## 1 Introduction
Long short-term memory networks (LSTMs) [30], convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) [31] models can predict traffic accident risks. A multi-model approach [32] integrating Bayesian Networks (BN), Machine Learning (ML) models, Natural Language Processing (NLP) with Sentiment Analysis, Agent-Based Modeling (ABM), and Survival Analysis to predict causal relationships in high-risk steel industry accidents was used. The Management Oversight and Risk Tree (MORT) was combined with machine learning algorithms [33] to derive a risk prediction model for hydrogen leakage accidents. [PERSON] et al. [34] provided sufficient conditions for stability that are time-delay dependent for GRN systems with specific complex structures, thus, advancing the theory of stability of dynamical systems. The research in this paper is rooted in industrial risk analysis, and its core is to develop and apply a multidisciplinary integrated practical framework to solve specific safety risk quantification problems. However, the high rigor of the study by [PERSON] et al. can provide inspiration for future work in risk quantification. However, these studies failed to provide a comprehensive analysis of the causal logic underlying biogas poisoning and suffocation accidents and have not thoroughly investigated the interplay among management factors contributing to such incidents. Additionally, limited research has been conducted on accident probability prediction and consequence simulation. To address these gaps, this paper proposed a multi-dimensional risk analysis framework: \"accident causality-management-probability-consequence simulation,\" namely, the Bow-Tie-Qualitative Comparative Analysis (QCA)-Bayesian NeuralNetwork-Consequence Simulation model. Compared with traditional methods such as HAZOP [35], FTA [36], and FMEA [37], the Bow-Tie [38] model offers the ability to simultaneously analyze the causal logic and impact of accidents, constructing a complete causal chain. The QCA [39] method excels in exploring the combination paths of causes within accident management through case studies. In contrast to conventional Bayesian models, the Bayesian neural network [40] model leverages deep learning to predict accident probabilities while incorporating Bayesian principles to quantify prediction uncertainties, thereby minimizing subjective influences. Commonly used consequence simulation tools include ANSYS Fluent [41], PHAST [42], and ALOHA [43]. ANSYS Fluent stands out for two reasons: it allows customization of leaked gas components and proportions, and it mandates grid-independent solution verification during simulations, enhancing the reliability of the results. In this paper, the Bow-Tie-QCA-Bayesian Neural Network-Consequence Simulation model was applied to conduct a quantification analysis on the poisoning and suffocation risk of the pre-treatment pool workshop. First, we clarified the causes of biogas poisoning and suffocation accidents in the pre-treatment pool workshop through Bow-Tie. Then, the QCA method explored the accident cause combination paths in management. Next, the Bayesian neural network model was used to predict the accident frequency distribution. Finally, the diffusion consequences of biogas in the pre-treatment pool workshop were numerically simulated in three different ventilation types using ANSYS Fluent. In addition, according to the results, this paper discussed the importance and necessity of ventilation in pre-treatment pool workshops, and specified the hazard factors in biogas poisoning and suffocation accidents in the pre-treatment pool workshop. Some suggestions on gas alarms were also proposed. This study is expected to provide a basis for the prevention of biogas poisoning and suffocation accidents, and a safe working environment for workers.