## 4 Discussion
### Sensitive Risk Factor Analysis  
Sensitivity analysis is a tool for assessing the sensitivity of a model to parameter changes and is categorized into univariate and multivariate analysis. In the chemical safety field, it helps identify key risk factors, assess their impact on safety, guide resource optimization, and process improvement, and reduce the risk of accidents. Univariate analysis measures the sensitivity of each factor to the model, while multivariate analysis integrates the effects of multiple parameters. In addition, parametric and interval sensitivity analyses enhance model robustness and support decision-making. Sensitivity analysis provides a scientific basis for chemical safety, identifies risk factors, optimizes safety management, and reduces accidents. This paper involves multiple risk factors of Bayesian network structure, so multivariate sensitivity analysis is used. The sensitivity factor between two nodes in Bayesian network structure is calculated:  
\[I_{REV}(F_{i})=\frac{\max\bigl{\{}P\bigl{(}S=S_{t}\big{|}F_{i}=f_{ij}\bigr{)} \bigr{\}}-P(S=S_{t})}{P(S=S_{t})} \tag{13}\]  
\[I_{RRV}(F_{i})=\frac{P(S=S_{t})-\min\bigl{\{}P\bigl{(}S=S_{t}\big{|}F_{i}=f_{ ij}\bigr{)}\bigr{\}}}{P(S=S_{t})} \tag{14}\]  
\[I_{AVG}(F_{i})=\frac{I_{REV}(F_{i})+I_{RRV}(F_{i})}{2} \tag{15}\]  
In the above equation, the \(S\) is a child node and \(S_{t}\) is its state; \(F_{i}\) is the child node \(S\) of the \(i\) parent node, and \(f_{ij}\) is the state of each parent node. \(I_{REV}(F_{i})\) denotes the parent node \(F_{i}\)'s risk expansion performance, and \(I_{RRV}(F_{i})\) denotes its risk-reducing performance, and the average of the two, \(I_{AVG}(F_{i})\) is the sensitivity coefficient between the parent node \(F_{i}\) and the child node \(S\). In this paper, we hope to discover the sensitive factors affecting the accident (\"Accident\" node) through sensitivity analysis. Therefore, sensitivity analysis is performed on the Bayesian network using GeNle 4.0, with 'Accident' selected as the target node. As shown in Figure 9, nodes with darker colors exhibit greater sensitivity. Figure 8: Bayesian network structure of chemical company accidents. The computed sensitivity values of the complete set of safety risk factors are visualized in Figure 10. In this paper, those greater than 0.01 are regarded as the susceptible factors of accidents. In descending order, they are non-compliant operation O5, inadequate equipment maintenance and management E5, operators not licensed or not accompanied by supervisors O2, inadequate risk identification for special operations O3, unreliable equipment and facilities E1, adventure organization operations O6, failure to wear protective equipment O1, abnormal pressure/temperature values E3, careless or misrepresentation of the work site inspection M1. Analysis reveals that while numerous critical factors contribute to chemical production accidents, susceptible factors are principally concentrated in the operating personnel, especially the sensitivity value of O5, which exceeds the sensitivity value of M1 by 166.83%. The sensitivity value of E5 exceeds that of M1 by 4.04%. Figure 10: Sensitivity values for security risk factors. Figure 9: Results of Bayesian network sensitivity analysis. According to Human Factors Engineering (HFE) and High-Reliability Organizing (HRO) theories, it is known that in a high-risk organization, the operating personnel are critical because they are directly involved in the operation, and they can quickly respond to accidents and prevent deterioration. High-risk operations in chemical production, such as fire and elevated work, require operators to have specialized skills and mental qualities, including chemical knowledge, equipment operation, and emergency response capabilities. They must be alert, make quick decisions, and respond effectively to emergencies. In addition, continuous training and education are vital to upgrading the quality of operators and keeping up with safety standards.