基于灰色聚类的Y化工企业安全生产风险管理研究

Research on Safety Production Risk Management of Y Chemical Enterprise Based on Grey Clustering

摘要: 在全球产业格局深度调整与国内化工行业向高质量发展转型的背景下，把安全生产作为保障产业持续稳定运行的核心底线，其系统性风险管控已成为行业发展的关键议题。橡胶硫化促进剂作为橡胶工业不可或缺的核心助剂，其生产该产品的化工企业的安全风险具有隐蔽性、累积性与连锁性等典型特征，传统安全管理模式已难以适配其精细化管控需求，需构建科学、高效的全流程风险管理体系。本文以特定橡胶硫化促进剂生产企业为研究对象，构建了“风险识别–风险评估”的全流程风险管理框架。采用文献搜集法与专家访谈法，从人的因素、物的因素、环境因素、管理因素、技术因素五大维度，系统识别出18项关键安全生产风险指标，形成全面覆盖企业生产运营各环节的风险清单。在风险评估阶段，创新融合ANP法与CRITIC法确定指标综合权重——ANP法充分吸纳专家经验以体现主观判断，CRITIC法基于数据特性规避人为误差以保证客观公正；再通过灰色聚类法将风险划分为较低、低、中、重大、巨大五个等级，明确各风险的优先级排序。本文构建的风险评价体系与管控策略，不仅能帮助Y化工企业提升抗风险能力，也为同类橡胶硫化促进剂生产企业的安全生产风险管理提供了可借鉴的实践模板，丰富了精细化工企业安全风险研究的理论与应用成果。

Abstract: Against the backdrop of profound adjustments in the global industrial landscape and the transformation of the domestic chemical industry toward high-quality development, safety in production serves as the core bottom line for ensuring the sustained and stable operation of the industry, and its systematic risk control has become a key issue for industrial development. As an indispensable core additive in the rubber industry, rubber vulcanization accelerators are produced by chemical enterprises whose safety risks are characterized by concealment, accumulation, and cascading effects. Traditional safety management models can hardly meet the refined control requirements, necessitating the construction of a scientific and efficient full-process risk management system. This paper takes a specific rubber vulcanization accelerator manufacturing enterprise as the research object and constructs a full-process risk management framework of “risk identification-risk assessment”. Using the methods of literature collection and expert interviews, 18 key safety production risk indicators are systematically identified from five dimensions: human factors, material factors, environmental factors, management factors, and technical factors, forming a risk list that comprehensively covers all links of the enterprise’s production and operation. In the risk assessment stage, the ANP method and the CRITIC method are innovatively integrated to determine the comprehensive weights of indicators—the ANP method fully absorbs expert experience to reflect subjective judgments, while the CRITIC method avoids human errors based on data characteristics to ensure objectivity and fairness; then, the grey clustering method is adopted to classify risks into five levels: lower, low, medium, major, and catastrophic, clarifying the priority ranking of each risk. The risk evaluation system and control strategy constructed in this paper can not only help Y Chemical Enterprise enhance its risk resistance capacity but also provide a practical template for safety production risk management of similar rubber vulcanization accelerator manufacturing enterprises, enriching the theoretical and applied achievements of safety risk research in fine chemical enterprises.