Journal of Loss Prevention in The Process Industries最新文献

{"title":"Investigation of propane/air explosion-venting characteristics: influence of venting parameters, prediction model establishment and hazardous condition analysis","authors":"Jizhe Wang ,&nbsp;Qianran Hu ,&nbsp;Huijie Yang ,&nbsp;Xiaojie Wang ,&nbsp;Xinming Qian ,&nbsp;Mengqi Yuan ,&nbsp;Pengliang Li","doi":"10.1016/j.jlp.2025.105790","DOIUrl":"10.1016/j.jlp.2025.105790","url":null,"abstract":"<div><div>Explosion-venting, as an effective measure for controlling explosion hazards, plays a significant role in mitigating the consequences of gas explosion in industrial and civil buildings. To systematically investigate the effects of venting characteristic parameters on the hazardous characteristics of premixed propane/air mixture explosion, a computational fluid dynamics (CFD) model with a vented volume of 63.48 m³ was constructed. The parametric study, model prediction, and hazard analysis were conducted to examine the influence of the opening pressure (<em>P</em>_<em>0</em>), the vent weight (<em>W</em>_<em>0</em>) and the vent area (<em>A</em>_<em>0</em>) on the explosion reaction time (<em>R</em>_<em>t</em>), the peak overpressure (<em>P</em>_<em>c</em>) and the peak temperature (<em>T</em>_<em>c</em>). The results indicated that as <em>P</em>_<em>0</em> and <em>W</em>_<em>0</em> increased, <em>R</em>_<em>t</em> showed a gradual decreasing trend (<em>t</em>_min = 0.463 s), while <em>P</em>_<em>c</em> and <em>T</em>_<em>c</em> exhibited an opposite increasing trend (<em>P</em>_max = 23.9 kPa, <em>T</em>_max = 2305 K). Meanwhile, with the increase of <em>A</em>_<em>0</em>, <em>R</em>_<em>t</em>, <em>P</em>_<em>c</em> and <em>T</em>_<em>c</em> initially decreased and then slowly increased. The minimum explosion parameters were achieved when <em>A</em>_<em>0</em> = 4.00 m² (<em>t</em>_min = 0.455 s, <em>P</em>_min = 7.32 kPa, <em>T</em>_min = 2263 K). Besides, the response surface methodology (RSM) was employed to determine the influence degree of different factors on <em>R</em>_<em>t</em>, <em>P</em>_<em>c</em> and <em>T</em>_<em>c</em> as <em>A</em>_<em>0</em> &gt; <em>P</em>_<em>0</em> &gt; <em>W</em>_<em>0</em>. Multi-factor prediction models for <em>R</em>_<em>t</em>, <em>P</em>_<em>c</em> and <em>T</em>_<em>c</em> were established and validated. The study also identified that the positive feedback effect between indoor and outdoor overpressure, but the negative feedback effect between indoor and outdoor flame temperature.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105790"},"PeriodicalIF":4.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045724","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}

{"title":"Development of a heat-exchanger performance degradation model by integrating physics-based modeling with an LSTM approach","authors":"Yen-Ju Lu ,&nbsp;Dai-Rui Lin ,&nbsp;Chen-Hua Wang","doi":"10.1016/j.jlp.2025.105788","DOIUrl":"10.1016/j.jlp.2025.105788","url":null,"abstract":"<div><div>Performance degradation in heat exchangers poses a significant risk to process stability and equipment safety. This study presents a predictive framework that combines the physical indicator of log mean temperature difference with a long short-term memory neural network to monitor degradation trends. In addition to LSTM, the study evaluates seven other time-series models, including AR, MA, ARMA, ARIMA, KNN, SVR, and Transformer. Model performance was assessed using five statistical metrics: mean absolute percentage error, mean squared error, root mean squared error, mean absolute error, and coefficient of determination. Among all models, LSTM consistently delivered the most reliable results across both training and test datasets. During testing, the LSTM model achieved a R² of 0.992, DTW similarity of 94.5 percent, and MAPE below 0.1 percent. These results confirm the model's strong fitting capability and generalizability. The proposed approach successfully addresses challenges such as the nonlinear behaviour of thermal signals and the lack of pronounced degradation features. It offers practical value for maintenance planning and process shutdown decision support in real industrial settings.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105788"},"PeriodicalIF":4.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045722","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}

{"title":"Event and fault tree-based Bayesian network for probabilistic safety assessment of earthquake-induced fire and explosion hazard","authors":"Se-Hyeok Lee ,&nbsp;Changuk Mun ,&nbsp;Junho Song ,&nbsp;Ji-Eun Byun","doi":"10.1016/j.jlp.2025.105789","DOIUrl":"10.1016/j.jlp.2025.105789","url":null,"abstract":"<div><div>In nuclear power plant engineering, probabilistic safety assessment (PSA) has been actively studied to evaluate risk due to earthquake events. Recently, the similar PSA framework has been proposed to calculate probability of shut-down of gas plants when earthquake occurred. However, in process plants, earthquakes can also trigger secondary hazards such as fires and explosions, which have been less addressed in seismic PSA despite their potentially catastrophic consequences. These cascading events would cause severe casualties, asset losses, and long-term health impacts by a leak of hazardous substances. To consider such multi-hazard impacts, i.e., earthquake-induced fires or explosions, this work proposes a Bayesian network (BN)-based framework, which is modelled by transforming from fault- and event-tree. For seismic risk, fault tree is constructed to represent the joint operation of constituting equipment, while the top event is defined as a shut-down by earthquake events. Then, the event tree is derived to represent an evolving process from release to final events (i.e., several types of fires and explosions). These constructed trees are transformed into BN, and this process can prevent causal errors when BN is modelled directly. By extending seismic PSA concepts with the traditional fire/explosion event-tree methodology in a unified BN framework, the intended contribution is to enable integrated multi-hazard risk assessment that can account for both seismic and post-seismic accident scenarios. The proposed framework is demonstrated by constructing BN model for earthquake-induced fire and explosion at a gas plant. Then, the inference of the BN model is presented. First, the risk of the multi-hazard on the system is quantified for different hazard levels of earthquake. Second, the contribution of each component to the system failure is evaluated with a retrofit strategy on crucial facilities. By analyzing various accident scenarios, it is showed that the proposed BN model can provide risk-informed decision-making for prioritizing repair and/or retrofitting of structures or equipment in the plant.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105789"},"PeriodicalIF":4.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045723","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}

{"title":"Network-aware multi-step hazard prediction using temporal knowledge graphs: A chemical industry case study","authors":"Jian Liu ,&nbsp;Zhuqing Zhang ,&nbsp;Rui Feng","doi":"10.1016/j.jlp.2025.105787","DOIUrl":"10.1016/j.jlp.2025.105787","url":null,"abstract":"<div><div>Proactive hazard prediction in complex industrial environments like the chemical sector is critical yet challenging due to dynamic, interconnected risks often overlooked by traditional methods. Existing data-driven approaches frequently fall short by failing to model evolving temporal dependencies and multi-step risk propagation across diverse hazard relationships. To overcome these limitations, this study introduces the Temporal Knowledge Graph-Autoregressive Multistep Prediction Model (TKG-AM). Our core innovation lies in representing dynamic, multi-relational hazard data using Temporal Knowledge Graphs (TKGs) and coupling this rich representation with an autoregressive deep learning engine specifically designed for accurate multi-step forecasting, providing crucial lead time for interventions. Validated on extensive hazard records from a chemical industrial park in Ningxia, China, TKG-AM demonstrated strong predictive power, achieving a direct hit rate (Hits@1) of 58.5 % and top-ten accuracy (Hits@10) of 67.3 %. Our analysis revealed the network's small-world properties, facilitating rapid risk diffusion, and identified 75 critical bridging nodes central to information flow. We further analyzed how network topology and specific relationship types impact prediction accuracy, finding, for instance, that inter-community predictions are inherently more challenging. To enhance practical application, we developed a data-driven prediction score threshold enabling risk prioritization (e.g., scores &gt;20 yielding &gt;90 % accuracy). These integrated findings validate TKG-AM as a robust and insightful methodology, offering significant improvements in the efficiency, specificity, and strategic targeting of hazard prevention and differentiated risk management efforts in the chemical industry.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105787"},"PeriodicalIF":4.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011002","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}

{"title":"Risk assessment and loss estimation of urban built environments exposed to UVCE from off-site gasoline transport incidents","authors":"Sagnika Chakraborty ,&nbsp;Tarak Nath Mazumder,&nbsp;Arup Das","doi":"10.1016/j.jlp.2025.105780","DOIUrl":"10.1016/j.jlp.2025.105780","url":null,"abstract":"<div><div>The increasing gasoline consumption in India has led to a surge in off-site gasoline transportation, heightening the risk of spills triggering Unconfined Vapor Cloud Explosions (UVCE). Such incidents pose significant threats to the built environment, particularly in densely populated urban corridors. This study presents a systematic framework for assessing the physical vulnerability of urban structures to UVCE, employing a building prototype-based approach. The methodology integrates macro and micro-level assessments, considering building and neighborhood characteristics, structural integrity, and exposure levels to quantify potential damage. A deterministic blast overpressure analysis is conducted using the TNT equivalency method, mapping damage levels to building structures. The study employs an indicator-based approach to capture the effects of neighborhood configurations, architectural design, and structural attributes of buildings on blast overpressure which in turn alter the building damage. By defining standardized building prototypes, the framework estimates repair costs as a proxy for vulnerability, offering a detailed spatial assessment of at-risk areas. The model is applied to a high-density urban corridor in the Kolkata Metropolitan Area (KMA), where 1,979 buildings are surveyed, classified, and analyzed under a worst-case scenario, providing spatially visualized risk assessments. Findings reveal that neighborhood characteristics, architectural configurations, and structural integrity significantly influence vulnerability, highlighting high-risk zones requiring targeted interventions. The study underscores the need to integrate safety considerations into urban planning, infrastructure resilience, and emergency response strategies. By bridging risk assessment with urban development, this research provides policymakers, engineers, and emergency planners with actionable insights to mitigate UVCE risks in rapidly urbanizing environments.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105780"},"PeriodicalIF":4.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933598","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}

{"title":"Research on the variation of CO concentration and temperature in the directional drilling process","authors":"Furu Kang ,&nbsp;Jiahao Song ,&nbsp;Jiaxiang Zhang ,&nbsp;Chao Pan ,&nbsp;Dengke Wang ,&nbsp;Zujin Bai ,&nbsp;Shixing Fan ,&nbsp;Jun Deng","doi":"10.1016/j.jlp.2025.105772","DOIUrl":"10.1016/j.jlp.2025.105772","url":null,"abstract":"<div><div>During drilling operations, the drill bit continuously rubs against the coal seam, generating heat and releasing CO gas, which may lead to severe CO poisoning accidents. To address this issue, we construct an experimental monitoring platform to analyze the variations in CO concentration and temperature during coal seam drilling under different drilling feed rates and drilling rig rotational speeds. The results indicate that no CO is produced at the initial stage of drilling. Subsequently, CO concentration increases linearly, accompanied by an increase in the CO generation rate, which leads to an exponential increase in CO concentration. A higher drilling rig rotational speed results in a faster CO generation rate and higher CO concentration. Similarly, a higher drilling feed rate also accelerates the CO generation rate. Specifically, at a drilling rig rotational speed of 990 r/min and a drilling feed rate of 0.5 cm/s, the CO concentration reaches a peak of 29.6 ppm. The temperatures of both the drill bit and coal body initially increase rapidly and then stabilize. Higher drilling rig rotational speeds lead to faster temperature rises in both the drill bit and coal body, causing them to enter the steady growth phase earlier and resulting in higher final temperatures. In contrast, higher drilling feed rates cause the drill bit temperature to rise more rapidly, while the coal body temperature increases at a slower rate. As a result, both temperatures reach the steady growth phase earlier but lead to a lower final temperature. At a drilling rig rotational speed of 990 r/min and a drilling feed rate of 1.5 cm/s, the drill bit and coal body temperatures enter the steady growth phase earliest, at 30 s. At a drilling rig rotational speed of 990 r/min and a drilling feed rate of 0.5 cm/s, the highest temperatures for the drill bit and coal body are reached, 118.2 °C and 68.2 °C, respectively. The temperature rise in both the drill bit and coal body follows a linear relationship with the average CO generation rate. This study provides valuable insights for ensuring safety during coal seam drilling operations.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105772"},"PeriodicalIF":4.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989095","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}

{"title":"How failures in interorganizational knowledge transfer impact process safety: Insights from the case study of an ageing offshore oil & gas facility acquisition","authors":"N.N. Ferreira ,&nbsp;R. Dziedzic ,&nbsp;G.P. Monteiro ,&nbsp;C.P. Migueles ,&nbsp;A. Burcharth","doi":"10.1016/j.jlp.2025.105779","DOIUrl":"10.1016/j.jlp.2025.105779","url":null,"abstract":"<div><div>An increasingly common form of organizational change in high-hazard industries is the acquisition of ageing facilities. While the safety implications of other types of organizational change have been widely studied, the risks associated with ageing facility acquisitions remain under-investigated. This acquisition process is often accompanied by significant challenges in interorganizational knowledge transfer (IKT). Although knowledge is widely recognized as essential to process safety, the impacts of IKT failures require further exploration, especially when original operational teams are not retained. This study aims to identify the process safety impacts of unsuccessful IKT during the acquisition of an ageing facility in which no personnel were transferred. Drawing on a qualitative case study of an offshore oil and gas platform acquisition, this study offers an in-depth analysis of the organizational and operational discontinuities that emerged during the asset handover. Safety incident data from the case study platform revealed an increase in safety incidents following the acquisition. Interviews with process safety experts and practitioners were conducted to map IKT challenges to Risk-Based Process Safety (RBPS). Failures in IKT were found to vary in their impact on process safety. Governance-related aspects of the IKT process, such as the availability of personnel from both companies during the transfer, and access to legacy databases, were found to be the most critical. These failures not only directly impacted RBPS elements such as process knowledge management but also contributed to safety impacts. These findings support the development of improved IKT frameworks for managers and regulators to avoid operational safety risks following an ageing facility acquisition.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105779"},"PeriodicalIF":4.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997237","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}

{"title":"Fatigue lifetime prediction of rubber O-rings in CCUS systems: A coupled diffusion-deformation-fatigue model","authors":"Xiong Xiao ,&nbsp;Li Mo ,&nbsp;Mengru Fang ,&nbsp;Furong Wang ,&nbsp;Shenbin Xiao ,&nbsp;Chao Chen","doi":"10.1016/j.jlp.2025.105765","DOIUrl":"10.1016/j.jlp.2025.105765","url":null,"abstract":"<div><div>Carbon capture, utilization, and storage (CCUS) technology serves as a critical approach for industrial emissions reduction. However, during high-pressure CO₂ pipeline transportation, the sealing performance of rubber O-rings at the pipeline end quick-opening blind flange is crucial to system integrity. Seal failure may not only lead to substantial CO₂ leakage, significantly compromising the CCUS system's emission reduction efficiency, but also pose serious safety hazards due to sudden high-pressure gas release. Current research on the diffusion behavior of CO₂ in rubber materials and the impact of its induced deformation on the fatigue life of sealing components remains notably insufficient. To address this issue, this study develops a finite element analysis (FEA) model coupling diffusion-deformation-fatigue life based on gas diffusion and hyperelasticity theories to assess the fatigue life of rubber O-ring. The results show that a moderate compression proportion (approximately 15 %) is beneficial for prolonging the O-ring fatigue life while minimizing leakage risks. Increasing cavity diameter reduces the fatigue life of the O-ring. As CO₂ pressure increases from 1 MPa to 5 MPa, the fatigue life of O-rings with cavities decreases from infinity to 7.36 cycles. Moreover, the acceleration of the depressurization rate adversely affects the fatigue life of O-rings containing cavities. The results provide essential guidelines for optimizing the design of CCUS pipeline sealing systems, ensuring long-term operational reliability of high-pressure CO₂ transportation infrastructure while mitigating potential safety hazards induced by seal failure.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105765"},"PeriodicalIF":4.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004765","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}

{"title":"Monitoring and early warning method for abnormal conditions in complex processes based on bidirectional causal reasoning and its application in diesel hydrotreating plants","authors":"Feng Wang,&nbsp;Hui Zhao,&nbsp;Xiaozhi Li,&nbsp;Jing Bian","doi":"10.1016/j.jlp.2025.105771","DOIUrl":"10.1016/j.jlp.2025.105771","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Under conditions of high temperature, high pressure, and other extreme factors, petrochemical plants are highly susceptible to abnormal conditions and accidents, which are difficult to trace the causes and predict the consequences. To address these issues, a monitoring and early warning method for abnormal conditions in complex processes based on bidirectional causal reasoning is proposed. Firstly, bidirectional causal reasoning models for abnormal conditions in complex processes are established. This involves constructing a risk identification and analysis knowledge base derived from HAZOP analysis reports. Data from the knowledge base are processed and trained models using algorithms such as LDA clustering, Apriori association analysis, and Naïve Bayes classification. These processes yield categorized causes and consequences, as well as causal coupling relationships, culminating in the establishment of causal clustering models and the risk prediction model. Subsequently, the sensing data are integrated into the Distributed Control System (DCS), Mechanical Condition Monitoring System (CMS), and Environmental Monitoring System according to the different monitoring entities under complex operating conditions. The typical parameters, critical monitoring locations, and risk points for each category of sensing data are analyzed in detail. Based on the monitoring units, parameters, and abnormality descriptions involved in the sensing data, a single abnormal condition text is generated in real-time. By combining the temporal sequence of abnormal conditions with the physical spatial order of abnormal entities, causal relationships among individual abnormal conditions are established, enabling the construction of a real-time and comprehensive description of complex operating conditions. Then, natural language segmentation algorithms and causal clustering models are employed to compute the posterior probabilities of the comprehensive description of complex operating conditions belonging to each category of causes and consequences in the risk identification and analysis knowledge base. Corresponding relationships between the comprehensive description and the classifications of causes and consequences are established. Based on the identified causal coupling relationships, potential causes leading to the current abnormal condition and the possible resulting consequences are determined. Finally, the Naïve Bayes risk prediction model is utilized to perform a risk assessment of the accident scenarios derived from the causal analysis of abnormal conditions. The basic information, comprehensive description, causes, consequences, and risk information of the abnormal conditions are stored in the risk identification and analysis knowledge base, thereby completing the monitoring and early warning of abnormal conditions and bidirectional causal reasoning. This paper presents an application example of the method in a diesel hydrotreating plant, demonstrating ","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105771"},"PeriodicalIF":4.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919930","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}

{"title":"An integrated analysis method for anaerobic reactor leakage and explosion risk","authors":"Bingjie Fan,&nbsp;Kaili Xu,&nbsp;Jiye Cai,&nbsp;Zhenhui Yu","doi":"10.1016/j.jlp.2025.105768","DOIUrl":"10.1016/j.jlp.2025.105768","url":null,"abstract":"<div><div>To strengthen the development and utilization of renewable energy and ensure the safe operation of biogas projects, this paper proposed a risk quantification method based on Fault Tree Analysis - Qualitative Comparative Analysis - Bayesian Neural Network - Computational Fluid Dynamics simulation, which is a risk analysis model of \"Technology - Management - Probability - Consequence\" with multiple dimensions and interdisciplinary integration. Firstly, the Fault Tree analysis was used to analyze the causes of the anaerobic reactor leakage and explosion accidents. Secondly, Qualitative Comparative Analysis was used to find out the necessary conditions and combination path of anaerobic reactor leakage and explosion accidents in management aspect. Next, the Bayesian Neural Network model was used to predict accidents frequency distribution. The frequency range was 0.315–0.380, and the uncertainty of the prediction results was given. Then, CFD simulation was used to simulate the leakage consequences of the manhole and the top biogas pipeline of the anaerobic reactor in windy and windless conditions, and the methane diffusion distribution after leakage was explored. Finally, according to results, an improved explosion hazard area and level division way was proposed.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"99 ","pages":"Article 105768"},"PeriodicalIF":4.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912506","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}