关键词: 高炉煤气, 调度, 动态贝叶斯网络, 多目标差分进化

Abstract: Considering that the scheduling of blast furnace gas(BFG) system is crucial for energy saving in iron and steel industry, this study proposes a novel scheduling method for BFG system based on dynamic Bayesian network(DBN) and improved multi-objective differential evolution(IMODE) algorithm. On account of the dynamic characteristic of the BFG system and the output uncertainty of time prediction model, this study models the BFG system with the causality based DBN method. Simultaneously, the optimization target is that the gas cabinet reaches the desired value fast with a large margin for adjustment. When optimizing the scheduling schemes, the crowding distance of the particles is involved into the searching mechanism of IMODE algorithm to improve the searching precision. Furthermore, in view of the fact that the gas tank cannot run securely by adjusting a single user and the differences of adjustment ability of different users, a multi-user scheduling scheme method is proposed. In order to verify the effectiveness of the proposed method, experiments are carried out with the BFG system production data of a domestic steel enterprise. The results show that the proposed method is more effective than others for the scheduling of the BFG system.

Key words: blast furnace gas, scheduling, Dynamic Bayesian network, multi-objective differential evolution