Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108538
Qiang Liu, Zhuangzhuang Xu, Guogang Yang, Han Sun, Shuyao Zhang
In the pipeline, ventilation, and storage systems, pipe structures with curved geometries may unexpectedly act as explosion risk amplifiers. Based on large Eddy Simulation (LES), this study conducted a numerical simulation of the explosion characteristics of hydrogen/air mixtures in wavy pipelines, focusing on analyzing the effects of waveform parameters (amplitude A and wavelength B) and hydrogen equivalence ratio on flame propagation dynamics. The results indicate that the wavy structure significantly promotes flame acceleration and instability by enhancing the interaction between pressure waves and the flame front through increased pressure wave reflection frequency, thereby inducing the formation of special structures such as asymmetric tulip flames. Increased amplitude accelerates flame propagation but leads to incomplete combustion; wavelength variations also affect explosion intensity, with the highest explosion hazard occurring at wavelength B= 0.5. An increase in the hydrogen equivalent ratio further intensifies flame velocity and explosion intensity. This study reveals the mechanism by which the coupling of pipeline structure and fuel concentration accelerates flame propagation, providing theoretical support for hydrogen energy safety protection.
{"title":"Study on the explosion characteristics of premixed hydrogen/air mixtures in a wavy confined pipe","authors":"Qiang Liu, Zhuangzhuang Xu, Guogang Yang, Han Sun, Shuyao Zhang","doi":"10.1016/j.psep.2026.108538","DOIUrl":"10.1016/j.psep.2026.108538","url":null,"abstract":"<div><div>In the pipeline, ventilation, and storage systems, pipe structures with curved geometries may unexpectedly act as explosion risk amplifiers. Based on large Eddy Simulation (LES), this study conducted a numerical simulation of the explosion characteristics of hydrogen/air mixtures in wavy pipelines, focusing on analyzing the effects of waveform parameters (amplitude A and wavelength B) and hydrogen equivalence ratio on flame propagation dynamics. The results indicate that the wavy structure significantly promotes flame acceleration and instability by enhancing the interaction between pressure waves and the flame front through increased pressure wave reflection frequency, thereby inducing the formation of special structures such as asymmetric tulip flames. Increased amplitude accelerates flame propagation but leads to incomplete combustion; wavelength variations also affect explosion intensity, with the highest explosion hazard occurring at wavelength B= 0.5. An increase in the hydrogen equivalent ratio further intensifies flame velocity and explosion intensity. This study reveals the mechanism by which the coupling of pipeline structure and fuel concentration accelerates flame propagation, providing theoretical support for hydrogen energy safety protection.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108538"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108498
Yinggang Jiao , Wen Cao , Haitao Liu , Yameng Li , Hong Liu , Bing Hu , Yang Zhang , Xudong Yang , Xiaohang Guo , Quanguo Zhang , Zhiping Zhang
Relying on their highly efficient photosynthetic capacity, microalgae can achieve the efficient conversion of pollutants in wastewater into high-value-added products. However, during the treatment of photofermentation biohydrogen production effluents (PFEs), their high organic load, high chroma and extreme pH values significantly inhibit the pollutant conversion efficiency of microalgae. Hence, this study investigated the cultivation mechanism of Chlorella pyrenoidosa based on in-situ PFEs, and evaluated the pollutant removal capacity and the output of high-value-added products. The results showed that the optimal tolerant concentration of Chlorella pyrenoidosa to PFEs was approximately 40 %. The inhibitory effect of PFEs on the growth of Chlorella pyrenoidosa was mitigated by optimizing light intensity and initial pH value. The maximum biomass yield of 1480.94 ± 5.13 mg/L was achieved under the conditions of 7000 lux light intensity and initial pH= 8. The removal rate of chemical oxygen demand (COD) in PFEs reached 74.30 %, which exceeded the 60 % removal criterion for municipal wastewater treatment plants. The removal efficiencies of typical pollutants including TN, NH4+ -N and PO43--P were 79.75 %, 90.06 % and 77.51 %, respectively. In terms of the output of high-value-added products, the maximum protein content reached 60.35 %, with the highest protein yield of 887.62 ± 15.11 mg/L, which was increased by 235.72 % compared with the group cultured in the traditional BG-11 medium. Compared with the single photofermentation biohydrogen production process, the integrated process of co-producing biohydrogen and microalgae from corn stover improved the overall carbon conversion efficiency by 56.59 %.
{"title":"Biological treatment and purification of photofermentation biohydrogen production effluent to achieve high-value conversion of pollutants","authors":"Yinggang Jiao , Wen Cao , Haitao Liu , Yameng Li , Hong Liu , Bing Hu , Yang Zhang , Xudong Yang , Xiaohang Guo , Quanguo Zhang , Zhiping Zhang","doi":"10.1016/j.psep.2026.108498","DOIUrl":"10.1016/j.psep.2026.108498","url":null,"abstract":"<div><div>Relying on their highly efficient photosynthetic capacity, microalgae can achieve the efficient conversion of pollutants in wastewater into high-value-added products. However, during the treatment of photofermentation biohydrogen production effluents (PFEs), their high organic load, high chroma and extreme pH values significantly inhibit the pollutant conversion efficiency of microalgae. Hence, this study investigated the cultivation mechanism of <em>Chlorella pyrenoidosa</em> based on in-situ PFEs, and evaluated the pollutant removal capacity and the output of high-value-added products. The results showed that the optimal tolerant concentration of <em>Chlorella pyrenoidosa</em> to PFEs was approximately 40 %. The inhibitory effect of PFEs on the growth of Chlorella pyrenoidosa was mitigated by optimizing light intensity and initial pH value. The maximum biomass yield of 1480.94 ± 5.13 mg/L was achieved under the conditions of 7000 lux light intensity and initial pH= 8. The removal rate of chemical oxygen demand (COD) in PFEs reached 74.30 %, which exceeded the 60 % removal criterion for municipal wastewater treatment plants. The removal efficiencies of typical pollutants including TN, NH<sub>4</sub><sup>+</sup> -N and PO<sub>4</sub><sup>3-</sup>-P were 79.75 %, 90.06 % and 77.51 %, respectively. In terms of the output of high-value-added products, the maximum protein content reached 60.35 %, with the highest protein yield of 887.62 ± 15.11 mg/L, which was increased by 235.72 % compared with the group cultured in the traditional BG-11 medium. Compared with the single photofermentation biohydrogen production process, the integrated process of co-producing biohydrogen and microalgae from corn stover improved the overall carbon conversion efficiency by 56.59 %.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108498"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108534
Maulidiah Nani Lailil Islahah , Wei Mo , Yang Yang , Shaojian Ma , Jinlin Yang , Jinpeng Feng , Xiujuan Su
Removing the highly toxic and mobile arsenite (As(III)) from water and wastewater is a persistent challenge. To address this, we designed two novel amorphous Fe–Al binary hydroxides, (Fe5) and (Fe7), using a simple co-precipitation method. The Fe5 adsorbent exhibited outstanding As removal, with record-high capacities of 793.65 for As(V) and 220.75 for As(III) at 318 K. Characterization showed that its amorphous structure, high surface area (∼204.8 ), and controlled Fe/Al stoichiometry work synergistically to enhance ligand exchange and provide redox-active sites. While adsorption for both species followed chemisorption kinetics, the mechanisms differed: As(V) adsorbed via inner-sphere complexation, whereas As(III) removal involved oxidation to As(V) followed by complexation. The material’s practical potential was confirmed through its robust performance under realistic conditions, including high removal (>98.5 %) in the presence of a 10,000-fold excess of competing anions, consistent efficiency across a broad pH range, and excellent regenerability, retaining over 90 % capacity after five cycles. This study provides a high-performance, sustainable adsorbent and clarifies the critical role of stoichiometric control in tailoring amorphous materials for environmental remediation.
{"title":"Amorphous Fe–Al binary hydroxides for enhanced Arsenic removal: Mechanistic role of stoichiometry and synergistic adsorption","authors":"Maulidiah Nani Lailil Islahah , Wei Mo , Yang Yang , Shaojian Ma , Jinlin Yang , Jinpeng Feng , Xiujuan Su","doi":"10.1016/j.psep.2026.108534","DOIUrl":"10.1016/j.psep.2026.108534","url":null,"abstract":"<div><div>Removing the highly toxic and mobile arsenite (As(III)) from water and wastewater is a persistent challenge. To address this, we designed two novel amorphous Fe–Al binary hydroxides, <span><math><mrow><msub><mrow><msub><mrow><mi>Fe</mi></mrow><mrow><mn>5</mn></mrow></msub><msub><mrow><mi>Al</mi></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mo>(</mo><mi>OH</mi><mo>)</mo></mrow><mrow><mn>21</mn></mrow></msub><mo>∙</mo><mn>6</mn><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub><mi>O</mi></mrow></math></span> (Fe5) and <span><math><mrow><msub><mrow><msub><mrow><mi>Fe</mi></mrow><mrow><mn>7</mn></mrow></msub><msub><mrow><mi>Al</mi></mrow><mrow><mn>3</mn></mrow></msub><msub><mrow><mo>(</mo><mi>OH</mi><mo>)</mo></mrow><mrow><mn>30</mn></mrow></msub><mo>∙</mo><mn>6</mn><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub><mi>O</mi></mrow></math></span> (Fe7), using a simple co-precipitation method. The Fe5 adsorbent exhibited outstanding As removal, with record-high capacities of 793.65 <span><math><mrow><mi>mg</mi><mo>∙</mo><msup><mrow><mi>g</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> for As(V) and 220.75 <span><math><mrow><mi>mg</mi><mo>∙</mo><msup><mrow><mi>g</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> for As(III) at 318 K. Characterization showed that its amorphous structure, high surface area (∼204.8 <span><math><mrow><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>∙</mo><msup><mrow><mi>g</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>), and controlled Fe/Al stoichiometry work synergistically to enhance ligand exchange and provide redox-active sites. While adsorption for both species followed chemisorption kinetics, the mechanisms differed: As(V) adsorbed via inner-sphere complexation, whereas As(III) removal involved oxidation to As(V) followed by complexation. The material’s practical potential was confirmed through its robust performance under realistic conditions, including high removal (>98.5 %) in the presence of a 10,000-fold excess of competing anions, consistent efficiency across a broad pH range, and excellent regenerability, retaining over 90 % capacity after five cycles. This study provides a high-performance, sustainable adsorbent and clarifies the critical role of stoichiometric control in tailoring amorphous materials for environmental remediation.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108534"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108439
Ling Xu , Shuyi Wang , Giuseppe Loprencipe , Feipeng Xiao , Yuanwen Lai
The preparation of emulsified asphalt is often challenged by initial aging and high energy consumption, particularly during high-temperature modification and inefficient emulsification. This study evaluates the performance and environmental impacts of emulsified asphalt modified with innovative waterborne polymers, featuring self-crosslinking properties. Four modifiers-epoxy resin, acrylate, nitrile rubber, and polyurethane-were incorporated at 3 %, 6 %, and 9 % dosages. Adhesion tests revealed that all self-crosslinking polymers significantly improved bonding strength, with polyurethane achieving the highest increase (over 30 % compared to the control). Rheological analyses explored using Multiple Stress Creep and Recovery tests showed that acrylate, epoxy resin, and polyurethane enhanced rutting resistance by increasing zero Viscosity and ηM values, while nitrile rubber had a softening effect. Fatigue resistance, assessed through dissipated energy calculations, also improved with crosslinking polymers but declined with nitrile rubber. Conversely, Glover–Rowe parameters at 180 kPa and 450 kPa indicated a moderate increase in low-temperature cracking susceptibility for crosslinking polymers, whereas nitrile rubber reduced cracking potential. Life-cycle assessment demonstrated that the use of waterborne polymers reduced total energy consumption by 16.55 %-17.94 % and carbon emissions by 15.64 %-16.88 % compared to traditional hot-mix processes. Finally, Grey Relational Analysis ranked 9 % polyurethane-modified asphalt as the optimal formulation considering the high-temperature environment, balancing mechanical performance and environmental benefits. Waterborne nitrile rubber showed deteriorated high-temperature rutting resistance but excellent fatigue resistance, which were suitable for anti-cracking at low temperature. These findings confirm that self-crossilinking waterborne polymers can enhance asphalt emulsion performance while reducing lifecycle energy use and emissions.
{"title":"Performance and low-carbon assessment of polymer modification processes for emulsified asphalt based on grey rational analysis","authors":"Ling Xu , Shuyi Wang , Giuseppe Loprencipe , Feipeng Xiao , Yuanwen Lai","doi":"10.1016/j.psep.2026.108439","DOIUrl":"10.1016/j.psep.2026.108439","url":null,"abstract":"<div><div>The preparation of emulsified asphalt is often challenged by initial aging and high energy consumption, particularly during high-temperature modification and inefficient emulsification. This study evaluates the performance and environmental impacts of emulsified asphalt modified with innovative waterborne polymers, featuring self-crosslinking properties. Four modifiers-epoxy resin, acrylate, nitrile rubber, and polyurethane-were incorporated at 3 %, 6 %, and 9 % dosages. Adhesion tests revealed that all self-crosslinking polymers significantly improved bonding strength, with polyurethane achieving the highest increase (over 30 % compared to the control). Rheological analyses explored using Multiple Stress Creep and Recovery tests showed that acrylate, epoxy resin, and polyurethane enhanced rutting resistance by increasing zero Viscosity and η<sub>M</sub> values, while nitrile rubber had a softening effect. Fatigue resistance, assessed through dissipated energy calculations, also improved with crosslinking polymers but declined with nitrile rubber. Conversely, Glover–Rowe parameters at 180 kPa and 450 kPa indicated a moderate increase in low-temperature cracking susceptibility for crosslinking polymers, whereas nitrile rubber reduced cracking potential. Life-cycle assessment demonstrated that the use of waterborne polymers reduced total energy consumption by 16.55 %-17.94 % and carbon emissions by 15.64 %-16.88 % compared to traditional hot-mix processes. Finally, Grey Relational Analysis ranked 9 % polyurethane-modified asphalt as the optimal formulation considering the high-temperature environment, balancing mechanical performance and environmental benefits. Waterborne nitrile rubber showed deteriorated high-temperature rutting resistance but excellent fatigue resistance, which were suitable for anti-cracking at low temperature. These findings confirm that self-crossilinking waterborne polymers can enhance asphalt emulsion performance while reducing lifecycle energy use and emissions.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108439"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108539
Han Li , Linghui Zeng , Zhongqi Wang , Shenghua Fu
A propylene oxide (PO) release may lead to a vapor cloud explosion (VCE), and the resulting shockwave can still cause significant damage in the far field. Motivated by the need to control shockwave consequences for sensitive receptors such as residential areas surrounding chemical parks, this study examines the mitigation effects of protective forest belts located around the park perimeter under an already occurred VCE scenario, and provides a quantitative evaluation based on combined experiments and numerical simulations. A PO/air VCE test was conducted, and a scaled hedgerow blast experiment was employed as a surrogate validation to examine the reasonableness of vegetation-obstacle-induced shockwave attenuation trends. On this basis, an Euler-Lagrange numerical framework was established and validated against measured peak overpressures, with an average relative error of less than 10 %. Using the validated simulations, we systematically evaluated the influence of canopy coverage and tree height on hazard extents: when coverage increased from 0 % to 65 %, the equivalent safe distance decreased from 62.2 m to 54.32 m, and the fatal and severe injury areas were reduced by 55.92 % and 56.97 %, respectively; when tree height increased from 5 m to 35 m, the safe distance decreased from 61.04 m to 58.13 m, and the total hazardous area decreased by 20.54 %. To support rapid engineering assessment, semi-empirical prediction models were further developed for the total hazardous area and three injury-level zones, and the total-area model achieved an R² of 0.992 within the parameter ranges considered in this study. These results indicate that protective forest belts around chemical parks can effectively mitigate far-field shockwave consequences following a VCE, providing quantitative support for safety-distance evaluation and green buffer design.
{"title":"Mitigation of propylene oxide/air vapor cloud explosion shockwave hazards using protective forest belts: Experimental and numerical insights","authors":"Han Li , Linghui Zeng , Zhongqi Wang , Shenghua Fu","doi":"10.1016/j.psep.2026.108539","DOIUrl":"10.1016/j.psep.2026.108539","url":null,"abstract":"<div><div>A propylene oxide (PO) release may lead to a vapor cloud explosion (VCE), and the resulting shockwave can still cause significant damage in the far field. Motivated by the need to control shockwave consequences for sensitive receptors such as residential areas surrounding chemical parks, this study examines the mitigation effects of protective forest belts located around the park perimeter under an already occurred VCE scenario, and provides a quantitative evaluation based on combined experiments and numerical simulations. A PO/air VCE test was conducted, and a scaled hedgerow blast experiment was employed as a surrogate validation to examine the reasonableness of vegetation-obstacle-induced shockwave attenuation trends. On this basis, an Euler-Lagrange numerical framework was established and validated against measured peak overpressures, with an average relative error of less than 10 %. Using the validated simulations, we systematically evaluated the influence of canopy coverage and tree height on hazard extents: when coverage increased from 0 % to 65 %, the equivalent safe distance decreased from 62.2 m to 54.32 m, and the fatal and severe injury areas were reduced by 55.92 % and 56.97 %, respectively; when tree height increased from 5 m to 35 m, the safe distance decreased from 61.04 m to 58.13 m, and the total hazardous area decreased by 20.54 %. To support rapid engineering assessment, semi-empirical prediction models were further developed for the total hazardous area and three injury-level zones, and the total-area model achieved an R² of 0.992 within the parameter ranges considered in this study. These results indicate that protective forest belts around chemical parks can effectively mitigate far-field shockwave consequences following a VCE, providing quantitative support for safety-distance evaluation and green buffer design.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108539"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108517
Remigius Nnadozie Ewuzie , Shivaneswar Gunasekaran , Zainal Ahmad , Norazwan Md Nor
Ensuring operational safety and reliability in chemical industries requires accurate and timely fault detection and diagnosis (FDD). Conventional machine learning and deep learning approaches often struggle to fully exploit temporal–spatial dependencies, provide interpretability, and adapt to nonlinear, time-varying process behaviors. To overcome these limitations, this study presents a novel attention-enhanced deep learning framework that integrates attention mechanisms into LSTM, GRU, and CNN architectures for intelligent FDD in dynamic chemical processes. The framework was validated using the continuous stirred-tank reactor (CSTR) benchmark involving twelve fault classes representing sensor bias, catalyst decay, and process disturbances, and benchmarked against conventional (KNN, ANN, RF) and advanced hybrid models (RATransformer, SST+CNN, KSCW-VAE). The Attention-LSTM achieved superior diagnostic performance with 98.0 % accuracy, 98.3 % F1-score, 98.2 % precision, and an inference latency of 0.493 ms. Its mean detection delay of 1.20 min confirmed rapid fault response, while a G-mean of 0.98 reflected balanced classification. Attention visualization through temporal and feature–time heatmaps enhanced interpretability. The effects of hyperparameters and activation functions were also analyzed, with Tanh and LeakyReLU yielding the best trade-off between accuracy and stability. The proposed framework offers a scalable, interpretable, and computationally efficient solution for real-time fault diagnosis in chemical processes.
{"title":"A novel attention-enhanced deep learning framework for intelligent fault detection and diagnosis in dynamic chemical processes","authors":"Remigius Nnadozie Ewuzie , Shivaneswar Gunasekaran , Zainal Ahmad , Norazwan Md Nor","doi":"10.1016/j.psep.2026.108517","DOIUrl":"10.1016/j.psep.2026.108517","url":null,"abstract":"<div><div>Ensuring operational safety and reliability in chemical industries requires accurate and timely fault detection and diagnosis (FDD). Conventional machine learning and deep learning approaches often struggle to fully exploit temporal–spatial dependencies, provide interpretability, and adapt to nonlinear, time-varying process behaviors. To overcome these limitations, this study presents a novel attention-enhanced deep learning framework that integrates attention mechanisms into LSTM, GRU, and CNN architectures for intelligent FDD in dynamic chemical processes. The framework was validated using the continuous stirred-tank reactor (CSTR) benchmark involving twelve fault classes representing sensor bias, catalyst decay, and process disturbances, and benchmarked against conventional (KNN, ANN, RF) and advanced hybrid models (RATransformer, SST+CNN, KSCW-VAE). The Attention-LSTM achieved superior diagnostic performance with 98.0 % accuracy, 98.3 % F1-score, 98.2 % precision, and an inference latency of 0.493 ms. Its mean detection delay of 1.20 min confirmed rapid fault response, while a G-mean of 0.98 reflected balanced classification. Attention visualization through temporal and feature–time heatmaps enhanced interpretability. The effects of hyperparameters and activation functions were also analyzed, with Tanh and LeakyReLU yielding the best trade-off between accuracy and stability. The proposed framework offers a scalable, interpretable, and computationally efficient solution for real-time fault diagnosis in chemical processes.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108517"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108537
Runzhe Hu , Yudi Tang , Boxue Pang , Shunzheng Jia , Xuebin Wu , Ismet Canbulat , Guangyao Si
Longwall mining faces persistent hazards from spontaneous combustion, methane explosion, and their coupled occurrence. Day-to-day hazard management practice relies on field monitoring, indicators, and empirical rules, while high-fidelity CFD is typically reserved for studies due to its high computational cost and specialist configuration. This study develops an operator-learning surrogate that maps three site parameters, ventilation flux, gas-emission rate, and drainage pressure, to continuous O₂ and CH₄ fields and to oxidation, explosive, and synergistic hazard masks. Trained on 2000 CFD-generated cases and evaluated on 400-case held-out test sets, the surrogate attains an R² of 0.992 for O₂ and 0.998 for CH₄, and achieves precision of 98.6 and 86.5 % for oxidation, 80.9 and 98.8 % for explosive, and 83.8 and 99.0 % for synergistic, reported for the positive and negative classes, respectively. The prediction of a single case completes within 0.015 s, which corresponds to an effective speedup of roughly 1.4 × 10⁶ relative to a six-hour CFD run. Increasing training-case cardinality yields rapid gains for the continuous fields from a few dozen to a few hundred cases, with diminishing returns beyond 200 cases, while positive-class precision for explosive and synergistic rises steadily and stabilises at 200 cases. Varying intra-case sampling density shows a marked deficit at 100–250 points per case and near-ceiling accuracy by 500–1000. The work turns CFD from a specialist, time-consuming research workflow into a practical decision-support tool for rapid scenario evaluation. This enables instantaneous prediction of goaf concentration fields and hazard maps under prescribed operating conditions, supporting shift-time planning and rapid comparison of ventilation and drainage designs with immediate visualisation.
{"title":"From CFD to real-time deployment: Operator learning for instantaneous longwall mine hazard prediction","authors":"Runzhe Hu , Yudi Tang , Boxue Pang , Shunzheng Jia , Xuebin Wu , Ismet Canbulat , Guangyao Si","doi":"10.1016/j.psep.2026.108537","DOIUrl":"10.1016/j.psep.2026.108537","url":null,"abstract":"<div><div>Longwall mining faces persistent hazards from spontaneous combustion, methane explosion, and their coupled occurrence. Day-to-day hazard management practice relies on field monitoring, indicators, and empirical rules, while high-fidelity CFD is typically reserved for studies due to its high computational cost and specialist configuration. This study develops an operator-learning surrogate that maps three site parameters, ventilation flux, gas-emission rate, and drainage pressure, to continuous O₂ and CH₄ fields and to oxidation, explosive, and synergistic hazard masks. Trained on 2000 CFD-generated cases and evaluated on 400-case held-out test sets, the surrogate attains an R² of 0.992 for O₂ and 0.998 for CH₄, and achieves precision of 98.6 and 86.5 % for oxidation, 80.9 and 98.8 % for explosive, and 83.8 and 99.0 % for synergistic, reported for the positive and negative classes, respectively. The prediction of a single case completes within 0.015 s, which corresponds to an effective speedup of roughly 1.4 × 10⁶ relative to a six-hour CFD run. Increasing training-case cardinality yields rapid gains for the continuous fields from a few dozen to a few hundred cases, with diminishing returns beyond 200 cases, while positive-class precision for explosive and synergistic rises steadily and stabilises at 200 cases. Varying intra-case sampling density shows a marked deficit at 100–250 points per case and near-ceiling accuracy by 500–1000. The work turns CFD from a specialist, time-consuming research workflow into a practical decision-support tool for rapid scenario evaluation. This enables instantaneous prediction of goaf concentration fields and hazard maps under prescribed operating conditions, supporting shift-time planning and rapid comparison of ventilation and drainage designs with immediate visualisation.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108537"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108533
Fu-qiang Yang , Hai-feng Liu , Shi-yi Li , Xing-lin Chen , Zong-hou Huang
In order to enhance the reliability of urban gas pipeline networks (UGPNs), this paper proposed a resilience assessment model based on dynamic Bayesian network (DBN). The model is constructed following a novel three-tiered “Accident-Barrier-Resilience” framework. Firstly, a Scenario Evolution and Barrier Deduction Analysis (SEBDA) method is proposed to systematically identify critical risk nodes and construct safety barrier systems based on the physical evolution paths of complete accident scenarios. Subsequently, accident evolution paths are generated via Event Tree Analysis (ETA). Finally, a DBN-based resilience assessment layer quantifies the system's time-varying performance. A computational resilience model was developed to simulate and quantify the resilience characteristics of UGPNs under disruption scenarios. The impact of different accident scenarios on the reliability of resilience nodes was then analyzed. The results show that the three resilience capacities (absorption, adaptation, and restorative) play corresponding functions to drive the overall resilience of the pipeline network system. Criticality analysis of resilience nodes identifies that pipeline diameter and wall thickness are the two most critical factors affecting the resilience of gas pipeline networks. Furthermore, the study incorporates learning ability as a key factor, demonstrating that it effectively influences the resilience attributes of the UGPN system. The dynamic model extends the static model through the incorporation of a resilience network. This enhancement improves operational reliability by explicitly accounting for resilience impacts on UGPNs. The gas network can ultimately have the potential to adequately handle disturbances by consistently implementing the improvement plan.
{"title":"A dynamic Bayesian network-based probabilistic analysis method for urban gas pipelines from the perspective of “Accident-Barrier-Resilience”","authors":"Fu-qiang Yang , Hai-feng Liu , Shi-yi Li , Xing-lin Chen , Zong-hou Huang","doi":"10.1016/j.psep.2026.108533","DOIUrl":"10.1016/j.psep.2026.108533","url":null,"abstract":"<div><div>In order to enhance the reliability of urban gas pipeline networks (UGPNs), this paper proposed a resilience assessment model based on dynamic Bayesian network (DBN). The model is constructed following a novel three-tiered “Accident-Barrier-Resilience” framework. Firstly, a Scenario Evolution and Barrier Deduction Analysis (SEBDA) method is proposed to systematically identify critical risk nodes and construct safety barrier systems based on the physical evolution paths of complete accident scenarios. Subsequently, accident evolution paths are generated via Event Tree Analysis (ETA). Finally, a DBN-based resilience assessment layer quantifies the system's time-varying performance. A computational resilience model was developed to simulate and quantify the resilience characteristics of UGPNs under disruption scenarios. The impact of different accident scenarios on the reliability of resilience nodes was then analyzed. The results show that the three resilience capacities (absorption, adaptation, and restorative) play corresponding functions to drive the overall resilience of the pipeline network system. Criticality analysis of resilience nodes identifies that pipeline diameter and wall thickness are the two most critical factors affecting the resilience of gas pipeline networks. Furthermore, the study incorporates learning ability as a key factor, demonstrating that it effectively influences the resilience attributes of the UGPN system. The dynamic model extends the static model through the incorporation of a resilience network. This enhancement improves operational reliability by explicitly accounting for resilience impacts on UGPNs. The gas network can ultimately have the potential to adequately handle disturbances by consistently implementing the improvement plan.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"209 ","pages":"Article 108533"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108543
Shi Shangguan , Lei Wang , Yanzhong Li , Gang Lei
The intrusion of atmospheric components into liquid hydrogen (LH2) system may pose significant safety hazards to the hydrogen storage and application, especially when solidified air with an oxygen-enriched surface occurs in the LH2 that could amplify the explosion risk. In this paper, Lennard-Jones (LJ) potential parameters for nitrogen and oxygen were optimized, and the maximum deviation of 7 % in predicting the key molecular system properties within the solidification temperature ranges was achieved. Based on the modified potential model, the solidification behaviors of the nitrogen-oxygen mixture under different cryogenic conditions were investigated using molecular dynamics (MD) simulation method. For the molecular system consisting of nitrogen and oxygen molecules, the results showed that the phenomenon of aggregation of nitrogen molecules or oxygen molecules did not occur during the cooling process. For the solidification of nitrogen and oxygen on a cold boundary constructed with copper atoms, comparatively, nitrogen molecules were more prone to concentrating near the cold boundary due to the difference in solidification temperatures of nitrogen and oxygen. For nitrogen and oxygen solidifying in a cold hydrogen atmosphere, it was found the proportion of oxygen molecules on the outer layer of the formed solid-air molecular cluster increased by 5.5 %, while that in the middle layer rose by 17.7 % and that in the inner layer decreased by 72.4 %. This phenomenon indicates that oxygen would gradually diffuse from the interior to the exterior during the cooling process in a hydrogen environment, resulting in an oxygen-rich outer layer in the solidified air. This work reveals the formation characteristics of oxygen-rich surfaces in solidified air deposits, providing critical theoretical support for improving safety assessment and hazard prevention strategies in LH2 system.
{"title":"Atomic-scale insights into solidification behaviors of nitrogen-oxygen mixture: A molecular dynamics study","authors":"Shi Shangguan , Lei Wang , Yanzhong Li , Gang Lei","doi":"10.1016/j.psep.2026.108543","DOIUrl":"10.1016/j.psep.2026.108543","url":null,"abstract":"<div><div>The intrusion of atmospheric components into liquid hydrogen (LH<sub>2</sub>) system may pose significant safety hazards to the hydrogen storage and application, especially when solidified air with an oxygen-enriched surface occurs in the LH<sub>2</sub> that could amplify the explosion risk. In this paper, Lennard-Jones (LJ) potential parameters for nitrogen and oxygen were optimized, and the maximum deviation of 7 % in predicting the key molecular system properties within the solidification temperature ranges was achieved. Based on the modified potential model, the solidification behaviors of the nitrogen-oxygen mixture under different cryogenic conditions were investigated using molecular dynamics (MD) simulation method. For the molecular system consisting of nitrogen and oxygen molecules, the results showed that the phenomenon of aggregation of nitrogen molecules or oxygen molecules did not occur during the cooling process. For the solidification of nitrogen and oxygen on a cold boundary constructed with copper atoms, comparatively, nitrogen molecules were more prone to concentrating near the cold boundary due to the difference in solidification temperatures of nitrogen and oxygen. For nitrogen and oxygen solidifying in a cold hydrogen atmosphere, it was found the proportion of oxygen molecules on the outer layer of the formed solid-air molecular cluster increased by 5.5 %, while that in the middle layer rose by 17.7 % and that in the inner layer decreased by 72.4 %. This phenomenon indicates that oxygen would gradually diffuse from the interior to the exterior during the cooling process in a hydrogen environment, resulting in an oxygen-rich outer layer in the solidified air. This work reveals the formation characteristics of oxygen-rich surfaces in solidified air deposits, providing critical theoretical support for improving safety assessment and hazard prevention strategies in LH<sub>2</sub> system.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108543"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.psep.2026.108524
Junlei Li , Jinpeng Zhao , Chenglong Zhang , Yonghai Zhang , Haixiao Liu , Shuaiwei Gu , Fengqi Li , Pengfei Duan , Jinjia Wei
Hydrogen‑enriched methane (HEM) is a promising bridge fuel for hydrogen‑ready gas networks, yet rapid, accurate prediction of its high‑hydrogen combustion behavior remains challenging. We present a multitask‑learning (MTL) framework that simultaneously predicts laminar burning velocity (LBV) and ignition delay time (IDT) for HEM with 60–100 % hydrogen volume fraction (HVF). The network is trained on data produced with a new 39‑steps skeletal mechanism—extracted from GRI‑Mech 3.0 via DRGEP and sensitivity analysis—and validated against computational‑fluid‑dynamics jet‑flame simulations covering HVF, equivalence ratio (Φ) and nozzle radius. The MTL model yields R² = 0.99 for LBV and perfect ignition‑state classification while preserving the physical coupling between the two tasks. Coupling the MTL outputs with a Damköhler‑number turbulent‑flame‑speed correction enables fast mapping of flame fronts and explosion‑hazard zones in pipeline‑leak scenarios. By integrating reduced chemistry, high‑fidelity computational fluid dynamics (CFD) and data‑driven inference, the workflow offers an efficient, physically consistent tool for Quantitative Risk Assessment of hydrogen‑rich combustion systems.
{"title":"Reduced-mechanism supported multitask learning model for fast flame hazard mapping in hydrogen-enriched methane jets","authors":"Junlei Li , Jinpeng Zhao , Chenglong Zhang , Yonghai Zhang , Haixiao Liu , Shuaiwei Gu , Fengqi Li , Pengfei Duan , Jinjia Wei","doi":"10.1016/j.psep.2026.108524","DOIUrl":"10.1016/j.psep.2026.108524","url":null,"abstract":"<div><div>Hydrogen‑enriched methane (HEM) is a promising bridge fuel for hydrogen‑ready gas networks, yet rapid, accurate prediction of its high‑hydrogen combustion behavior remains challenging. We present a multitask‑learning (MTL) framework that simultaneously predicts laminar burning velocity (LBV) and ignition delay time (IDT) for HEM with 60–100 % hydrogen volume fraction (HVF). The network is trained on data produced with a new 39‑steps skeletal mechanism—extracted from GRI‑Mech 3.0 via DRGEP and sensitivity analysis—and validated against computational‑fluid‑dynamics jet‑flame simulations covering HVF, equivalence ratio (<em>Φ</em>) and nozzle radius. The MTL model yields <em>R</em>² = 0.99 for LBV and perfect ignition‑state classification while preserving the physical coupling between the two tasks. Coupling the MTL outputs with a Damköhler‑number turbulent‑flame‑speed correction enables fast mapping of flame fronts and explosion‑hazard zones in pipeline‑leak scenarios. By integrating reduced chemistry, high‑fidelity computational fluid dynamics (CFD) and data‑driven inference, the workflow offers an efficient, physically consistent tool for Quantitative Risk Assessment of hydrogen‑rich combustion systems.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"208 ","pages":"Article 108524"},"PeriodicalIF":7.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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