Considering the uncertainty and fuzziness of the risk assessment index of coal mine gas explosion, a risk assessment model of gas explosion based on combinatorial weighting‐unascertained measure of safety information loss is proposed. First, 20 risk indicators are extracted from the six aspects of supply loss, transformation loss, transmission loss, perceived loss, cognition loss, and response loss in the process of safety information flow. The weight vector is constructed by the G1 method and anti‐entropy weight combination weighting method. Then, the single‐index measurement function is used to process the risk index measure. Based on the classification standard of the gas explosion risk assessment index, the single‐index and multi‐index measurement matrices are constructed. And the grade is judged according to the principle of maximum membership. Finally, a mine is selected for case application. The results show that the evaluation results are consistent with the actual situation and the method has certain feasibility. It provides a new idea and method for advanced control and accident prevention of coal mine gas explosion risk.
{"title":"Risk assessment method in relation to coal mine gas explosion based on safety information loss","authors":"Huimin Guo, Lianhua Cheng, Shugang Li, Bolin Jiang","doi":"10.1002/prs.12601","DOIUrl":"https://doi.org/10.1002/prs.12601","url":null,"abstract":"Considering the uncertainty and fuzziness of the risk assessment index of coal mine gas explosion, a risk assessment model of gas explosion based on combinatorial weighting‐unascertained measure of safety information loss is proposed. First, 20 risk indicators are extracted from the six aspects of supply loss, transformation loss, transmission loss, perceived loss, cognition loss, and response loss in the process of safety information flow. The weight vector is constructed by the G1 method and anti‐entropy weight combination weighting method. Then, the single‐index measurement function is used to process the risk index measure. Based on the classification standard of the gas explosion risk assessment index, the single‐index and multi‐index measurement matrices are constructed. And the grade is judged according to the principle of maximum membership. Finally, a mine is selected for case application. The results show that the evaluation results are consistent with the actual situation and the method has certain feasibility. It provides a new idea and method for advanced control and accident prevention of coal mine gas explosion risk.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"23 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140302051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ionic liquids (ILs) are highly favored in the oil and gas industry as gas hydrate inhibitors due to their dual functionality as thermodynamic inhibitor and kinetic hydrate inhibitor. Though known as the “green alternatives,” concerns about the effects of ILs in the environment are rising such that ILs can stabilize in water systems. Furthermore, there are insufficient data on the toxicity of ILs, limiting the use of ILs for industrial applications. Ridge, LASSO, decision tree, random forest, extra tree, gradient boost, and support vector regressions were used to develop IL toxicity predictive models. Random forest yielded the strongest predictive performance, scoring the highest R2 value of 0.86, with mean absolute error and root mean square error values of 0.32 and 0.43, respectively. Feature selections were conducted to investigate the contributions of the five molecular descriptors involved in developing regression models in this work. Descriptor MSDC was found to contribute the highest at 67% in predicting the toxicity of ILs, followed by SNarA and MAXDPC, demonstrating contributions of 15.2% and 14.1%, respectively. Further quantitative structure–activity relationship model validations were executed; the use of three descriptors resulted in a 2% increase in predictive performance for decision tree regression, whereas R2 values remained the same for random forest, extra tree, and gradient boosting.
离子液体（ILs）具有热力学抑制剂和动力学水合物抑制剂的双重功能，因此作为天然气水合物抑制剂在石油和天然气行业备受青睐。虽然离子液体被称为 "绿色替代品"，但人们对其环境影响的担忧也在增加，因为离子液体可在水系统中稳定存在。此外，有关 ILs 毒性的数据不足，限制了 ILs 在工业中的应用。研究人员使用 Ridge、LASSO、决策树、随机森林、额外树、梯度提升和支持向量回归等方法建立了惰性气体毒性预测模型。随机森林的预测性能最强，R2 值最高，为 0.86，平均绝对误差和均方根误差值分别为 0.32 和 0.43。对特征进行了选择，以研究本研究中建立回归模型所涉及的五个分子描述符的贡献。结果发现，描述符 MSDC 在预测 IL 毒性方面的贡献率最高，达到 67%，其次是 SNarA 和 MAXDPC，贡献率分别为 15.2% 和 14.1%。进一步对定量结构-活性关系模型进行了验证；使用三个描述符后，决策树回归的预测性能提高了 2%，而随机森林、额外树和梯度提升的 R2 值保持不变。
{"title":"Prediction of ionic liquids toxicity using machine learning models for application to gas hydrate","authors":"Nurul Hannah Abdullah, Dzulkarnain Zaini, Bhajan Lal","doi":"10.1002/prs.12599","DOIUrl":"https://doi.org/10.1002/prs.12599","url":null,"abstract":"Ionic liquids (ILs) are highly favored in the oil and gas industry as gas hydrate inhibitors due to their dual functionality as thermodynamic inhibitor and kinetic hydrate inhibitor. Though known as the “green alternatives,” concerns about the effects of ILs in the environment are rising such that ILs can stabilize in water systems. Furthermore, there are insufficient data on the toxicity of ILs, limiting the use of ILs for industrial applications. Ridge, LASSO, decision tree, random forest, extra tree, gradient boost, and support vector regressions were used to develop IL toxicity predictive models. Random forest yielded the strongest predictive performance, scoring the highest <jats:italic>R</jats:italic><jats:sup>2</jats:sup> value of 0.86, with mean absolute error and root mean square error values of 0.32 and 0.43, respectively. Feature selections were conducted to investigate the contributions of the five molecular descriptors involved in developing regression models in this work. Descriptor MSD<jats:sup>C</jats:sup> was found to contribute the highest at 67% in predicting the toxicity of ILs, followed by SNar<jats:sup>A</jats:sup> and MAXDP<jats:sup>C</jats:sup>, demonstrating contributions of 15.2% and 14.1%, respectively. Further quantitative structure–activity relationship model validations were executed; the use of three descriptors resulted in a 2% increase in predictive performance for decision tree regression, whereas <jats:italic>R</jats:italic><jats:sup>2</jats:sup> values remained the same for random forest, extra tree, and gradient boosting.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"86 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sophie Welch, Sungmin Youn, Andrew Nichols, Sukjoon Na, Ruiqing Shen
Railway transportation of hazardous materials (HAZMAT) is common and is generally considered safe. However, transporting toxic, flammable, and explosive substances via railways carries significant risk due to their high volume, proximity to populated areas, low public awareness, and potential domino effect. Particularly, the practice has come into question after a Norfolk Southern train derailed in Ohio in February of 2023. This derailment continuously reminds the public and the industry that such incidents can profoundly affect a community, critical infrastructure, and the environment. To identify the root cause of the Ohio train derailment and discover the deficiency of the safety system applied, combining the cause mapping approach with the safety triad concept was employed in this study. Based on this approach and the preliminary incident investigation released by the National Transportation Safety Board, the incident sequence is established, and the causal events leading to this incident are identified in the three essential pillars of its safety system: prevention, mitigation, and response, respectively. The study subsequently develops recommendations to improve the safety system of HAZMAT freight trains. This is expected to lower further the probability and consequence of HAZMAT freight train incidents and ultimately result in long‐term changes in railroad transportation.
{"title":"Transportation of hazardous material via railroad: Incident investigation and a case study of derailment in 2023","authors":"Sophie Welch, Sungmin Youn, Andrew Nichols, Sukjoon Na, Ruiqing Shen","doi":"10.1002/prs.12598","DOIUrl":"https://doi.org/10.1002/prs.12598","url":null,"abstract":"Railway transportation of hazardous materials (HAZMAT) is common and is generally considered safe. However, transporting toxic, flammable, and explosive substances via railways carries significant risk due to their high volume, proximity to populated areas, low public awareness, and potential domino effect. Particularly, the practice has come into question after a Norfolk Southern train derailed in Ohio in February of 2023. This derailment continuously reminds the public and the industry that such incidents can profoundly affect a community, critical infrastructure, and the environment. To identify the root cause of the Ohio train derailment and discover the deficiency of the safety system applied, combining the cause mapping approach with the safety triad concept was employed in this study. Based on this approach and the preliminary incident investigation released by the National Transportation Safety Board, the incident sequence is established, and the causal events leading to this incident are identified in the three essential pillars of its safety system: prevention, mitigation, and response, respectively. The study subsequently develops recommendations to improve the safety system of HAZMAT freight trains. This is expected to lower further the probability and consequence of HAZMAT freight train incidents and ultimately result in long‐term changes in railroad transportation.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"163 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nurul Atikah Azmi, Hanida Abdul Aziz, Syamsul Bahari Abdullah, Norhuda Abdul Manaf, Lian See Tan
Fat, oil, and grease (FOG), a deposit in wastewater, develops from any processes in food and processing industries. FOG deposits in wastewater management systems can cause blockages in sewer systems. Grease Interceptor (GI) is one of the control measures to treat and prevent the blockage of FOG deposits in the sewer system. Nevertheless, few studies evaluate the extended hazard of GI application in FOG treatment. This study aims to assess the risk of GI in FOG treatment by using a bowtie technique. The hazard identified is FOG itself, whereas the top event was GI failure. The high contributions of the threats were an unsuitable variation of GI's type and design, no GI cleaning process schedule, and a clogged outflow pipe from GI. GI design according to standard and cleaning FOG deposits in GI by a licensed grease hauler were two suitable preventive barriers. The desludging process is essential to guarantee that the GI operates smoothly and is identified as an excellent recovery barrier. Inter-rater reliability (IRR) analysis to measure the consistency of their ratings on the data provided. The experts had a 75% overall agreement value of IRR risk rating value on assets, while 50% agreement value on people.
脂肪、油和油脂（FOG）是废水中的沉淀物，产生于食品和加工行业的任何加工过程。废水管理系统中的油脂沉积物会导致下水道系统堵塞。油脂拦截器（GI）是处理和防止下水道系统中油脂沉积物堵塞的控制措施之一。然而，很少有研究评估在处理油脂时使用隔油池的扩展危害。本研究旨在使用弓形技术评估在处理 FOG 时使用 GI 的风险。已确定的危害是 FOG 本身，而首要事件是 GI 故障。威胁程度较高的是消化道类型和设计的不适当变化、没有消化道清洗流程计划以及消化道流出管道堵塞。按照标准设计消化池和由持证油脂运输商清理消化池中的沉淀油脂是两个合适的预防屏障。清淤过程是保证消化道顺利运行的关键，也被认为是极佳的回收屏障。专家之间的可靠性（IRR）分析用于衡量他们对所提供数据的评分是否一致。专家们对资产的 IRR 风险评级值的总体一致值为 75%，而对人员的一致值为 50%。
{"title":"Evaluation of risk associated with treatment of fat, oil, and grease: Grease interceptor from food processing industry effluent using bowtie analysis","authors":"Nurul Atikah Azmi, Hanida Abdul Aziz, Syamsul Bahari Abdullah, Norhuda Abdul Manaf, Lian See Tan","doi":"10.1002/prs.12600","DOIUrl":"https://doi.org/10.1002/prs.12600","url":null,"abstract":"Fat, oil, and grease (FOG), a deposit in wastewater, develops from any processes in food and processing industries. FOG deposits in wastewater management systems can cause blockages in sewer systems. Grease Interceptor (GI) is one of the control measures to treat and prevent the blockage of FOG deposits in the sewer system. Nevertheless, few studies evaluate the extended hazard of GI application in FOG treatment. This study aims to assess the risk of GI in FOG treatment by using a bowtie technique. The hazard identified is FOG itself, whereas the top event was GI failure. The high contributions of the threats were an unsuitable variation of GI's type and design, no GI cleaning process schedule, and a clogged outflow pipe from GI. GI design according to standard and cleaning FOG deposits in GI by a licensed grease hauler were two suitable preventive barriers. The desludging process is essential to guarantee that the GI operates smoothly and is identified as an excellent recovery barrier. Inter-rater reliability (IRR) analysis to measure the consistency of their ratings on the data provided. The experts had a 75% overall agreement value of IRR risk rating value on assets, while 50% agreement value on people.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"149 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140172680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This assessment tool was created to generate a decision‐making support system and assessment approach for a post‐fire (aftermath) and blast explosion impact. It uses a multidecision analysis neural network. Multidecision analysis is very crucial as the judgment of the assessment relies not only on engineering input but also on many contributing factors, such as lead time for replacement. This article outlines the framework for post‐incident explosion and fire‐damaged assessment using evaluation acceptance criteria and judgment that streamlines the process. Grounded theory (GT) and fuzzy cognitive mapping (FCM) techniques are presented and evaluated. Overall, this study aims to develop how the project organizations can ensure safety and asset integrity decision‐making by identifying hindrances and later developing a multicriteria analysis framework for the post‐incident assessment phase of implementation.
{"title":"Decision‐making analysis in post‐fire and explosion aftermath assessment tool: A fuzzy cognitive mapping approach","authors":"Hasnolizar Zakaria, Masdi Muhammad","doi":"10.1002/prs.12586","DOIUrl":"https://doi.org/10.1002/prs.12586","url":null,"abstract":"This assessment tool was created to generate a decision‐making support system and assessment approach for a post‐fire (aftermath) and blast explosion impact. It uses a multidecision analysis neural network. Multidecision analysis is very crucial as the judgment of the assessment relies not only on engineering input but also on many contributing factors, such as lead time for replacement. This article outlines the framework for post‐incident explosion and fire‐damaged assessment using evaluation acceptance criteria and judgment that streamlines the process. Grounded theory (GT) and fuzzy cognitive mapping (FCM) techniques are presented and evaluated. Overall, this study aims to develop how the project organizations can ensure safety and asset integrity decision‐making by identifying hindrances and later developing a multicriteria analysis framework for the post‐incident assessment phase of implementation.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"24 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Kryštof, Petr Adamec, Luboš Kotek, Zuzana Tichá, Petr Trávníček
A key element in learning from accidents is the skill associated with the transfer of knowledge gained by the operator from historical incidents. These incidents can include accidents and near-misses that occurred on site or in similar companies outside the plant. Knowledge transfer within the enterprise can be supported by a suitable framework or model that is easily understood by a wide range of people who are interested in the lessons learned from accidents. The application of some of the knowledge transfer models used so far can be quite time consuming and uncomfortable for the participants. For this reason, this paper aims to propose a simple model designed to support knowledge transfer. This model is proposed based on the widely used PDCA (plan-do-check-act) framework. Its use is demonstrated by the case of a major accident that occurred in the Czech Republic. The model can be used not only for learning from major accidents that occur in the subject company but also for learning from near-misses or events that occurred in the past in similar plants. Thus, the model can easily help in increasing the efficiency of the accident-learning system in particular.
{"title":"Supporting the transfer of knowledge in high-risk major accident environment","authors":"David Kryštof, Petr Adamec, Luboš Kotek, Zuzana Tichá, Petr Trávníček","doi":"10.1002/prs.12594","DOIUrl":"https://doi.org/10.1002/prs.12594","url":null,"abstract":"A key element in learning from accidents is the skill associated with the transfer of knowledge gained by the operator from historical incidents. These incidents can include accidents and near-misses that occurred on site or in similar companies outside the plant. Knowledge transfer within the enterprise can be supported by a suitable framework or model that is easily understood by a wide range of people who are interested in the lessons learned from accidents. The application of some of the knowledge transfer models used so far can be quite time consuming and uncomfortable for the participants. For this reason, this paper aims to propose a simple model designed to support knowledge transfer. This model is proposed based on the widely used PDCA (plan-do-check-act) framework. Its use is demonstrated by the case of a major accident that occurred in the Czech Republic. The model can be used not only for learning from major accidents that occur in the subject company but also for learning from near-misses or events that occurred in the past in similar plants. Thus, the model can easily help in increasing the efficiency of the accident-learning system in particular.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"28 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hans Pasman, Edison Sripaul, Faisal Khan, Bruno Fabiano
Decarbonization and energy transition will have a large impact on how our mobility, as well as supply of materials for clothing, building, and construction, and even for food, will be energized and processed. This review will give a brief survey of a few main changes and what they mean for process safety. It concerns hazards of hydrogen production and handling, battery systems, and electrification of the process industry, which will bring a range of changes, some easy, others intricate. Coincidentally, three recent papers in the journal Ergonomics prove to be in line with these process safety challenges.
{"title":"Energy transition technology comes with new process safety challenges and risks—What does it mean?","authors":"Hans Pasman, Edison Sripaul, Faisal Khan, Bruno Fabiano","doi":"10.1002/prs.12593","DOIUrl":"https://doi.org/10.1002/prs.12593","url":null,"abstract":"Decarbonization and energy transition will have a large impact on how our mobility, as well as supply of materials for clothing, building, and construction, and even for food, will be energized and processed. This review will give a brief survey of a few main changes and what they mean for process safety. It concerns hazards of hydrogen production and handling, battery systems, and electrification of the process industry, which will bring a range of changes, some easy, others intricate. Coincidentally, three recent papers in the journal <i>Ergonomics</i> prove to be in line with these process safety challenges.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"78 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139969754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Al-Baraa Abdulrahman Al-Mekhlafi, Ahmad Shahrul Nizam Isha, Ahmad Sobri Hashim
Fatigue is a significant concern in the offshore oil and gas industry, posing risks to both resources and lives. This study aims to assess fatigue levels among daily trippers on offshore platforms and compare them across different job roles. The Offshore Fatigue Assessment Application (OFAA) was developed to measure fatigue in Malaysian offshore workers, focusing on alert response and lapses. A total of 51 participants were involved, with 31 in Crew A and 20 in Crew B. Based on the results, Crew A exhibited fatigue, with only 3 out of 12 data collection points showing an average reaction time (RT) below 500 ms, mainly during post-work periods. Crew B also experienced fatigue, with only 1 out of 12 data collection points showing an average RT below 500 ms, occurring during mid-work. While Crew B displayed fatigue during pre-work and mid-work, they generally maintained alertness during their sessions. Overall, Crew A consistently faced lapses in alertness, while Crew B showed fatigue during pre-work and mid-work but remained alert during their sessions. These findings highlight varying fatigue levels among offshore personnel, emphasizing the need for targeted fatigue management strategies in this industry.
疲劳是近海石油和天然气行业的一个重大问题，对资源和生命都构成风险。本研究旨在评估海上平台每日出海工人的疲劳程度，并对不同工作角色的疲劳程度进行比较。开发了海上疲劳评估应用程序（OFAA）来测量马来西亚海上工人的疲劳程度，重点是警报反应和失误。结果显示，船员 A 表现出疲劳，12 个数据采集点中只有 3 个点的平均反应时间（RT）低于 500 毫秒，主要集中在下班后时段。船员 B 也出现了疲劳现象，12 个数据收集点中只有 1 个点的平均反应时间低于 500 毫秒，且发生在工作中期。虽然 B 组人员在工作前和工作中表现出疲劳，但他们在工作过程中一般都能保持警觉。总体而言，船员 A 始终面临警觉性下降的问题，而船员 B 在工作前和工作中表现出疲劳，但在工作过程中仍保持警觉。这些发现凸显了近海作业人员疲劳程度的差异，强调了在这一行业中制定有针对性的疲劳管理策略的必要性。
{"title":"Developing offshore fatigue assessment application to measure fatigue among offshore workers in Malaysia","authors":"Al-Baraa Abdulrahman Al-Mekhlafi, Ahmad Shahrul Nizam Isha, Ahmad Sobri Hashim","doi":"10.1002/prs.12592","DOIUrl":"https://doi.org/10.1002/prs.12592","url":null,"abstract":"Fatigue is a significant concern in the offshore oil and gas industry, posing risks to both resources and lives. This study aims to assess fatigue levels among daily trippers on offshore platforms and compare them across different job roles. The Offshore Fatigue Assessment Application (OFAA) was developed to measure fatigue in Malaysian offshore workers, focusing on alert response and lapses. A total of 51 participants were involved, with 31 in Crew A and 20 in Crew B. Based on the results, Crew A exhibited fatigue, with only 3 out of 12 data collection points showing an average reaction time (RT) below 500 ms, mainly during post-work periods. Crew B also experienced fatigue, with only 1 out of 12 data collection points showing an average RT below 500 ms, occurring during mid-work. While Crew B displayed fatigue during pre-work and mid-work, they generally maintained alertness during their sessions. Overall, Crew A consistently faced lapses in alertness, while Crew B showed fatigue during pre-work and mid-work but remained alert during their sessions. These findings highlight varying fatigue levels among offshore personnel, emphasizing the need for targeted fatigue management strategies in this industry.","PeriodicalId":20680,"journal":{"name":"Process Safety Progress","volume":"43 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139968329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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