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About the Journal
Superintended by：China North Industries Group Corporation Limited
Sponsoredy：Beijing Institute of Technology, Chinese Scoiety for Environmental Sciences, China Occupational Safety and Health Association
Edited & Published by: Editorial Department of Journal of Safety and Environment
Issues per year: 12
ISSN 1009-6094
CN 11-4537/X
A coal mine fire risk assessment model based on difference matrix and fuzzy Bayesian network
Li Yan;Wang Xue;Li Hongxia;To enhance the specificity and causal reasoning capabilities of coal mine fire risk assessment, this study constructed an integrated evaluation model that combines rough set differential matrices with fuzzy Bayesian network. Based on the causation mechanisms of coal mine fires and relevant standards, we established an indicator system encompassing four dimensions—fire prevention, emergency response, risk monitoring, and management capacity—comprising 14 secondary indicators. We employed the rough set differential matrix method to reduce indicator attributes, identifying five key indicators from the initial set: ignition source management(A3), ventilation system design and maintenance(A4), emergency plan completeness(B1), intelligent monitoring system application(C2), and personnel training and safety awareness(D2). This process, grounded in data from 45 coal mine accident cases and expert assessments, achieved objective indicator selection through the construction of a differential matrix, calculation of attribute discrimination, and determination of normalized weights. Building upon the identified key indicators, we applied fuzzy set theory to address uncertainties in expert evaluations. We converted expert linguistic assessments into triangular fuzzy numbers and derived fuzzy probabilities for each root node using weighted averaging and defuzzification. Using these as prior probabilities, we constructed a fuzzy Bayesian network structure. Through network parameter learning and conditional probability table configuration, we achieved forward probability prediction and backward causation diagnosis under conditions of uncertainty. Model application and validation results indicate that ventilation system design, emergency plan completeness, and intelligent monitoring system application are high-risk factors, each exhibiting a probability exceeding 50% of being in a “high” risk state. Reverse inference further confirms that when the system is in a high-risk state, the posterior probabilities of these nodes increase significantly, indicating they are key drivers of the risk state. Validation using the Chongqing Songzao Coal Mine “9.27” major fire accident case demonstrates that the model successfully raised the high-risk probability from a baseline state of 27.1% to 89.3% based on anomalies in key indicators. Through posterior analysis, it precisely quantified the risk contribution of each factor. This research integrates the objective attribute reduction capability of rough set differential matrices with the uncertainty reasoning capacity of fuzzy Bayesian network, effectively addressing shortcomings of traditional risk assessments, such as redundant indicators, subjective weighting, and inadequate handling of ambiguous information. The model not only significantly enhances the accuracy and interpretability of coal mine fire risk identification but also facilitates quantitative diagnosis and early warning of key causal factors. It provides a reliable decision support tool for coal mining enterprises to implement differentiated and precise fire risk management. The model and methodology developed herein offer a novel technical approach for tackling the dynamic assessment of multi-factor coupled risks within complex industrial systems.
Explosion risk assessment of powder compaction process for roman candles pyrotechnic composition based on dynamic Bayesian network
Zhang Shulin;Wang Lanning;Zhang Jingying;Li Qi;Guo Xin;Lu Yi;To scientifically evaluate the explosion risk associated with the powder compaction process of Roman Candle pyrotechnic compositions, this study integrated Fault Tree Analysis(FTA) with Dynamic Bayesian Network(DBN) methodologies. Initially, an FTA model was developed to identify and analyze the causal pathways leading to explosion accidents. This model was subsequently transformed into an equivalent Bayesian Network(BN) model. Next, eight experts were invited to assess the basic events using a five-point scale, and the reliability of their scores was verified through consistency checks utilizing a judgment matrix. Finally, the concept of time slices was introduced into the BN model to enhance its analytical capabilities. Basic events were categorized into static nodes and dynamic nodes based on the influence of time on their behavior. The prior probabilities for static nodes and the transition probabilities for dynamic nodes were calculated sequentially. A fuzzy analysis method was employed to convert expert scores into trapezoidal fuzzy numbers, allowing for the integration of expert opinions by assessing the similarity among their scores. This facilitated the computation of prior probabilities for the static nodes based on the consolidated expert assessments. Dynamic nodes were further classified into three categories—equipment failure, human error, and organizational factors—according to the types of accidents, enabling the calculation of their transition probabilities. GeNle software was utilized to simulate changes in accident probability over the next three years, using a 90-day time slice interval, under the assumption of neglecting the repair rate. The results indicated that the initial accident probability was 0.052 8, which increased to 0.268 after three years. Additionally, diagnostic reasoning was employed to calculate the posterior probabilities of basic events. By comparing the relative differences between posterior and prior probabilities, five key hazard sources were identified: low ignition energy of sulfur, improper material selection for mixing, failure of pressure relief devices, inadequate personnel training, and motor sparks resulting from line short circuits. Finally, targeted risk control measures were recommended across three dimensions: standardizing personnel operations, enhancing equipment and processes, and strengthening safety management systems. These measures aim to reduce the probability of fire and explosion incidents effectively.
Risk analysis for railway dangerous goods transportation system based on a FMEA-ETIFIAD approach
Chong Pengyun;Yao Mian;Huang Wencheng;This study introduces a risk analysis method, Failure Mode and Effect Analysis-Extended Triangular Intuitionistic Fuzzy Information Axiomatic Design(FMEA-ETIFIAD), to address the limitations of traditional risk assessment techniques in railway hazardous goods transportation systems. The method incorporates three key innovations to enhance conventional FMEA. First, Triangular Intuitive Fuzzy Numbers(TIFNs) are employed to evaluate the severity(S), occurrence probability(O), and detection possibility(D) of each risk sub-indicator, replacing the traditional discrete real-number scoring system. This fuzzy approach incorporates membership, non-membership, and hesitation values, thereby minimizing information loss and effectively managing uncertainties. Second, the method calculates the information content of S, O, and D using the Information Axiom(IA), moving away from the traditional Risk Priority Number(RPN) that relies on linear multiplication. This modification resolves the issue of multiple risk levels being assigned identical RPNs. Third, the Entropy Weight Method(EWM) is applied to objectively determine the weights of risk sub-indicators based on historical accident data from the Chinese railway hazardous goods transportation system(1985-2017), thereby eliminating biases associated with subjective expert judgments. A case study was conducted utilizing data from the Chinese railway hazardous goods transportation system. Three experts from Southwest Jiaotong University and the Chengdu Railway Bureau assessed the risk sub-indicators using TIFNs. The data were processed using MATLAB R2012b and Microsoft Excel 2013 VB to compute the information content of each risk factor. Results indicate that personnel risk(H) holds the highest total information content(1.448 342 59), followed by equipment risk(M1, 1.229 193 54), cargo risk(M2, 0.967 631 42), management risk(M3, 0.788 793 21), and environmental risk(E, 0.764 327 26). Critical sub-indicators include insufficient technical knowledge of staff, equipment failure, hazardous goods handling, and inadequate railway infrastructure. A comparison with traditional FMEA demonstrates that the FMEA-ETIFIAD method effectively distinguishes risk levels in cases exhibiting identical RPNs and provides a more reliable risk assessment that aligns closely with actual accident data.
Research on a cloud model for assessing airport runway incursion risk using integrated weighting
Zhang Yuhui;Sun Zeyuan;Liao Shujiao;In response to the current inadequacies in quantitative assessment methodologies for runway incursion risks, which hinder the accurate identification of high-risk segments in airport operational management and contribute to frequent safety incidents and operational efficiency constraints, this study proposes an integrated cloud model framework based on integrated weighting principles for refined risk identification. By systematically applying the Latent Dirichlet Allocation(LDA) topic modeling technique, thematic elements and relevant keywords were extracted from a comprehensive collection of 122 global runway incursion accident investigation reports spanning the past two decades. The derived thematic content underwent rigorous validation by a panel of aviation safety experts, ultimately establishing a well-structured runway incursion risk indicator system comprising four primary indicators—human factors, communication failures, airport environment, and management factors—along with 24 carefully defined secondary indicators. Regarding the weighting methodology, an improved Analytic Hierarchy Process(AHP) was employed to calculate subjective weights for each indicator, while the Kendall concordance coefficient was utilized to verify the reliability of these weight assignments. Simultaneously, the enhanced entropy weight method was implemented to derive objective weights based on data characteristics. Through the application of game theory principles, the final combined weights were determined to be 0.153 for subjective dimensions and 0.847 for objective dimensions, respectively. The validity of these final indicator weights was further confirmed through both additive and multiplicative combinatorial weighting approaches. Utilizing the cloud model as the theoretical foundation for risk quantification, empirical data from four representative Chinese airports were selected for comprehensive model validation: a 4E-category airport in North China, a 4F-category airport in East China, a 4C-category airport in Southwest China, and a 3C-category airport in Northwest China. The validation results demonstrated risk level assessments of medium-high(0.665), medium-low(0.553), medium-low(0.715), and medium-high(0.628), respectively, showing strong alignment with actual operational conditions. Furthermore, the model exhibited satisfactory stability under various parameter variations, providing valuable practical references for targeted improvements in specific risk factors across different airport environments, ultimately contributing to the effective reduction of runway incursion risks.
Modelling of thermal decomposition kinetics and study of reaction mechanisms of NCM523
Wang Yan;Zhang Jingyue;Yu Tianmin;Chen Jie;Su Yang;Xu Yabei;To gain an in-depth understanding of the thermal decomposition mechanism of lithium-ion battery cathode materials, this study utilized Thermogravimetric analysis(TG) data of LiNi0.5Co0.2Mn0.3O2(NCM523) obtained at multiple heating rates and integrated these data with a Chemical Reaction Neural Network(CRNN) for kinetic modeling of the pyrolysis process. Through iterative training and validation, a 4-4 model comprising four species and four principal reactions was successfully developed. The model accurately captures the thermal mass-loss behavior of NCM523 and achieves a relative error of less than 1% across all heating rates. Additionally, the modeling results suggest that the pyrolysis follows an irreversible phase-transition pathway, progressing from a layered structure to a lithiated spinel, and ultimately to a rock-salt phase, reflecting the intrinsic structural evolution of the material. The thermal decomposition process can be divided into four distinct stages. In the first stage, the layered structure partially transforms into a lithiated spinel, with a relatively slow reaction rate due to the initial rearrangement of the lattice. In the second stage, both the lithiated spinel and the residual layered phases continue converting into a delithiated spinel, accompanied by an accelerated reaction rate as the system gains thermal activation. The third stage involves the decomposition of intermediate phases into a rock-salt structure, with the concurrent release of oxygen. During this stage, the reaction rate increases sharply in the high-temperature region before becoming diffusion-limited. In the final stage, all intermediate phases fully convert into the rock-salt structure, and the reaction rate gradually stabilizes as structural reorganization is completed. Key kinetic parameters, including activation energies and pre-exponential factors for each reaction step, were autonomously extracted by the CRNN model. These results demonstrate that the CRNN approach not only identifies complex thermal decomposition pathways but also provides physically interpretable kinetic constants without requiring prior assumptions about the reaction mechanisms. Overall, this work highlights the potential of the CRNN as an effective, data-driven tool for analyzing the thermal decomposition behavior of lithium-ion battery cathode materials.
