## 1 Introduction  
Metal smelting enterprises involve complex production processes that pose various risks [1, 2]. Toxic gas leakage is one of the most serious accidents in steel plants, which may lead to secondary accidents, including fires, explosions, and gas poisoning. The crude steel production in many countries has increased to varying degrees recently [3]. Simultaneously, the risk of toxic gas leaks in steel plants increases with production growth [4, 5, 6].  
Quantitative risk analysis (QRA) is widely applied in estimating the risk of an accident and the severity of the incident [7, 8]. [PERSON] [9] has constructed a fuzzy membership function based on the closest point of approach (CPA) and the time to CPA (TCPA) in order to recognize dangerous situations in vessel traffic surveillance. [PERSON] [10] has adopted an analytical hierarchy process (AHP) to manage crisis and support the decision-making process. [PERSON] [11] has improved the rockfall risk assessment system for Pakistan by quantifying animal activity along the highways. The entropy weighting method (EWM) is one of the weighting methods adopted in the fuzzy methods [12]. [PERSON] et al. [13] have adopted the improved entropy weight method to evaluate the risk of urban floods, while [PERSON] et al. [14] have used the entropy method to quantitatively analyze the uncertainty in the process of weight calculation. The order relationship method (ORM) is used as a tool in assessing and evaluating risk levels [15]. [PERSON] et al. [16] have applied the ORM to calculate indicator weights in order to evaluate the city innovation capability in Liaoning province, China. [PERSON] [17] has simulated the gas concentration changes at various points in order to guide emergency rescues after gas leaks. [PERSON] et al. [18] have developed a system based on QRA to suggest an optimal evacuation route in the circumstances of gas leakage.  
However, the application of the fuzzy evaluation method in the risk assessment of toxic gas leaks in steel manufacturing plants to prevent accidents is rare, which makesthe existing risk of such gas leakage accidents challenging to detect, and the identification of risk factors is not comprehensive. The risk analysis time is long in the existing risk assessment approach, and the leakage accident cannot be warned in time and effectively. Consequently, it is necessary to carry out a comprehensive risk factor analysis of gas leakage in the metal smelting process and adopt principles of fuzzy mathematics to determine and calculate the importance of each risk factor based on the various complex causes of gas leaks in metal smelting enterprises.  
The objective of the present research is to evaluate the risk of toxic gas leakage in a steel plant in order to promote the efficiency of gas leak risk assessment. We propose a comprehensive weighting method (CWM), integrating the entropy weighting method (EWM) and the order relationship method (ORM) to calculate the weight vectors (Section 2). By analyzing toxic gas leak accidents and related literature surveys [19; 20], the evaluation indexes are confirmed using the Delphi method [21] and the clustering method [22], and a toxic gas leakage index system is established based on these evaluation indexes (Section 3). A risk assessment system (RAS) for toxic gas leakage is developed (Section 3). The system iss applied to evaluate each factor's risk level and importance ranking, and the results prove effective (Section 4). RAS enriches the risk factors of the existing safety checklist and sorts the importance of the indexes, which can optimize the distribution of management resources. The time cost of a gas leakage risk assessment is reduced after applying RAS (Section 5).