Pub 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":"2025-12-24","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}
Pub Date : 2025-12-24DOI: 10.1016/j.jlp.2025.105905
Kaixin Shen , Meng Lan , Siqi Du , Yiping Bai , Jialin Wu , Wenguo Weng
The increasing frequency of natural disasters, particularly typhoons, exacerbates safety challenges for the process industry. While prior studies have concentrated on direct physical damage risks, the external risks arising from power system dependence were largely overlooked. One major challenge lies in the computational burden of large-scale scenario sets generated under disaster uncertainties, which makes detailed outage analysis infeasible. As a mainstream solution, existing scenario reduction techniques typically operate from a downstream, consequence-centric perspective (e.g., selecting representatives by damaged-facility counts). Such approaches often overlook the underlying temporal and spatial patterns of typhoon events and limit the suitability of the reduced set for complex risk assessments. To address these challenges, this study proposes a novel disaster-centric scenario reduction framework based on a Transformer-based Variational Autoencoder (VAE). The framework leverages a Transformer encoder to capture long-range temporal correlations in typhoon time series and employs the VAE to extract latent patterns, thereby enabling efficient compression of multi-dimensional typhoon scenario collections. A semi-supervised mechanism is integrated to leverage historical extreme cases to strengthen detection of extreme events. A subsequent two-stage strategy, combining anomaly detection and clustering, explicitly retains detected extremes while compressing non-extreme scenarios. Validation demonstrates that the selected representative scenarios effectively preserve the original outage risk distribution while reducing the scenario set by over 70 %. The validated representative set was then integrated with a purpose-built spatiotemporal risk metric, the Outage-Duration-Exceedance Probability (ODEP), to support in-depth analyses of power reliability and backup allocation for industrial parks. A comparison between disaster-induced and random-failure modes reveals significant systemic differences, highlighting the deficiencies of applying random-failure models to disaster-related outage risk assessment in industrial safety. Through this cross-system perspective, the proposed methodology provides an advanced and reliable solution for disaster-related safety risk management in the process industry.
{"title":"Process safety risk assessment against natural disasters: A cross-system scenario analysis perspective","authors":"Kaixin Shen , Meng Lan , Siqi Du , Yiping Bai , Jialin Wu , Wenguo Weng","doi":"10.1016/j.jlp.2025.105905","DOIUrl":"10.1016/j.jlp.2025.105905","url":null,"abstract":"<div><div>The increasing frequency of natural disasters, particularly typhoons, exacerbates safety challenges for the process industry. While prior studies have concentrated on direct physical damage risks, the external risks arising from power system dependence were largely overlooked. One major challenge lies in the computational burden of large-scale scenario sets generated under disaster uncertainties, which makes detailed outage analysis infeasible. As a mainstream solution, existing scenario reduction techniques typically operate from a downstream, consequence-centric perspective (e.g., selecting representatives by damaged-facility counts). Such approaches often overlook the underlying temporal and spatial patterns of typhoon events and limit the suitability of the reduced set for complex risk assessments. To address these challenges, this study proposes a novel disaster-centric scenario reduction framework based on a Transformer-based Variational Autoencoder (VAE). The framework leverages a Transformer encoder to capture long-range temporal correlations in typhoon time series and employs the VAE to extract latent patterns, thereby enabling efficient compression of multi-dimensional typhoon scenario collections. A semi-supervised mechanism is integrated to leverage historical extreme cases to strengthen detection of extreme events. A subsequent two-stage strategy, combining anomaly detection and clustering, explicitly retains detected extremes while compressing non-extreme scenarios. Validation demonstrates that the selected representative scenarios effectively preserve the original outage risk distribution while reducing the scenario set by over 70 %. The validated representative set was then integrated with a purpose-built spatiotemporal risk metric, the Outage-Duration-Exceedance Probability (ODEP), to support in-depth analyses of power reliability and backup allocation for industrial parks. A comparison between disaster-induced and random-failure modes reveals significant systemic differences, highlighting the deficiencies of applying random-failure models to disaster-related outage risk assessment in industrial safety. Through this cross-system perspective, the proposed methodology provides an advanced and reliable solution for disaster-related safety risk management in the process industry.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105905"},"PeriodicalIF":4.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880061","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 : 2025-12-23DOI: 10.1016/j.jlp.2025.105898
Yi Yang , Jikuo Zhang , Guanxia Zheng , Yun-Ting Tsai
The suitability and rationality of fire and rescue equipment allocation are central to effectively responding to regional fires and emergency operations. However, current fire station equipment deployment in China is largely based on standardized national guidelines, with limited consideration of regional variations and local risk profiles. This study integrates fuzzy set-valued statistics for demand assessment and AnyLogic agent-based modelling for dynamic simulation. This method is based on the Standard for Construction of Urban Fire Station in China. It combines local fire safety risk assessments, disaster and accident risk analyses, and evaluations of existing firefighting equipment to determine the actual needs for firefighting and rescue resources. These factors are then used to derive formulas that calculate both the required quantities and the prioritization of equipment allocation in each region. The result is a customized allocation framework that is adaptable to the unique operational conditions and risks of different regions.
{"title":"Optimization method for fire rescue equipment allocation based on fuzzy needs assessment and AnyLogic simulation","authors":"Yi Yang , Jikuo Zhang , Guanxia Zheng , Yun-Ting Tsai","doi":"10.1016/j.jlp.2025.105898","DOIUrl":"10.1016/j.jlp.2025.105898","url":null,"abstract":"<div><div>The suitability and rationality of fire and rescue equipment allocation are central to effectively responding to regional fires and emergency operations. However, current fire station equipment deployment in China is largely based on standardized national guidelines, with limited consideration of regional variations and local risk profiles. This study integrates fuzzy set-valued statistics for demand assessment and AnyLogic agent-based modelling for dynamic simulation. This method is based on the Standard for Construction of Urban Fire Station in China. It combines local fire safety risk assessments, disaster and accident risk analyses, and evaluations of existing firefighting equipment to determine the actual needs for firefighting and rescue resources. These factors are then used to derive formulas that calculate both the required quantities and the prioritization of equipment allocation in each region. The result is a customized allocation framework that is adaptable to the unique operational conditions and risks of different regions.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105898"},"PeriodicalIF":4.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879945","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 : 2025-12-23DOI: 10.1016/j.jlp.2025.105901
Yan Tang , Jia-Ping Zhao , Lin-Jie Xie , Qi-Tong Ke , Chen Liang , Jun-Cheng Jiang , An-Chi Huang
LiNixCoyMn1-x-yO2 (NCM, x > 0.6) is a high-nickel ternary cathode material extensively utilized in the renewable energy sector due to its cost-effectiveness and high energy density. Nonetheless, its utilization is constrained by structural instability, interfacial side reactions, and thermal safety concerns. This investigation involved the application of varying concentrations of Co3O4 onto the surface of NCM for modification purposes. The influence of coating quantity on the thermal safety and electrochemical performance of the material was examined in conjunction with morphological characterization, electrochemical testing, and thermal safety assessment. The findings indicate that Co3O4 coating acts as a physical barrier to isolate NCM from the electrolyte, diminish lithium-nickel intermixing, and improve the structural stability of the material. Electrochemical tests revealed that the 1 % Co3O4-coated NCM delivered superior cycling stability. After 200 cycles at 0.2C, the capacity retention rate reached 82.85 %, which was 16 % higher than the pristine NCM (66.85 %), and the discharge specific capacity at 3C is 133.3 mAh/g. Thermal analysis results indicate that Co3O4 coating increases the initial temperature of thermal runaway in NCM material. In summary, a modest Co3O4 coating can synergistically optimize the electrochemical performance and safety of high nickel ternary NCM materials by refining surface structure, promoting interface charge transmission, and improving thermal stability.
LiNixCoyMn1-x-yO2 （NCM, x > 0.6）是一种高镍三元正极材料，因其具有成本效益和高能量密度而广泛应用于可再生能源领域。然而，它的使用受到结构不稳定性、界面副反应和热安全问题的限制。本研究涉及在NCM表面应用不同浓度的Co3O4进行改性。结合形貌表征、电化学测试和热安全性评价，考察了涂层量对材料热安全性和电化学性能的影响。结果表明，Co3O4涂层作为物理屏障将NCM与电解质隔离，减少了锂镍混合，提高了材料的结构稳定性。电化学测试表明，1% co3o4涂层的NCM具有优异的循环稳定性。在0.2C下循环200次后，容量保持率达到82.85%，比原始NCM的66.85%提高了16%，在3C下的放电比容量为133.3 mAh/g。热分析结果表明，Co3O4涂层提高了NCM材料热失控的初始温度。综上所述，适度的Co3O4涂层可以通过改善高镍三元NCM材料的表面结构、促进界面电荷传输和提高热稳定性来协同优化其电化学性能和安全性。
{"title":"Electrochemical performance and thermal stability of Co3O4 modified high nickel ternary cathode materials","authors":"Yan Tang , Jia-Ping Zhao , Lin-Jie Xie , Qi-Tong Ke , Chen Liang , Jun-Cheng Jiang , An-Chi Huang","doi":"10.1016/j.jlp.2025.105901","DOIUrl":"10.1016/j.jlp.2025.105901","url":null,"abstract":"<div><div>LiNi<sub>x</sub>Co<sub>y</sub>Mn<sub>1-x-y</sub>O<sub>2</sub> (NCM, x > 0.6) is a high-nickel ternary cathode material extensively utilized in the renewable energy sector due to its cost-effectiveness and high energy density. Nonetheless, its utilization is constrained by structural instability, interfacial side reactions, and thermal safety concerns. This investigation involved the application of varying concentrations of Co<sub>3</sub>O<sub>4</sub> onto the surface of NCM for modification purposes. The influence of coating quantity on the thermal safety and electrochemical performance of the material was examined in conjunction with morphological characterization, electrochemical testing, and thermal safety assessment. The findings indicate that Co<sub>3</sub>O<sub>4</sub> coating acts as a physical barrier to isolate NCM from the electrolyte, diminish lithium-nickel intermixing, and improve the structural stability of the material. Electrochemical tests revealed that the 1 % Co<sub>3</sub>O<sub>4</sub>-coated NCM delivered superior cycling stability. After 200 cycles at 0.2C, the capacity retention rate reached 82.85 %, which was 16 % higher than the pristine NCM (66.85 %), and the discharge specific capacity at 3C is 133.3 mAh/g. Thermal analysis results indicate that Co<sub>3</sub>O<sub>4</sub> coating increases the initial temperature of thermal runaway in NCM material. In summary, a modest Co<sub>3</sub>O<sub>4</sub> coating can synergistically optimize the electrochemical performance and safety of high nickel ternary NCM materials by refining surface structure, promoting interface charge transmission, and improving thermal stability.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105901"},"PeriodicalIF":4.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836621","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 : 2025-12-22DOI: 10.1016/j.jlp.2025.105896
Xinhong Li , Jintong Cao , Yabei Liu , Peihua Liu , Yan Chen , Renren Zhang
Corrosion-induced wall thinning poses a critical threat to the safety of oil and gas gathering pipelines. Existing methods frequently focus on the static prediction, resulting in warning latency and passive maintenance strategies. Dynamic monitoring is essential to transform integrity management from reactive repair to proactive intervention. This study develops a comprehensive intelligent monitoring framework. By acquiring pipeline operational parameters, a full-scale simulation model is constructed to achieve corrosion-prone segment identification and sensor layout optimization. Utilizing a dataset of 500 field samples covering 9 key physicochemical factors, a hybrid SSA-CNN-BiGRU corrosion rate monitoring model is established. The SSA optimizes the CNN for feature extraction, combined with a BiGRU to capture complex temporal dependencies. Comparisons with other models demonstrated that this method achieved superior evaluation metrics (R2 = 0.99165, RMSE = 0.01283). The study is currently limited by the restricted dataset scale and use of fixed rather than adaptive warning thresholds. This research establishing a framework integrates multiphase flow simulation, IoT sensing, and deep learning, effectively shifting pipeline integrity management from static estimation to dynamic, data-driven intelligence.
{"title":"Integrated corrosion monitoring framework for gathering pipelines: coupling simulation, IoT, and machine learning","authors":"Xinhong Li , Jintong Cao , Yabei Liu , Peihua Liu , Yan Chen , Renren Zhang","doi":"10.1016/j.jlp.2025.105896","DOIUrl":"10.1016/j.jlp.2025.105896","url":null,"abstract":"<div><div>Corrosion-induced wall thinning poses a critical threat to the safety of oil and gas gathering pipelines. Existing methods frequently focus on the static prediction, resulting in warning latency and passive maintenance strategies. Dynamic monitoring is essential to transform integrity management from reactive repair to proactive intervention. This study develops a comprehensive intelligent monitoring framework. By acquiring pipeline operational parameters, a full-scale simulation model is constructed to achieve corrosion-prone segment identification and sensor layout optimization. Utilizing a dataset of 500 field samples covering 9 key physicochemical factors, a hybrid SSA-CNN-BiGRU corrosion rate monitoring model is established. The SSA optimizes the CNN for feature extraction, combined with a BiGRU to capture complex temporal dependencies. Comparisons with other models demonstrated that this method achieved superior evaluation metrics (R<sup>2</sup> = 0.99165, RMSE = 0.01283). The study is currently limited by the restricted dataset scale and use of fixed rather than adaptive warning thresholds. This research establishing a framework integrates multiphase flow simulation, IoT sensing, and deep learning, effectively shifting pipeline integrity management from static estimation to dynamic, data-driven intelligence.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105896"},"PeriodicalIF":4.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836691","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 : 2025-12-22DOI: 10.1016/j.jlp.2025.105895
Xiangbao Meng , Dengzhao Li , Jun Yuan , Shanshan Liu
The accumulation of combustible dust in industrial bag filters poses severe explosion risks. Automatic suppressant powder injection serves as a key protective measure, whose effectiveness hinges critically on the injection flow field and the uniformity of suppressant dispersion. This study employs a transient gas-solid two-phase CFD model in ANSYS Fluent to investigate an industrial bag filter. Based on the Euler-Lagrange framework and the Discrete Phase Model (DPM), flow field characteristics and suppressant dispersion patterns were systematically analyzed under three injection directions (downwind, upwind, sideward) and two pressures (4 MPa and 5 MPa). The coefficient of variation (COV) was introduced to quantify distribution uniformity. Results indicate that the downwind injection at 5 MPa achieves an optimal balance between coverage and stability, yielding a bottom average dust concentration of 0.85 kg/m3 and a COV of 0.33. The sideward injection at 4 MPa offers better flow stability (COV = 0.42) albeit with slightly lower coverage. In contrast, the upwind 5 MPa condition is the least favorable, as intense vortices induce suppressant re-suspension. These findings provide a theoretical basis and direct parametric guidance for the explosion-proof design and optimization of bag filter systems.
{"title":"CFD analysis and parameter optimization of explosion suppression powder injection in a bag filter","authors":"Xiangbao Meng , Dengzhao Li , Jun Yuan , Shanshan Liu","doi":"10.1016/j.jlp.2025.105895","DOIUrl":"10.1016/j.jlp.2025.105895","url":null,"abstract":"<div><div>The accumulation of combustible dust in industrial bag filters poses severe explosion risks. Automatic suppressant powder injection serves as a key protective measure, whose effectiveness hinges critically on the injection flow field and the uniformity of suppressant dispersion. This study employs a transient gas-solid two-phase CFD model in ANSYS Fluent to investigate an industrial bag filter. Based on the Euler-Lagrange framework and the Discrete Phase Model (DPM), flow field characteristics and suppressant dispersion patterns were systematically analyzed under three injection directions (downwind, upwind, sideward) and two pressures (4 MPa and 5 MPa). The coefficient of variation (COV) was introduced to quantify distribution uniformity. Results indicate that the downwind injection at 5 MPa achieves an optimal balance between coverage and stability, yielding a bottom average dust concentration of 0.85 kg/m<sup>3</sup> and a COV of 0.33. The sideward injection at 4 MPa offers better flow stability (COV = 0.42) albeit with slightly lower coverage. In contrast, the upwind 5 MPa condition is the least favorable, as intense vortices induce suppressant re-suspension. These findings provide a theoretical basis and direct parametric guidance for the explosion-proof design and optimization of bag filter systems.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105895"},"PeriodicalIF":4.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836661","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 : 2025-12-20DOI: 10.1016/j.jlp.2025.105891
Yang Xiao , Jia-Wang He , Yong Cao , Zhen-Ping Wang , Jun Deng , Chi-Min Shu
The investigation into the migration patterns of mine dust holds substantial significance for ensuring safety production and safeguarding on occupational health in mining operations. To thoroughly explain the present research situation and development tendencies in this area, this work employed bibliometric and visual analyses on 914 papers retrieved from the Web of Science Core Collection (2005–2025) using CiteSpace and VOSviewer software. The results revealed a dramatic upsurge in annual publications addressing mine dust migration, with an average annual growth rate exceeding 20 %. The research trajectory can be categorised into four distinct phases: Foundational exploration, technological introduction, rapid advancement, and in-depth applications. China, the United States, and Russia emerge as the leading nations in this field, with China contributing 68 % of the total publications. Notable institutions, such as Shandong University of Science and Technology, and China University of Mining and Technology, have established a core collaborative network. Highly cited papers predominantly focus on numerical simulations of dust diffusion, the development of dust suppressants, and ventilation optimization technologies. Keyword co-occurrence analysis highlights “numerical simulation”, “coal dust suppression”, and “respirable dust” as key research areas. Cluster analysis further revealed that dust explosion characteristics, wetting mechanisms, and multi-physical field coupling represented frontier research directions. Bursting word analysis indicated that nanomaterials, intelligent monitoring, and intelligent dust control were emerging themes gaining increasing attention. Looking ahead, it is imperative to enhance interdisciplinary integration to advance refined modelling and intelligent control technologies for dust migration patterns. The results provided a robust theoretical foundation for strategic planning and technological innovation in mine dust research through the construction of a comprehensive knowledge map.
研究矿山粉尘的运移规律，对保障矿山安全生产和保障职业健康具有重要意义。为了全面阐述这一领域的研究现状和发展趋势，本研究利用CiteSpace和VOSviewer软件对Web of Science核心文集（2005-2025）中的914篇论文进行了文献计量学和可视化分析。结果显示，关于矿山粉尘迁移的年度出版物急剧增加，年平均增长率超过20%。研究轨迹可分为基础探索、技术引进、快速推进和深入应用四个阶段。中国、美国和俄罗斯成为该领域的主要国家，其中中国占总出版物的68%。山东科技大学、中国矿业大学等知名院校已经建立了核心合作网络。高被引论文主要集中在粉尘扩散的数值模拟、抑尘剂的开发和通风优化技术。关键词共现分析突出了“数值模拟”、“煤尘抑制”和“呼吸性粉尘”作为重点研究领域。聚类分析进一步揭示了粉尘爆炸特征、润湿机理和多物理场耦合是研究的前沿方向。爆发词分析表明，纳米材料、智能监测和智能粉尘控制是日益受到关注的新兴主题。展望未来，必须加强跨学科的整合，以推进尘埃迁移模式的精细建模和智能控制技术。研究结果通过构建综合知识图谱，为矿山粉尘研究的战略规划和技术创新提供了坚实的理论基础。
{"title":"Migration patterns of underground mine dust in the past two decades based upon the methods of bibliometric and visual analyses","authors":"Yang Xiao , Jia-Wang He , Yong Cao , Zhen-Ping Wang , Jun Deng , Chi-Min Shu","doi":"10.1016/j.jlp.2025.105891","DOIUrl":"10.1016/j.jlp.2025.105891","url":null,"abstract":"<div><div>The investigation into the migration patterns of mine dust holds substantial significance for ensuring safety production and safeguarding on occupational health in mining operations. To thoroughly explain the present research situation and development tendencies in this area, this work employed bibliometric and visual analyses on 914 papers retrieved from the Web of Science Core Collection (2005–2025) using CiteSpace and VOSviewer software. The results revealed a dramatic upsurge in annual publications addressing mine dust migration, with an average annual growth rate exceeding 20 %. The research trajectory can be categorised into four distinct phases: Foundational exploration, technological introduction, rapid advancement, and in-depth applications. China, the United States, and Russia emerge as the leading nations in this field, with China contributing 68 % of the total publications. Notable institutions, such as Shandong University of Science and Technology, and China University of Mining and Technology, have established a core collaborative network. Highly cited papers predominantly focus on numerical simulations of dust diffusion, the development of dust suppressants, and ventilation optimization technologies. Keyword co-occurrence analysis highlights “numerical simulation”, “coal dust suppression”, and “respirable dust” as key research areas. Cluster analysis further revealed that dust explosion characteristics, wetting mechanisms, and multi-physical field coupling represented frontier research directions. Bursting word analysis indicated that nanomaterials, intelligent monitoring, and intelligent dust control were emerging themes gaining increasing attention. Looking ahead, it is imperative to enhance interdisciplinary integration to advance refined modelling and intelligent control technologies for dust migration patterns. The results provided a robust theoretical foundation for strategic planning and technological innovation in mine dust research through the construction of a comprehensive knowledge map.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105891"},"PeriodicalIF":4.2,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879944","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}
Fire accident environments expose firefighters to life-threatening hazardous gases such as CO, HCN, and HCl, which can cause asphyxiation, organ damage, or even fatalities. Despite advancements in protective gear, conventional firefighting suits primarily offer passive protection, lacking real-time hazard forecasting. This reactive paradigm often results in delayed warnings against dynamic gas threats. This study proposes an innovative hardware-software integrated firefighting suit designed for proactive safety. The system combines wearable multi-gas sensors, edge computing, and a time series prediction model to forecast gas concentrations with 96.25 % accuracy. By analyzing historical data trends, the suit dynamically classifies hazard levels using a human vulnerability probit model, enabling proactive risk mitigation. Experimental results from simulated fire scenarios demonstrate superior performance in predicting concentrations of gases like H2S and CO. The integration of predictive algorithms with real-time monitoring shifts safety management from passive response to proactive decision-making, enhancing firefighter survivability and operational efficiency. This advancement lays the foundation for next-generation intelligent firefighting equipment. This study is expected to provide a basis for the design of a kind of active protective firefighting suit.
{"title":"Design of an integrated firefighting suit with hazardous gas monitoring and early warning applying a time series model","authors":"Yiwei Peng , Wenguo Weng , Xinyan Huang , Zhichao He","doi":"10.1016/j.jlp.2025.105894","DOIUrl":"10.1016/j.jlp.2025.105894","url":null,"abstract":"<div><div>Fire accident environments expose firefighters to life-threatening hazardous gases such as CO, HCN, and HCl, which can cause asphyxiation, organ damage, or even fatalities. Despite advancements in protective gear, conventional firefighting suits primarily offer passive protection, lacking real-time hazard forecasting. This reactive paradigm often results in delayed warnings against dynamic gas threats. This study proposes an innovative hardware-software integrated firefighting suit designed for proactive safety. The system combines wearable multi-gas sensors, edge computing, and a time series prediction model to forecast gas concentrations with 96.25 % accuracy. By analyzing historical data trends, the suit dynamically classifies hazard levels using a human vulnerability probit model, enabling proactive risk mitigation. Experimental results from simulated fire scenarios demonstrate superior performance in predicting concentrations of gases like H<sub>2</sub>S and CO. The integration of predictive algorithms with real-time monitoring shifts safety management from passive response to proactive decision-making, enhancing firefighter survivability and operational efficiency. This advancement lays the foundation for next-generation intelligent firefighting equipment. This study is expected to provide a basis for the design of a kind of active protective firefighting suit.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105894"},"PeriodicalIF":4.2,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836689","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 : 2025-12-18DOI: 10.1016/j.jlp.2025.105886
Yiping Lin, Hong Huang, Xiaole Zhang
Gas leakage incidents in chemical industrial parks can lead to severe economic losses and pose significant risks to human safety. Rapid identification of the leakage source enables timely mitigation, while accurate estimation of the emission strength helps assess the severity of the incident. This study presents a multi-task learning (MTL) framework for source term estimation (STE) that simultaneously predicts source location and time-varying emission strength. Three representative deep learning architectures, a Deep Feedforward Neural Network (DFNN), a Long Short-Term Memory (LSTM) network, and a Transformer, are compared under both constant and dynamic release scenarios. This work provides the first systematic evaluation of these distinct architectures within an MTL framework for STE, demonstrating the advantages of temporal feature learning for inverse modeling applications. A realistic and large-scale dataset is generated using computational fluid dynamics (CFD) and the response factor method (RFM) to simulate dispersion. Optuna-based hyperparameter optimization is employed to ensure reliable model comparison. Results demonstrate that all three models achieve strong inversion performance. The DFNN proves efficient and robust in constant-release scenarios, while the LSTM excels under dynamic conditions, significantly improving the estimation accuracy over a shallow ANN without MTL, reducing the MAE for source strength from 0.394 to 0.147 and increasing the R from 0.284 to 0.768. Therefore, for time-varying emissions, the MTL-based LSTM is recommended due to its superior ability to capture temporal dynamics and provide precise rate estimates.
{"title":"Multi-task deep learning for pollutant source inversion with DFNN, LSTM, and Transformer architectures","authors":"Yiping Lin, Hong Huang, Xiaole Zhang","doi":"10.1016/j.jlp.2025.105886","DOIUrl":"10.1016/j.jlp.2025.105886","url":null,"abstract":"<div><div>Gas leakage incidents in chemical industrial parks can lead to severe economic losses and pose significant risks to human safety. Rapid identification of the leakage source enables timely mitigation, while accurate estimation of the emission strength helps assess the severity of the incident. This study presents a multi-task learning (MTL) framework for source term estimation (STE) that simultaneously predicts source location and time-varying emission strength. Three representative deep learning architectures, a Deep Feedforward Neural Network (DFNN), a Long Short-Term Memory (LSTM) network, and a Transformer, are compared under both constant and dynamic release scenarios. This work provides the first systematic evaluation of these distinct architectures within an MTL framework for STE, demonstrating the advantages of temporal feature learning for inverse modeling applications. A realistic and large-scale dataset is generated using computational fluid dynamics (CFD) and the response factor method (RFM) to simulate dispersion. Optuna-based hyperparameter optimization is employed to ensure reliable model comparison. Results demonstrate that all three models achieve strong inversion performance. The DFNN proves efficient and robust in constant-release scenarios, while the LSTM excels under dynamic conditions, significantly improving the estimation accuracy over a shallow ANN without MTL, reducing the MAE for source strength from 0.394 to 0.147 and increasing the R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> from 0.284 to 0.768. Therefore, for time-varying emissions, the MTL-based LSTM is recommended due to its superior ability to capture temporal dynamics and provide precise rate estimates.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105886"},"PeriodicalIF":4.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836623","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}
To systematically examine the relationship between the combustion acoustic signals and boilover in oil-storage-tank fires, and to accurately analyze the boilover severity index, small-scale tank experiments were conducted via time-frequency analysis of combustion acoustic signals. By performing small-scale tank fire tests with crude oil and diesel oil, and processing the acoustic data with wavelet denoising and MATLAB routines, the present investigation developed a boiling-state classification framework that relies on the signals’ time-domain waveform, probability density function (PDF), and power spectral density (PSD). In parallel, an integrated analysis was performed to couple these acoustic signatures with flame morphology and temporal evolution. The results demonstrate a responsive relationship between the evolution of the combustion acoustic signals and the boilover stage. Correlation analysis of boilover acoustic signatures with fire dynamics reveals two phenomena: overflow and splash, each displaying distinct acoustic stages and evolutionary trends.
{"title":"Experimental investigation of time–frequency characteristics of acoustic signals during boilover in small-scale oil tanks","authors":"Yanshan Sha , Dongliang Chen , Feiyang Wu , Qiang Cao , Yucong Zhou , Weihua Zhang , Xin Huang , Minghui Wang","doi":"10.1016/j.jlp.2025.105881","DOIUrl":"10.1016/j.jlp.2025.105881","url":null,"abstract":"<div><div>To systematically examine the relationship between the combustion acoustic signals and boilover in oil-storage-tank fires, and to accurately analyze the boilover severity index, small-scale tank experiments were conducted via time-frequency analysis of combustion acoustic signals. By performing small-scale tank fire tests with crude oil and diesel oil, and processing the acoustic data with wavelet denoising and MATLAB routines, the present investigation developed a boiling-state classification framework that relies on the signals’ time-domain waveform, probability density function (PDF), and power spectral density (PSD). In parallel, an integrated analysis was performed to couple these acoustic signatures with flame morphology and temporal evolution. The results demonstrate a responsive relationship between the evolution of the combustion acoustic signals and the boilover stage. Correlation analysis of boilover acoustic signatures with fire dynamics reveals two phenomena: overflow and splash, each displaying distinct acoustic stages and evolutionary trends.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105881"},"PeriodicalIF":4.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836690","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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