以确保主动预防和有效降低风险,从而保护资产、工人和环境。根据滞后的安全指标(原因或促成因素),我们的目标是制定领先的安全改进措施,以加强安全管理体系。关键的一步是建立DCS层次结构,以评估其在整体安全管理框架中的作用。该层次结构量化了每个驾驶员的驾驶和依赖能力。前者是指每个司机所影响的司机数量,后者是指每个司机所影响的司机数量。这种层次结构有利于资源分配,并决定了每个驾驶员在安全管理中的有效性。该工具的开发和培训使用了公开的CSB数据库,这是一个全面的事件调查数据来源。为了进一步验证该模型的有效性,在CSB于2025年1月发布的26个释放事件的未公开数据库中进行了测试和验证。该模型成功地确定了负责每个事件的DCS。
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来源期刊
Canadian Journal of Chemical Engineering
Canadian Journal of Chemical Engineering 工程技术-工程:化工
CiteScore
3.60
自引率
14.30%
发文量
448
审稿时长
3.2 months
期刊介绍: The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.
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