Pub Date : 2024-10-09DOI: 10.1016/j.jlp.2024.105455
Mohammad Ali Aghazadeh , Esmaeil Zarei , Abolfazl Ghahramani , He Li
This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.
{"title":"A dynamic system reliability analysis model on safety instrumented systems","authors":"Mohammad Ali Aghazadeh , Esmaeil Zarei , Abolfazl Ghahramani , He Li","doi":"10.1016/j.jlp.2024.105455","DOIUrl":"10.1016/j.jlp.2024.105455","url":null,"abstract":"<div><div>This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105455"},"PeriodicalIF":3.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422471","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 : 2024-10-09DOI: 10.1016/j.jlp.2024.105449
Junjie Li , Xiaoyuan Qian , Jihao Shi , Zonghao Xie , Yuanjiang Chang , Guoming Chen
Natural gas leak from offshore platform poses a potential to cause explosion disaster and bring significant causalities and economic losses. Existing deep learning-based leak detection approaches are limited by the requirement of a large number of labeled leak datasets, and also has worse performance in the complex and changeable marine environment. This study proposes a detection approach of natural gas leakage from offshore platform by integrating optical gas imaging (OGI) camera and unsupervised deep probability learning. In this approach, unsupervised deep learning is applied to learn the changeable infrared features of offshore platform, and variational Bayesian inference is integrated to provide the larger epistemic uncertainty contour corresponding to the infrared natural gas plume. An epistemic uncertainty-based detection score is proposed as the detection criterion to improve the accuracy of natural gas plume detection and localization. An OGI imaging experiment of natural gas leak from offshore platform is conducted to construct the benchmark dataset. With such datasets, two pre-defined parameters, namely Monte Carlo sampling number m = 100 and dropout probability are determined to guarantee the detection accuracy and efficiency. Comparison between the proposed approach and prevalent unsupervised deep learning approach is also conducted. The results demonstrate that our proposed approach has the higher detection accuracy with AUC = 0.9753, as well as the real-time capability with inference time of 4s/frame. Overall, our proposed approach provides a more accurate and generalized approach of natural gas detection for safety monitoring and detection management of offshore platforms.
{"title":"Natural gas leakage detection from offshore platform by OGI camera and unsupervised deep learning","authors":"Junjie Li , Xiaoyuan Qian , Jihao Shi , Zonghao Xie , Yuanjiang Chang , Guoming Chen","doi":"10.1016/j.jlp.2024.105449","DOIUrl":"10.1016/j.jlp.2024.105449","url":null,"abstract":"<div><div>Natural gas leak from offshore platform poses a potential to cause explosion disaster and bring significant causalities and economic losses. Existing deep learning-based leak detection approaches are limited by the requirement of a large number of labeled leak datasets, and also has worse performance in the complex and changeable marine environment. This study proposes a detection approach of natural gas leakage from offshore platform by integrating optical gas imaging (OGI) camera and unsupervised deep probability learning. In this approach, unsupervised deep learning is applied to learn the changeable infrared features of offshore platform, and variational Bayesian inference is integrated to provide the larger epistemic uncertainty contour corresponding to the infrared natural gas plume. An epistemic uncertainty-based detection score is proposed as the detection criterion to improve the accuracy of natural gas plume detection and localization. An OGI imaging experiment of natural gas leak from offshore platform is conducted to construct the benchmark dataset. With such datasets, two pre-defined parameters, namely Monte Carlo sampling number m = 100 and dropout probability <span><math><mrow><mi>p</mi><mo>=</mo><mn>0.1</mn></mrow></math></span> are determined to guarantee the detection accuracy and efficiency. Comparison between the proposed approach and prevalent unsupervised deep learning approach is also conducted. The results demonstrate that our proposed approach has the higher detection accuracy with AUC = 0.9753, as well as the real-time capability with inference time of 4s/frame. Overall, our proposed approach provides a more accurate and generalized approach of natural gas detection for safety monitoring and detection management of offshore platforms.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105449"},"PeriodicalIF":3.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532666","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 : 2024-10-05DOI: 10.1016/j.jlp.2024.105458
Ting-Chia Kao , Yuan-Chen Lin , Hui-Ning Yang , Hsiao-Yun Tsai , Jenq-Renn Chen
A large hydrogen explosion and fire occurred on October 27, 2022 in a residue desulfurization (RDS) process in a refinery in Kaohsiung, Taiwan. RDS is an important process that use hydrogen to convert the sulfur in the fuel into hydrogen sulfide for producing low sulfur fuel. The incident occurred during pressurization of the RDS reactors and downstream coolers by hydrogen. The incident led to significant damages to the downstream coolers and some part of the RDS reactors. Detailed incident investigation was carried out. A reactor effluent air cooler (REAC) in the downstream of RDS reactors was found to be ruptured in the rectangular header box. With security videos, process DCS data, leak and dispersion modelling, and actual damage on the process, it is possible to reproduce the leak rate, size of the initial fireball and damages done to the process. It is also found that the hydrogen leak was accompanied by prompt ignition which is commonly encountered in high-pressure hydrogen services. Recommendations are made to prevent and mitigate any future incident from the RDS process.
{"title":"Incident investigation of hydrogen explosion and fire in a residue desulfurization process","authors":"Ting-Chia Kao , Yuan-Chen Lin , Hui-Ning Yang , Hsiao-Yun Tsai , Jenq-Renn Chen","doi":"10.1016/j.jlp.2024.105458","DOIUrl":"10.1016/j.jlp.2024.105458","url":null,"abstract":"<div><div>A large hydrogen explosion and fire occurred on October 27, 2022 in a residue desulfurization (RDS) process in a refinery in Kaohsiung, Taiwan. RDS is an important process that use hydrogen to convert the sulfur in the fuel into hydrogen sulfide for producing low sulfur fuel. The incident occurred during pressurization of the RDS reactors and downstream coolers by hydrogen. The incident led to significant damages to the downstream coolers and some part of the RDS reactors. Detailed incident investigation was carried out. A reactor effluent air cooler (REAC) in the downstream of RDS reactors was found to be ruptured in the rectangular header box. With security videos, process DCS data, leak and dispersion modelling, and actual damage on the process, it is possible to reproduce the leak rate, size of the initial fireball and damages done to the process. It is also found that the hydrogen leak was accompanied by prompt ignition which is commonly encountered in high-pressure hydrogen services. Recommendations are made to prevent and mitigate any future incident from the RDS process.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105458"},"PeriodicalIF":3.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422469","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 : 2024-10-05DOI: 10.1016/j.jlp.2024.105446
Hans Langva Skarsvåg, Eirik Holm Fyhn, Ailo Aasen
Ammonia is expected to play an important role in the green transition, both as a hydrogen carrier and a zero-emission fuel. The use of refrigerated ammonia is attractive due to its relatively high volumetric energy density and increased safety compared to pressurized solutions. Ammonia is highly toxic, and with new applications and increased global demand come stricter requirements for safe handling. Cold gaseous ammonia following a spill of refrigerated ammonia will in contact with humid air cause fog formation. In an environment rich in ammonia, these droplets will due to ammonia’s strong hygroscopicity consist of considerable amounts of liquid ammonia as well as water. Fog formation affects the ammonia-air density and thus influences the dispersion dynamics, with a potentially significant impact on hazardous zones. In this work, we present a CFD model including an ammonia-water fog formation model based on accurate thermodynamics. This includes modeling the vapor–liquid equilibrium and accounting for the exothermic mixing of ammonia and water. We apply this CFD model to relevant cases and demonstrate the significant impact of the fog. We analyze the effect of varying relative humidity, fog visibility, influence of wind, and pool evaporation rate. Finally, we model the Red Squirrel test 1F and show how the fog formation could have influenced the dispersion behavior.
{"title":"Influence of ammonia-water fog formation on ammonia dispersion from a liquid spill","authors":"Hans Langva Skarsvåg, Eirik Holm Fyhn, Ailo Aasen","doi":"10.1016/j.jlp.2024.105446","DOIUrl":"10.1016/j.jlp.2024.105446","url":null,"abstract":"<div><div>Ammonia is expected to play an important role in the green transition, both as a hydrogen carrier and a zero-emission fuel. The use of refrigerated ammonia is attractive due to its relatively high volumetric energy density and increased safety compared to pressurized solutions. Ammonia is highly toxic, and with new applications and increased global demand come stricter requirements for safe handling. Cold gaseous ammonia following a spill of refrigerated ammonia will in contact with humid air cause fog formation. In an environment rich in ammonia, these droplets will due to ammonia’s strong hygroscopicity consist of considerable amounts of liquid ammonia as well as water. Fog formation affects the ammonia-air density and thus influences the dispersion dynamics, with a potentially significant impact on hazardous zones. In this work, we present a CFD model including an ammonia-water fog formation model based on accurate thermodynamics. This includes modeling the vapor–liquid equilibrium and accounting for the exothermic mixing of ammonia and water. We apply this CFD model to relevant cases and demonstrate the significant impact of the fog. We analyze the effect of varying relative humidity, fog visibility, influence of wind, and pool evaporation rate. Finally, we model the Red Squirrel test 1F and show how the fog formation could have influenced the dispersion behavior.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105446"},"PeriodicalIF":3.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.jlp.2024.105457
Tao Zeng , Lijun Wei , Yingquan Duo , Chao Chen , Rujun Wang , Guoliang Yang , Sining Chen
Storage tanks are vulnerable to catastrophic technological hazards such as fires or explosions. Although many attempts have been made to assess the tank vulnerability exposed to a certain fire or explosion, little attention has been paid to the uncertainties of diverse accident scenarios and time-dependent damage effects. In this paper, a novel methodology is developed to dynamically estimate the vulnerability of chemical storage tank, while the static and dynamic factors related to tank vulnerability are studied in a unified framework. The uncertainties in the evolution of technological hazard are discretized and assessed using the dynamic event tree, in which the dynamic possibility of ignition and potential scenario transition are taken into account. Furthermore, a detailed study of the dynamic consequence and time-dependent damage behavior of physical effects is conducted, supporting a more accurate assessment of tank vulnerability. The case study demonstrates that the developed methodology could simulate the stochastic process of spatio-temporal evolution of technological hazards and enable a comprehensive analysis of damage patterns over time. Besides, the protection and mitigation effects of different safety barriers are evaluated and discussed, the results are valuable for reducing the risks of tank farms.
{"title":"A novel methodology for dynamic vulnerability assessment of storage tank exposed to technological hazards","authors":"Tao Zeng , Lijun Wei , Yingquan Duo , Chao Chen , Rujun Wang , Guoliang Yang , Sining Chen","doi":"10.1016/j.jlp.2024.105457","DOIUrl":"10.1016/j.jlp.2024.105457","url":null,"abstract":"<div><div>Storage tanks are vulnerable to catastrophic technological hazards such as fires or explosions. Although many attempts have been made to assess the tank vulnerability exposed to a certain fire or explosion, little attention has been paid to the uncertainties of diverse accident scenarios and time-dependent damage effects. In this paper, a novel methodology is developed to dynamically estimate the vulnerability of chemical storage tank, while the static and dynamic factors related to tank vulnerability are studied in a unified framework. The uncertainties in the evolution of technological hazard are discretized and assessed using the dynamic event tree, in which the dynamic possibility of ignition and potential scenario transition are taken into account. Furthermore, a detailed study of the dynamic consequence and time-dependent damage behavior of physical effects is conducted, supporting a more accurate assessment of tank vulnerability. The case study demonstrates that the developed methodology could simulate the stochastic process of spatio-temporal evolution of technological hazards and enable a comprehensive analysis of damage patterns over time. Besides, the protection and mitigation effects of different safety barriers are evaluated and discussed, the results are valuable for reducing the risks of tank farms.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105457"},"PeriodicalIF":3.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422475","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 : 2024-10-03DOI: 10.1016/j.jlp.2024.105453
Giuseppa Ancione , Silvia Maria Ansaldi , Paolo Bragatto , Patrizia Agnello , Maria Francesca Milazzo
Ageing management of critical equipment is essential for major accident hazard prevention. In European Union, the Seveso Directive explicitly refers to equipment ageing and specific procedures and tools have been developed in some countries to verify the adequacy of ageing management plans. Ageing management requires up-to-date knowledge of damage mechanisms, failure rates, and inspection techniques. Although the energy transition is moving towards innovative and low-impact processes, the issue of equipment ageing remains a concern for the future due to several factors as many existing petroleum industries will be kept operating for years and green fuels will partially use existing plants. The use of digital technologies, such as inspection robots and pervasive sensors, is increasing in order to optimize maintenance costs and extend the useful lifetime of critical equipment. This poses challenges for stakeholders in the establishment, control bodies, and competent authorities, which need to effectively manage knowledge in the face of digital and energy transitions. Ontologies represent a fundamental methodology for organizing and integrating information throughout the equipment life cycle. These are beneficial for activities with high cognitive content such as incident analysis, risk assessment, maintenance management, and inspection planning. This work aims to strengthen an ontology for ageing management by making it able to incorporate emerging digital inspection technologies and damage mechanisms related to green fuels; finally, through the developed web-application, the ontology is available to establishment operators and auditors for the evaluation of the adequacy of ageing management.
{"title":"An ontological approach increasing the knowledge about equipment ageing including the effects of current transitions","authors":"Giuseppa Ancione , Silvia Maria Ansaldi , Paolo Bragatto , Patrizia Agnello , Maria Francesca Milazzo","doi":"10.1016/j.jlp.2024.105453","DOIUrl":"10.1016/j.jlp.2024.105453","url":null,"abstract":"<div><div>Ageing management of critical equipment is essential for major accident hazard prevention. In European Union, the Seveso Directive explicitly refers to equipment ageing and specific procedures and tools have been developed in some countries to verify the adequacy of ageing management plans. Ageing management requires up-to-date knowledge of damage mechanisms, failure rates, and inspection techniques. Although the energy transition is moving towards innovative and low-impact processes, the issue of equipment ageing remains a concern for the future due to several factors as many existing petroleum industries will be kept operating for years and green fuels will partially use existing plants. The use of digital technologies, such as inspection robots and pervasive sensors, is increasing in order to optimize maintenance costs and extend the useful lifetime of critical equipment. This poses challenges for stakeholders in the establishment, control bodies, and competent authorities, which need to effectively manage knowledge in the face of digital and energy transitions. Ontologies represent a fundamental methodology for organizing and integrating information throughout the equipment life cycle. These are beneficial for activities with high cognitive content such as incident analysis, risk assessment, maintenance management, and inspection planning. This work aims to strengthen an ontology for ageing management by making it able to incorporate emerging digital inspection technologies and damage mechanisms related to green fuels; finally, through the developed web-application, the ontology is available to establishment operators and auditors for the evaluation of the adequacy of ageing management.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105453"},"PeriodicalIF":3.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1016/j.jlp.2024.105454
Weikang Liu , Xuxu Sun , Ao Peng , Xianfeng Chen
In this study, the influence of porous non-metallic balls on the dynamics of detonation propagation in hydrogen–oxygen mixtures is studied in stainless steel tubes with square cross-sections. The effect of the length and thickness of the balls is considered in detail. Four pressure transducers are used to record the detonation time-of-arrival, and the average velocity of detonation propagation is calculate. The smoked foil technique is performed to register the detonation cellular structures. The results indicate that increasing the filling length and thickness of the balls leads to a significant increase in velocity loss, critical pressure, and re-initiation distance during detonation propagation. Two propagation modes are observed near the critical pressure. In sub-critical mode, the detonation wave is attenuated and failed eventually, propagating to the end of the tube in the form of a low-speed deflagration wave. In super-critical mode, the detonation propagation can be promoted by inducing a vertical transverse wave generated by the detonation wave passing through a group of small balls. It is worth noting that the increase in filling thickness of the small balls leads to a greater velocity loss compared to the increase in filling length. However, an increase in the filling length will cause the detonation wave to undergo a longer and more sustained weakening process as it passes through the small balls, thereby resulting in a longer re-initiation distance and a lower average velocity over a short distance after re-initiation. By increasing the filling length of the small balls to 312 mm, the transition of the detonation wave from multi-head detonation to double-head detonation can be observed, and then successfully turns into stable detonation. However, when filling double-layer small balls, even in super-critical conditions, the attenuated detonation wave cannot achieve re-initiation over a short distance.
{"title":"The effect of porous non-metallic balls on detonation propagation in hydrogen–oxygen mixtures","authors":"Weikang Liu , Xuxu Sun , Ao Peng , Xianfeng Chen","doi":"10.1016/j.jlp.2024.105454","DOIUrl":"10.1016/j.jlp.2024.105454","url":null,"abstract":"<div><div>In this study, the influence of porous non-metallic balls on the dynamics of detonation propagation in hydrogen–oxygen mixtures is studied in stainless steel tubes with square cross-sections. The effect of the length and thickness of the balls is considered in detail. Four pressure transducers are used to record the detonation time-of-arrival, and the average velocity of detonation propagation is calculate. The smoked foil technique is performed to register the detonation cellular structures. The results indicate that increasing the filling length and thickness of the balls leads to a significant increase in velocity loss, critical pressure, and re-initiation distance during detonation propagation. Two propagation modes are observed near the critical pressure. In sub-critical mode, the detonation wave is attenuated and failed eventually, propagating to the end of the tube in the form of a low-speed deflagration wave. In super-critical mode, the detonation propagation can be promoted by inducing a vertical transverse wave generated by the detonation wave passing through a group of small balls. It is worth noting that the increase in filling thickness of the small balls leads to a greater velocity loss compared to the increase in filling length. However, an increase in the filling length will cause the detonation wave to undergo a longer and more sustained weakening process as it passes through the small balls, thereby resulting in a longer re-initiation distance and a lower average velocity over a short distance after re-initiation. By increasing the filling length of the small balls to 312 mm, the transition of the detonation wave from multi-head detonation to double-head detonation can be observed, and then successfully turns into stable detonation. However, when filling double-layer small balls, even in super-critical conditions, the attenuated detonation wave cannot achieve re-initiation over a short distance.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105454"},"PeriodicalIF":3.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422389","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 : 2024-10-03DOI: 10.1016/j.jlp.2024.105451
Yuanyuan Li , Jun Wu , Ruiping Yuan
Road transportation serves as the primary mode for hazardous materials (hazmat) transportation in China. However, the current academic literature lacks sufficient exploration of optimizing transportation networks through road-blocking strategies. This research proposed a bi-level programming model, where the government acts as the decision-maker in the upper-level programming, and the transportation enterprise operates at the lower level. Road-blocking strategies are employed by the government to mitigate transportation risks. We developed exact solution algorithms for the upper programming and employed genetic algorithms for the lower model separately. Finally, using a real road network in Beijing as a case study, we showcased the effectiveness of our approach. We computed government decision schemes and enterprise transportation routes for 13 scenarios, and conducted a further discussion and analysis on scenarios with road service levels of 83% and 80.9%. The method and results presented in our study can adeptly dissect the transportation of hazmat in city areas, offering insightful perspectives and robust support for devising more streamlined management strategies.
{"title":"Optimizing hazardous materials transportation network: A bi-level programming approach considering road blocking","authors":"Yuanyuan Li , Jun Wu , Ruiping Yuan","doi":"10.1016/j.jlp.2024.105451","DOIUrl":"10.1016/j.jlp.2024.105451","url":null,"abstract":"<div><div>Road transportation serves as the primary mode for hazardous materials (hazmat) transportation in China. However, the current academic literature lacks sufficient exploration of optimizing transportation networks through road-blocking strategies. This research proposed a bi-level programming model, where the government acts as the decision-maker in the upper-level programming, and the transportation enterprise operates at the lower level. Road-blocking strategies are employed by the government to mitigate transportation risks. We developed exact solution algorithms for the upper programming and employed genetic algorithms for the lower model separately. Finally, using a real road network in Beijing as a case study, we showcased the effectiveness of our approach. We computed government decision schemes and enterprise transportation routes for 13 scenarios, and conducted a further discussion and analysis on scenarios with road service levels of 83% and 80.9%. The method and results presented in our study can adeptly dissect the transportation of hazmat in city areas, offering insightful perspectives and robust support for devising more streamlined management strategies.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105451"},"PeriodicalIF":3.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422473","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 : 2024-09-29DOI: 10.1016/j.jlp.2024.105448
Zhongheng Nie , Wei Gao , Haipeng Jiang , Jianxin Lu , Zhengkang Lu , Xinsheng Jiang
In the study of flame quenching, quenching thickness is one of the important parameters to determine the design of a flame arrester, and often determines the flame quenching performance of the arrester. In the study, residual network (ResNet) and artificial neural network (ANN) are used to predict the critical quench thickness of combustible gas in pipelines. The critical quench thickness is influenced by fuel concentration and density, pipeline size, inert gas type and concentration, porous media porosity, and thermal conductivity. The influence of different combinations of hyper-parameters on the prediction performance of the two models is explored. The results show that the prediction performance of both models reaches the best after hyper-parameter optimization. Compared with ANN, the ResNet model shows more stable and better prediction ability, and its optimal evaluation parameters are: MAE is 1.4679, MSE is 91.7431, R2 is 0.9216. The prediction errors of the two models on the same dataset are subjected to analysis, and the impact of the use of normalized data on the performance of the two models is compared. It is determined that the ResNet model demonstrated superior robustness and generalization ability in predicting the critical quenching thickness of combustible gases. The study is helpful for the safety protection of combustible gas and the safety design of pipeline arresters.
{"title":"Predicting critical flame quenching thickness using machine learning approach with ResNet and ANN","authors":"Zhongheng Nie , Wei Gao , Haipeng Jiang , Jianxin Lu , Zhengkang Lu , Xinsheng Jiang","doi":"10.1016/j.jlp.2024.105448","DOIUrl":"10.1016/j.jlp.2024.105448","url":null,"abstract":"<div><div>In the study of flame quenching, quenching thickness is one of the important parameters to determine the design of a flame arrester, and often determines the flame quenching performance of the arrester. In the study, residual network (ResNet) and artificial neural network (ANN) are used to predict the critical quench thickness of combustible gas in pipelines. The critical quench thickness is influenced by fuel concentration and density, pipeline size, inert gas type and concentration, porous media porosity, and thermal conductivity. The influence of different combinations of hyper-parameters on the prediction performance of the two models is explored. The results show that the prediction performance of both models reaches the best after hyper-parameter optimization. Compared with ANN, the ResNet model shows more stable and better prediction ability, and its optimal evaluation parameters are: MAE is 1.4679, MSE is 91.7431, R<sup>2</sup> is 0.9216. The prediction errors of the two models on the same dataset are subjected to analysis, and the impact of the use of normalized data on the performance of the two models is compared. It is determined that the ResNet model demonstrated superior robustness and generalization ability in predicting the critical quenching thickness of combustible gases. The study is helpful for the safety protection of combustible gas and the safety design of pipeline arresters.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105448"},"PeriodicalIF":3.6,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422477","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 : 2024-09-28DOI: 10.1016/j.jlp.2024.105447
Boqian Zhang, Donghui Ma, Wei Wang
The population distribution within areas potentially impacted by major accidents is crucial for societal risk analysis. Population data are essential for land use planning activities related to major accident hazards. However, repeated quantitative risk assessments (QRA) and diverse planning scenarios complicate planning decisions in Major Hazard Installation (MHI) areas. This study introduces an analysis method for the safety of population distribution in potential impact zones of major accidents. By combining accident simulation and population distribution characteristics analysis, we established standardized indicators that succinctly indicate safety thresholds for societal risks, addressing the challenge of rapidly assessing off-site population safety. A rapid formula meeting practical needs is provided for urban managers and planners, enabling them to easily evaluate how population density and distribution affect societal risks in MHI areas. Finally, we applied this method to the flammable and explosive hazardous material enterprises in Shijiazhuang, China, demonstrating its functionality and practicality. The application results suggest its potential as a useful decision-support method for guiding urban planning and emergency management. This work offers new insights and valuable demonstrations for enhancing vulnerability and land use risk assessments in MHI regions.
{"title":"A rapid analysis method for regional population distribution safety based on societal risk assessment","authors":"Boqian Zhang, Donghui Ma, Wei Wang","doi":"10.1016/j.jlp.2024.105447","DOIUrl":"10.1016/j.jlp.2024.105447","url":null,"abstract":"<div><div>The population distribution within areas potentially impacted by major accidents is crucial for societal risk analysis. Population data are essential for land use planning activities related to major accident hazards. However, repeated quantitative risk assessments (QRA) and diverse planning scenarios complicate planning decisions in Major Hazard Installation (MHI) areas. This study introduces an analysis method for the safety of population distribution in potential impact zones of major accidents. By combining accident simulation and population distribution characteristics analysis, we established standardized indicators that succinctly indicate safety thresholds for societal risks, addressing the challenge of rapidly assessing off-site population safety. A rapid formula meeting practical needs is provided for urban managers and planners, enabling them to easily evaluate how population density and distribution affect societal risks in MHI areas. Finally, we applied this method to the flammable and explosive hazardous material enterprises in Shijiazhuang, China, demonstrating its functionality and practicality. The application results suggest its potential as a useful decision-support method for guiding urban planning and emergency management. This work offers new insights and valuable demonstrations for enhancing vulnerability and land use risk assessments in MHI regions.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"92 ","pages":"Article 105447"},"PeriodicalIF":3.6,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357406","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}