Pub Date : 2026-04-01Epub Date: 2025-12-15DOI: 10.1016/j.jlp.2025.105887
Lingfeng Wang , Haiyan Chen , Zhengdong Liu , Chang Li , Chunmiao Yuan
<div><div>Coal dust explosions pose a major threat to the safety of industrial processes involving coal handling and utilization (e.g., coal mining, coal processing, and coal-fired power generation). The Minimum Ignition Temperature (MIT), as a core parameter for evaluating the risk of coal dust explosions in industrial process safety management, is influenced by multiple coupled factors including water immersion time, coalification degree, volatile matter content, and particle size distribution. This study systematically investigates the mechanism by which coal dust characteristics affect the MIT in the context of industrial water-related coal handling processes and builds a multi-factor predictive model using experimental testing and machine learning methods—with the goal of providing a tool for process safety risk mitigation. The Godbert-Greenwald furnace was employed to measure the MIT of coal dust clouds under various water immersion conditions. Key influencing factors were identified through Pearson and Spearman correlation analyses, with a focus on their relevance to process parameter optimization. The XG-Boost algorithm was utilized to develop a predictive model with features such as water immersion time, volatile matter content, active functional group content, median particle size, dust cloud concentration, and wettability. The results indicate that the volatile matter content (Pearson coefficient −0.78, <em>p</em> < 0.001) and active functional group content (Spearman coefficient −0.71, <em>p</em> < 0.001) are strongly negatively correlated with MIT, serving as key determinants influencing MIT in coal-related industrial processes. Water immersion time shows a moderate negative correlation with MIT (Spearman coefficient −0.50, <em>p</em> < 0.001), with prolonged immersion reducing MIT by 60°C—this elucidates how moisture (a controllable process factor) changes the hydroxyl content and pore structure of coal dust surfaces, thereby lowering the activation energy of oxidation and increasing process safety risks. The XG-Boost model ranks feature importance as follows: volatile matter content > active functional group content > water immersion time > wettability > dust cloud concentration > median particle size—providing clear guidance for prioritizing process parameter monitoring. The determination coefficients (<em>R</em><sup><em>2</em></sup>) for the model training and testing datasets are 0.9999 and 0.9512, with average absolute errors (<em>MAE</em>) of 1.470 × 10<sup>−4</sup> and 1.647, demonstrating a high level of predictive accuracy for supporting real-time process safety decision-making. This study offers a theoretical foundation for the dynamic assessment of coal dust explosion risks in industrial processes with variable coal quality and controllable process parameters. It is advised that in industrial process safety practice, emphasis should be placed on monitoring volatile matter and active functional grou
{"title":"Model and mechanism of the impact of water immersion process on the minimum ignition temperature of coal dust","authors":"Lingfeng Wang , Haiyan Chen , Zhengdong Liu , Chang Li , Chunmiao Yuan","doi":"10.1016/j.jlp.2025.105887","DOIUrl":"10.1016/j.jlp.2025.105887","url":null,"abstract":"<div><div>Coal dust explosions pose a major threat to the safety of industrial processes involving coal handling and utilization (e.g., coal mining, coal processing, and coal-fired power generation). The Minimum Ignition Temperature (MIT), as a core parameter for evaluating the risk of coal dust explosions in industrial process safety management, is influenced by multiple coupled factors including water immersion time, coalification degree, volatile matter content, and particle size distribution. This study systematically investigates the mechanism by which coal dust characteristics affect the MIT in the context of industrial water-related coal handling processes and builds a multi-factor predictive model using experimental testing and machine learning methods—with the goal of providing a tool for process safety risk mitigation. The Godbert-Greenwald furnace was employed to measure the MIT of coal dust clouds under various water immersion conditions. Key influencing factors were identified through Pearson and Spearman correlation analyses, with a focus on their relevance to process parameter optimization. The XG-Boost algorithm was utilized to develop a predictive model with features such as water immersion time, volatile matter content, active functional group content, median particle size, dust cloud concentration, and wettability. The results indicate that the volatile matter content (Pearson coefficient −0.78, <em>p</em> < 0.001) and active functional group content (Spearman coefficient −0.71, <em>p</em> < 0.001) are strongly negatively correlated with MIT, serving as key determinants influencing MIT in coal-related industrial processes. Water immersion time shows a moderate negative correlation with MIT (Spearman coefficient −0.50, <em>p</em> < 0.001), with prolonged immersion reducing MIT by 60°C—this elucidates how moisture (a controllable process factor) changes the hydroxyl content and pore structure of coal dust surfaces, thereby lowering the activation energy of oxidation and increasing process safety risks. The XG-Boost model ranks feature importance as follows: volatile matter content > active functional group content > water immersion time > wettability > dust cloud concentration > median particle size—providing clear guidance for prioritizing process parameter monitoring. The determination coefficients (<em>R</em><sup><em>2</em></sup>) for the model training and testing datasets are 0.9999 and 0.9512, with average absolute errors (<em>MAE</em>) of 1.470 × 10<sup>−4</sup> and 1.647, demonstrating a high level of predictive accuracy for supporting real-time process safety decision-making. This study offers a theoretical foundation for the dynamic assessment of coal dust explosion risks in industrial processes with variable coal quality and controllable process parameters. It is advised that in industrial process safety practice, emphasis should be placed on monitoring volatile matter and active functional grou","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105887"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2025-12-17DOI: 10.1016/j.jlp.2025.105890
Tylee L. Kareck , Chi-Yang Li , Jiejia Wang , Michael J. Gollner , Qingsheng Wang
Modern data centers are becoming increasingly vital infrastructure, yet several recent high-profile fire incidents have exposed persistent vulnerabilities. As artificial intelligence (AI) technologies continue to advance, these risks will only intensify. Contributing causes of such fires include electrical faults, battery failures, cooling system malfunctions, and human error. This perspective paper synthesizes key information from recently reported incidents and discusses practical fire safety strategies for both prevention (i.e., AI-driven fault detection and fire-safe battery storage) and suppression (i.e., clean agents and liquid nitrogen system). Emerging technologies are highlighted as potential fire safety enhancements, and their development and implementation in modern data centers are recommended. Two relevant methods for fire risk assessment are explored, specifically non-scenario-based consideration of common fire causes and scenario-based examination of recent incidents. These assessment methods should be utilized while considering engineering design practices, operational feasibility, and regulatory alignment to enhance resilience and promote adoption in modern data centers. This work intends to offer a perspective on data center fire risk assessment by examining past incidents, presenting insights into current knowledge gaps, and proposing future research and stakeholder efforts for the improvement of data center fire safety.
{"title":"From incident to insight: Fire risk in modern data centers","authors":"Tylee L. Kareck , Chi-Yang Li , Jiejia Wang , Michael J. Gollner , Qingsheng Wang","doi":"10.1016/j.jlp.2025.105890","DOIUrl":"10.1016/j.jlp.2025.105890","url":null,"abstract":"<div><div>Modern data centers are becoming increasingly vital infrastructure, yet several recent high-profile fire incidents have exposed persistent vulnerabilities. As artificial intelligence (AI) technologies continue to advance, these risks will only intensify. Contributing causes of such fires include electrical faults, battery failures, cooling system malfunctions, and human error. This perspective paper synthesizes key information from recently reported incidents and discusses practical fire safety strategies for both prevention (i.e., AI-driven fault detection and fire-safe battery storage) and suppression (i.e., clean agents and liquid nitrogen system). Emerging technologies are highlighted as potential fire safety enhancements, and their development and implementation in modern data centers are recommended. Two relevant methods for fire risk assessment are explored, specifically non-scenario-based consideration of common fire causes and scenario-based examination of recent incidents. These assessment methods should be utilized while considering engineering design practices, operational feasibility, and regulatory alignment to enhance resilience and promote adoption in modern data centers. This work intends to offer a perspective on data center fire risk assessment by examining past incidents, presenting insights into current knowledge gaps, and proposing future research and stakeholder efforts for the improvement of data center fire safety.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105890"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.jlp.2026.105914
Xiao Tang , Xiaolong Luo , Baofeng Di , Bingwei Tian
Natural hazard triggered technological accidents, known as Natech events, are becoming increasingly frequent with the intensification of extreme weather, posing significant challenges to the risk management and response of such complex disasters. Therefore, the risk management and response to complex disasters represented by Natech events have become crucial components of contemporary risk management. In light of these challenges, this study conducted a field investigation on public perception of flood-triggered Natech events in the industrial areas of Luzhou, China. Guided by the Risk Information Seeking and Processing (RISP) model, the field investigation employed semi-structured street interviews (n = 33) to explore patterns of public risk perception and behavioral responses in Natech scenarios. The findings reveal that Individuals with dual identities (the workers at chemical and distillery factories and residents) tend to have a higher Natech risk perception. In addition, behavioral responses are influenced by risk perception, while decision-making is shaped by perceived barriers and response evaluations. Moreover, insufficient Natech risk information drives risk communication only when individuals are willing to reduce Natech risk but lack the information for effective responses. Lastly, a social expectation bias exists between actual Natech risk perception and behavioral responses and the intentions expressed in interviews. Within this sample, we identified several gaps in public Natech risk perception and response preparedness. Based on these gaps, several targeted recommendations are proposed. These measures will enable researchers to formulate more effective Natech risk communication strategies targeting public risk perception and behavioral responses.
{"title":"Public risk perception and behavioral responses to flood-triggered Natech Events: A field investigation in industrial areas of Luzhou, China","authors":"Xiao Tang , Xiaolong Luo , Baofeng Di , Bingwei Tian","doi":"10.1016/j.jlp.2026.105914","DOIUrl":"10.1016/j.jlp.2026.105914","url":null,"abstract":"<div><div>Natural hazard triggered technological accidents, known as Natech events, are becoming increasingly frequent with the intensification of extreme weather, posing significant challenges to the risk management and response of such complex disasters. Therefore, the risk management and response to complex disasters represented by Natech events have become crucial components of contemporary risk management. In light of these challenges, this study conducted a field investigation on public perception of flood-triggered Natech events in the industrial areas of Luzhou, China. Guided by the Risk Information Seeking and Processing (RISP) model, the field investigation employed semi-structured street interviews (n = 33) to explore patterns of public risk perception and behavioral responses in Natech scenarios. The findings reveal that Individuals with dual identities (the workers at chemical and distillery factories and residents) tend to have a higher Natech risk perception. In addition, behavioral responses are influenced by risk perception, while decision-making is shaped by perceived barriers and response evaluations. Moreover, insufficient Natech risk information drives risk communication only when individuals are willing to reduce Natech risk but lack the information for effective responses. Lastly, a social expectation bias exists between actual Natech risk perception and behavioral responses and the intentions expressed in interviews. Within this sample, we identified several gaps in public Natech risk perception and response preparedness. Based on these gaps, several targeted recommendations are proposed. These measures will enable researchers to formulate more effective Natech risk communication strategies targeting public risk perception and behavioral responses.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105914"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.jlp.2026.105916
Uiam Lee , Dal Jae Park
Chemical laboratories storing diverse chemicals in limited spaces face significant risks from incompatible chemical storage, which can lead to fires, explosions, and toxic gas releases during accidental spills or leaks. Although various institutions have developed their own segregation guidelines, no standardized method exists for quantitatively comparing these approaches or systematically optimizing storage under space constraints. This study develops the Chemical incompatibility Hazard Index (C.H.I.), a novel quantitative metric for evaluating mixed storage risks, and applies it to compare seven international segregation methods.
Using the CAMEO (Computer-Aided Management of Emergency Operations) Chemicals database, 52 chemicals from a Korean quantum dot synthesis laboratory were classified according to each method. The seven methods evaluated include the existing Korean regulatory-based approach, systems from Stanford University, Harvard University, Imperial College London, and Fred Hutchinson Cancer Research Center, the Merck classification system, and the National Oceanic and Atmospheric Administration (NOAA) reactive groups. Compatibility charts were generated for each method, and C.H.I. values were calculated based on the proportion of incompatible, caution, and compatible pairwise reactions within each storage group.
Among the seven methods, the NOAA reactive group-based approach yielded the lowest average C.H.I. (22.68), significantly outperforming the existing laboratory method (47.78). Subsequent optimization through consolidation of compatible groups to reduce storage locations and selective isolation of six high-reactivity chemicals (11.5 % of inventory) achieved a 71.6 % reduction in average C.H.I. (from 47.78 to 13.56) and a 55.4 % reduction in maximum C.H.I. (from 80.00 to 35.71).
This study establishes the first quantitative framework for comparing and optimizing chemical segregation methods. The C.H.I. methodology provides a reproducible approach applicable to diverse laboratory environments, particularly benefiting space-constrained research facilities seeking maximum safety improvement with minimal intervention.
{"title":"Comparative evaluation of chemical segregation methods based on chemical compatibility assessment in chemical laboratories","authors":"Uiam Lee , Dal Jae Park","doi":"10.1016/j.jlp.2026.105916","DOIUrl":"10.1016/j.jlp.2026.105916","url":null,"abstract":"<div><div>Chemical laboratories storing diverse chemicals in limited spaces face significant risks from incompatible chemical storage, which can lead to fires, explosions, and toxic gas releases during accidental spills or leaks. Although various institutions have developed their own segregation guidelines, no standardized method exists for quantitatively comparing these approaches or systematically optimizing storage under space constraints. This study develops the Chemical incompatibility Hazard Index (C.H.I.), a novel quantitative metric for evaluating mixed storage risks, and applies it to compare seven international segregation methods.</div><div>Using the CAMEO (Computer-Aided Management of Emergency Operations) Chemicals database, 52 chemicals from a Korean quantum dot synthesis laboratory were classified according to each method. The seven methods evaluated include the existing Korean regulatory-based approach, systems from Stanford University, Harvard University, Imperial College London, and Fred Hutchinson Cancer Research Center, the Merck classification system, and the National Oceanic and Atmospheric Administration (NOAA) reactive groups. Compatibility charts were generated for each method, and C.H.I. values were calculated based on the proportion of incompatible, caution, and compatible pairwise reactions within each storage group.</div><div>Among the seven methods, the NOAA reactive group-based approach yielded the lowest average C.H.I. (22.68), significantly outperforming the existing laboratory method (47.78). Subsequent optimization through consolidation of compatible groups to reduce storage locations and selective isolation of six high-reactivity chemicals (11.5 % of inventory) achieved a 71.6 % reduction in average C.H.I. (from 47.78 to 13.56) and a 55.4 % reduction in maximum C.H.I. (from 80.00 to 35.71).</div><div>This study establishes the first quantitative framework for comparing and optimizing chemical segregation methods. The C.H.I. methodology provides a reproducible approach applicable to diverse laboratory environments, particularly benefiting space-constrained research facilities seeking maximum safety improvement with minimal intervention.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105916"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-07DOI: 10.1016/j.jlp.2026.105921
Saksham Timalsina, Chengyi Zhang, Uttam Kumar Pal
Hydrogen infrastructure introduces complex safety and reliability challenges due to its unique physicochemical properties, which tightly connect technical, human, and operational elements involved. Addressing these challenges requires methods that incorporate real system behavior and quantify uncertainty rather than relying solely on theoretical hazard models. This study analyzes incidents from the H2Tools database and develops an integrated framework for reliability and probabilistic safety assessment of hydrogen systems. Root causes are categorized using an Ishikawa diagram, after which a risk-impact matrix evaluates the relative contribution of each failure mechanism. A Monte-Carlo uncertainty analysis improves assessment by characterizing uncertainty in risk severity and highlighting causes that may escalate under unfavorable conditions. Results show that human errors, procedural deficiencies, and technical failures dominate risk profiles, exhibiting significant uncertainty ranges that affect system-level reliability. To support practical safety engineering, the study compiles targeted engineering controls, operational standards, maintenance considerations, and training measures linked directly to the identified failure modes. The proposed framework transforms empirical incident data into a structured, reliability-oriented decision-support tool, offering actionable guidance for enhancing safety, resilience, and operational assurance in hydrogen energy systems.
{"title":"Quantifying reliability and uncertainty in hydrogen infrastructure through integrated incident analysis","authors":"Saksham Timalsina, Chengyi Zhang, Uttam Kumar Pal","doi":"10.1016/j.jlp.2026.105921","DOIUrl":"10.1016/j.jlp.2026.105921","url":null,"abstract":"<div><div>Hydrogen infrastructure introduces complex safety and reliability challenges due to its unique physicochemical properties, which tightly connect technical, human, and operational elements involved. Addressing these challenges requires methods that incorporate real system behavior and quantify uncertainty rather than relying solely on theoretical hazard models. This study analyzes incidents from the H2Tools database and develops an integrated framework for reliability and probabilistic safety assessment of hydrogen systems. Root causes are categorized using an Ishikawa diagram, after which a risk-impact matrix evaluates the relative contribution of each failure mechanism. A Monte-Carlo uncertainty analysis improves assessment by characterizing uncertainty in risk severity and highlighting causes that may escalate under unfavorable conditions. Results show that human errors, procedural deficiencies, and technical failures dominate risk profiles, exhibiting significant uncertainty ranges that affect system-level reliability. To support practical safety engineering, the study compiles targeted engineering controls, operational standards, maintenance considerations, and training measures linked directly to the identified failure modes. The proposed framework transforms empirical incident data into a structured, reliability-oriented decision-support tool, offering actionable guidance for enhancing safety, resilience, and operational assurance in hydrogen energy systems.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105921"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-07DOI: 10.1016/j.jlp.2026.105918
Shengli Kong , Qianyu Tan , Kai Zhou , Wei Wang , Ting Wang
Fire investigations often rely on visual evidence, which may be compromised by surveillance failures, occlusion, or image loss. Broadband acoustic signals, with strong penetration and temporal resolution, provide complementary clues about equipment conditions, explosions, and structural failures. Yet their non-stationary and overlapping characteristics hinder analysis using conventional methods. This study presents a machine learning-based broadband sound recognition approach, using grinding machines as representative fire hazards. Mel-frequency cepstral coefficients (MFCCs) and spectrogram gray-level co-occurrence matrix (GLCM) features were extracted to capture spectral and texture information, then fused for classification with XGBoost. Bayesian optimization was applied to adapt hyperparameters and improve robustness. The proposed model initially achieved 94.2 % accuracy and 91.5 % recall in multi-condition recognition using default hyperparameters. After applying Bayesian optimization to adapt hyperparameters and improve robustness, the model achieved 96.7 % accuracy and 93.3 % recall, outperforming support vector machines, random forests, and backpropagation neural networks. These results demonstrate the potential of broadband acoustic data to support fire investigations and provide a practical pathway for scene reconstruction and evidence enhancement.
{"title":"A machine learning-based approach for broadband sound recognition: A case study on investigating potential fire hazards of grinding machines","authors":"Shengli Kong , Qianyu Tan , Kai Zhou , Wei Wang , Ting Wang","doi":"10.1016/j.jlp.2026.105918","DOIUrl":"10.1016/j.jlp.2026.105918","url":null,"abstract":"<div><div>Fire investigations often rely on visual evidence, which may be compromised by surveillance failures, occlusion, or image loss. Broadband acoustic signals, with strong penetration and temporal resolution, provide complementary clues about equipment conditions, explosions, and structural failures. Yet their non-stationary and overlapping characteristics hinder analysis using conventional methods. This study presents a machine learning-based broadband sound recognition approach, using grinding machines as representative fire hazards. Mel-frequency cepstral coefficients (MFCCs) and spectrogram gray-level co-occurrence matrix (GLCM) features were extracted to capture spectral and texture information, then fused for classification with XGBoost. Bayesian optimization was applied to adapt hyperparameters and improve robustness. The proposed model initially achieved 94.2 % accuracy and 91.5 % recall in multi-condition recognition using default hyperparameters. After applying Bayesian optimization to adapt hyperparameters and improve robustness, the model achieved 96.7 % accuracy and 93.3 % recall, outperforming support vector machines, random forests, and backpropagation neural networks. These results demonstrate the potential of broadband acoustic data to support fire investigations and provide a practical pathway for scene reconstruction and evidence enhancement.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105918"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper discusses the evolution of the Barrier and Operational Risk Analysis (BORA) methodology into a more flexible tool by integrating fuzzy logic with a Bayesian Network (BN) framework to improve safety risk assessments in industrial processes. While BORA is traditionally used to assess the performance of safety barriers, it has limitations, particularly in dynamic risk assessment, handling dependencies, and managing uncertainties. To address these issues, fuzzy logic is applied to transform generic data into fuzzy sets, using the cumulative inverse method to derive crisp values using screened OREDA, ICSI, and SINTEF datasets supplemented by calibrated expert triplets to address data gaps and imprecision. This approach enables a more accurate representation of frequency and failure probability values. By incorporating a BN, the framework yields a versatile model capable of probabilistic reasoning. This enhancement enables real-time updates of risk levels by considering the interdependencies of safety barriers while incorporating the latest available data. The suggested approach involves transforming BORA into a network of probabilistic variables, enhancing predictive accuracy and decision-making processes. The importance of this approach is underscored through uncertainty and sensitivity analyses. A case study in the CP2K Unit Reactor showcases the practical benefits of using the fuzzy BORA-BN in industrial processes. The proposed method reduced the predicted overall accident frequency from 1.16 × 10−4 yr−1 to 3.03 × 10−7 yr−1, demonstrating improved uncertainty management.
{"title":"Enhancement of generic data in risk assessment using a fuzzy BORA and Bayesian network approach: Case study CP2K Unit reactor, SKIKDA","authors":"Abderraouf Bouafia , Mohammed Bougofa , Wafia Benhamlaoui , Amin Baziz , Ammar Chakhrit , Mounira Rouainia","doi":"10.1016/j.jlp.2026.105932","DOIUrl":"10.1016/j.jlp.2026.105932","url":null,"abstract":"<div><div>This paper discusses the evolution of the Barrier and Operational Risk Analysis (BORA) methodology into a more flexible tool by integrating fuzzy logic with a Bayesian Network (BN) framework to improve safety risk assessments in industrial processes. While BORA is traditionally used to assess the performance of safety barriers, it has limitations, particularly in dynamic risk assessment, handling dependencies, and managing uncertainties. To address these issues, fuzzy logic is applied to transform generic data into fuzzy sets, using the cumulative inverse method to derive crisp values using screened OREDA, ICSI, and SINTEF datasets supplemented by calibrated expert triplets to address data gaps and imprecision. This approach enables a more accurate representation of frequency and failure probability values. By incorporating a BN, the framework yields a versatile model capable of probabilistic reasoning. This enhancement enables real-time updates of risk levels by considering the interdependencies of safety barriers while incorporating the latest available data. The suggested approach involves transforming BORA into a network of probabilistic variables, enhancing predictive accuracy and decision-making processes. The importance of this approach is underscored through uncertainty and sensitivity analyses. A case study in the CP2K Unit Reactor showcases the practical benefits of using the fuzzy BORA-BN in industrial processes. The proposed method reduced the predicted overall accident frequency from 1.16 × 10<sup>−4</sup> yr<sup>−1</sup> to 3.03 × 10<sup>−7</sup> yr<sup>−1</sup>, demonstrating improved uncertainty management.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105932"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2025-12-04DOI: 10.1016/j.jlp.2025.105878
Zheng Duan , Bei Pei , Yuxuan Deng , Xinyi Li , Liang Wang , Chang Lu
Methane explosions in confined pipelines pose a persistent hazard in gas transmission and urban distribution systems, and explosion venting is a primary mitigation measure. However, the synergistic interaction between end and side vents and its influence on flame dynamics and overpressure relief remain insufficiently understood. This study experimentally investigates the coupled effects of end-vent area and side-vent position on the propagation of explosions in a 15 cm × 15 cm × 200 cm transparent acrylic duct filled with 9.5 % methane–air premixed gas. End vents with areas of 100, 64, and 36 cm2 are combined with side vents located 0.7, 1.0, and 1.3 m from the ignition end. High-speed imaging and pressure transducers are employed to capture flame evolution and explosion overpressure histories, and each condition is repeated three times to ensure reproducibility. Without side vents, the flame exhibits a staged evolution from spherical to finger-shaped, planar, and tulip flames. Decreasing end-vent area advances tulip-flame formation, reduces flame propagation velocity, and prolongs the overall propagation time due to stronger wave reflection and sustained internal overpressure. With coupled end–side venting, flame dynamics display a characteristic three-stage behavior: pressure-driven acceleration upstream of the side vent, pronounced deceleration as the flame traverses the vent owing to lateral mass discharge and momentum extraction, and mild re-acceleration near the end vent. The side vent markedly reduces peak explosion overpressure and the duration of high-pressure loading, and it effectively suppresses secondary overpressure peaks by discharging unburned mixture and reshaping the internal flow field. The mitigating effect of the side vent strengthens as the end-vent area decreases, because the end vent controls the pressure differential driving side-vent discharge. A dimensionless synergy coefficient Ψ, defined from the peak overpressures with single and dual vents, increases from 0.073 to 0.48 (a 558% enhancement) when the end-vent area is reduced from 100 to 36 cm², demonstrating strong nonlinear coupling between the vents. These findings elucidate the fluid–dynamic mechanism of synergistic venting and provide a quantitative basis for optimizing multi-point explosion venting configurations in industrial pipeline protection.
密闭管道中的甲烷爆炸对输气和城市配气系统造成了持续的危害，而爆炸通风是主要的缓解措施。然而，端侧通风口之间的协同作用及其对火焰动力学和超压释放的影响仍未得到充分的了解。实验研究了在15 cm × 15 cm × 200 cm填充9.5%甲烷-空气预混气体的透明丙烯酸管道中，端部通风口面积和侧部通风口位置对爆炸传播的耦合影响。末端通风口面积分别为100,64和36cm2，与位于0.7,1.0和1.3 m的点火端侧通风口相结合。采用高速成像和压力传感器捕捉火焰演变和爆炸超压历史，每种情况重复三次以确保再现性。没有侧面通风口，火焰表现出阶段性的演变，从球形到手指形，平面，和郁金香火焰。减小末端通风口面积可以促进郁金香火焰的形成，降低火焰的传播速度，并且由于更强的波反射和持续的内部超压而延长了整体传播时间。使用耦合的端侧通风，火焰动力学表现出一个特征的三级行为：压力驱动的加速在侧通风口上游，明显的减速火焰穿过通风口由于侧向质量排放和动量提取，和温和的再加速在末端通风口附近。侧通气孔显著降低爆炸超压峰值和高压加载持续时间，并通过排出未燃混合气和重塑内部流场有效抑制二次超压峰值。侧通气孔的缓解作用随着端通气孔面积的减小而增强，这是因为端通气孔控制着驱动侧通气孔排放的压差。当末端通风口面积从100 cm²减少到36 cm²时，由单通风口和双通风口峰值超压定义的无量纲协同系数Ψ从0.073增加到0.48（增加558%），表明通风口之间存在强烈的非线性耦合。研究结果阐明了协同通风的流体动力学机理，为工业管道保护多点爆炸通风配置优化提供了定量依据。
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Pub Date : 2026-04-01Epub Date: 2025-12-08DOI: 10.1016/j.jlp.2025.105879
Yan Li , Lingyuan Lan , Tianshuo Zhang , Yiqing Jia , Yixiao Zhang , Yu Shan , Yan Li , Kun Zhang , Jun Xie
Lithium-ion batteries serve as the core energy storage units in modern battery energy storage systems (BESS). However, their susceptibility to thermal runaway poses significant risks of fire and explosion, making safety a critical concern. To address this challenge, smoke detectors are commonly employed in BESS containers for early warning, which need to be connected via cables/lines for power and signal transmission. Nevertheless, the current arrangement of smoke detection systems predominantly relies on semi-quantitative experience and regulations, lacking a scientifically rational design methodology. This may lead to a smoke detection system with unsatisfied detection sensitivity or unnecessary wires. To overcome this limitation, an optimized layout design approach for smoke detection systems based on the black-winged kite algorithm (BKA) is proposed, aiming to minimize wiring length while fulfilling system response time requirements. First, a simulation model of the BESS container is established using fire dynamics simulator (FDS), and the smoke detector's obscuration rate over time is obtained. Second, an optimization model is constructed to achieve the technical and economic targets, which is reducing the wire length while guaranteeing the detection system's sensitivity. The BKA is utilized to derive the optimal smoke detector arrangement. Finally, the robustness of the detection system is evaluated based on the cumulative failure probability of detectors. Case studies demonstrate that, compared to traditional optimization algorithms, the BKA exhibits significant advantages in convergence speed and accuracy. The proposed method ensures that the system can reliably trigger an alarm within 10 s during a thermal runaway fire in any battery cabinet with the minimum number of detectors. The robustness analysis results confirm that even under detector failure conditions, the system can still maintain reliable alarm performance within 10 s. The proposed smoke detector layout design approach reduces installation costs by 33.39 % compared to an additional redundant detector. This study provides theoretical support and practical references for the scientific design of fire detection systems in BESS containers.
{"title":"Optimized smoke detector layout design approach for battery energy storage system containers based on black-winged kite algorithm","authors":"Yan Li , Lingyuan Lan , Tianshuo Zhang , Yiqing Jia , Yixiao Zhang , Yu Shan , Yan Li , Kun Zhang , Jun Xie","doi":"10.1016/j.jlp.2025.105879","DOIUrl":"10.1016/j.jlp.2025.105879","url":null,"abstract":"<div><div>Lithium-ion batteries serve as the core energy storage units in modern battery energy storage systems (BESS). However, their susceptibility to thermal runaway poses significant risks of fire and explosion, making safety a critical concern. To address this challenge, smoke detectors are commonly employed in BESS containers for early warning, which need to be connected via cables/lines for power and signal transmission. Nevertheless, the current arrangement of smoke detection systems predominantly relies on semi-quantitative experience and regulations, lacking a scientifically rational design methodology. This may lead to a smoke detection system with unsatisfied detection sensitivity or unnecessary wires. To overcome this limitation, an optimized layout design approach for smoke detection systems based on the black-winged kite algorithm (BKA) is proposed, aiming to minimize wiring length while fulfilling system response time requirements. First, a simulation model of the BESS container is established using fire dynamics simulator (FDS), and the smoke detector's obscuration rate over time is obtained. Second, an optimization model is constructed to achieve the technical and economic targets, which is reducing the wire length while guaranteeing the detection system's sensitivity. The BKA is utilized to derive the optimal smoke detector arrangement. Finally, the robustness of the detection system is evaluated based on the cumulative failure probability of detectors. Case studies demonstrate that, compared to traditional optimization algorithms, the BKA exhibits significant advantages in convergence speed and accuracy. The proposed method ensures that the system can reliably trigger an alarm within 10 s during a thermal runaway fire in any battery cabinet with the minimum number of detectors. The robustness analysis results confirm that even under detector failure conditions, the system can still maintain reliable alarm performance within 10 s. The proposed smoke detector layout design approach reduces installation costs by 33.39 % compared to an additional redundant detector. This study provides theoretical support and practical references for the scientific design of fire detection systems in BESS containers.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105879"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2025-12-24DOI: 10.1016/j.jlp.2025.105897
Borja Rengel, Virginie Dréan, Laurent Paris, Eric Guillaume
Hydrogen jet flames from accidental releases pose significant risks due to their extensive flame lengths, temperatures and associated radiation hazards. Various methodologies and tools have been developed to estimate the effects of hazardous jet fires, assessing the associated risks and enhancing the implementation of robust safety measures and mitigation strategies. This study assesses the predictive capabilities of two CFD tools, FDS and FLACS-Fire, in estimating thermal radiation from free horizontal hydrogen jet fires, utilizing 93 experimental heat flux measurements from literature. The findings increase confidence in CFD simulations, particularly before applying them to more complex scenarios, such as jet impingement on obstacles.
{"title":"Validation of FDS and FLACS-Fire codes against radiation from free horizontal hydrogen jet fires","authors":"Borja Rengel, Virginie Dréan, Laurent Paris, Eric Guillaume","doi":"10.1016/j.jlp.2025.105897","DOIUrl":"10.1016/j.jlp.2025.105897","url":null,"abstract":"<div><div>Hydrogen jet flames from accidental releases pose significant risks due to their extensive flame lengths, temperatures and associated radiation hazards. Various methodologies and tools have been developed to estimate the effects of hazardous jet fires, assessing the associated risks and enhancing the implementation of robust safety measures and mitigation strategies. This study assesses the predictive capabilities of two CFD tools, FDS and FLACS-Fire, in estimating thermal radiation from free horizontal hydrogen jet fires, utilizing 93 experimental heat flux measurements from literature. The findings increase confidence in CFD simulations, particularly before applying them to more complex scenarios, such as jet impingement on obstacles.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105897"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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