The accident early warning system for iron and steel enterprises based on combination weighting and Grey Prediction Model GM (1, 1)

Abstract

In order to prevent the occurrence of accidents in iron and steel enterprises, it is essential to change the risk management pattern from post-emergency response to hazard control and prevention. Based on the characteristics of iron and steel enterprises, this paper investigates the early warning system for accidents for iron and steel enterprises, aiming for the adoption of accident prevention and hazard control. An early warning index system and an early warning model were constructed based on production types and accident statistics of the enterprises. On account of the factors that influence accidents, this early warning index system contains 3 hierarchies with 5 composite indexes and 22 thematic indexes. The indexes have been quantified, regularized, and their weights were determined using a combination weighting method based on the Analytic Hierarchy Process and the Entropy Weight Method. The early warning index model was established according to Grey System Theory GM (1,1), and the comprehensive early warning indexes were calculated by Multi-objective Linear Weighted Function. The thresholds were then determined, the early warning levels were identified, and the early warning signals were output accordingly. The feasibility and validity of the proposed early warning model was tested and verified through its application in a functioning industrial plant.