{"title":"A systematic approach for identifying drivers of critical safety and establishing their hierarchy","authors":"Mohammad Zaid Kamil, Faisal Khan, Paul Amyotte","doi":"10.1002/cjce.70029","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 10","pages":"4628-4646"},"PeriodicalIF":1.9000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.70029","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cjce.70029","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
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.
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.