基于改进PSO优化ELM的高炉喷煤量预测研究  

Research on the prediction of coal injection rate of blast furnace based on improved PSO to optimize ELM

作　　者：单宝明[1] 薛永杰 Shan Baoming;Xue Yongjie(College of Automation and Electronical Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,青岛266061

出　　处：《电子测量技术》2021年第3期93-98,共6页Electronic Measurement Technology

摘　　要：高炉炼铁过程中喷煤量的设定值通常由炉长经验得出,具有一定的盲目性和模糊性,为了增加喷煤量设定的合理性,提出了一种基于改进粒子群算法优化极限学习机的高炉喷煤量预测模型。基于某高炉实际运行数据,采用混沌惯性权重和自适应学习因子来改进粒子群优化(PSO)算法的收敛性,并通过引入遗传算法的交叉变异操作提高算法的全局最优性,然后利用改进的粒子群算法优化极限学习机(ELM)构建基于改进粒子群算法的极限学习机预测模型(IPSO-ELM)。最后,基于某高炉运行数据将所提出的IPSO-ELM预测模型与传统粒子群优化极限学习机预测模型(PSO-ELM),以及极限学习机预测模型进行比较。结果表明,该IPSO-ELM预测模型具有更高的预测精度,能够实现对高炉喷煤量变化的准确预测,有较高的工业应用价值。The setting value of the coal injection rate in the blast furnace ironmaking process is usually derived from the experience of the furnace manager, which has certain blindness and ambiguity. In order to increase the rationality of the coal injection rate setting, this paper proposes an improved particle swarm optimize the prediction model of blast furnace coal injection rate based on extreme learning machine. Based on the actual operating data of a blast furnace, this paper uses chaotic inertia weights and adaptive learning factors to improve the convergence of the particle swarm optimization(PSO) algorithm, and introduces the cross-mutation operation of genetic algorithms to improve the global optimality of the algorithm, and then uses the improvement. The particle swarm algorithm optimized extreme learning machine(ELM) builds an extreme learning machine prediction model(IPSO-ELM) based on the improved particle swarm algorithm. Finally, based on the operating data of a certain blast furnace, the IPSO-ELM prediction model proposed is compared with the traditional particle swarm optimization extreme learning machine prediction model(PSO-ELM) and the extreme learning machine prediction model. The results show that the IPSO-ELM prediction model has higher prediction accuracy, can achieve accurate prediction of the change of the blast furnace coal injection rate, and has high industrial application value.

关 键 词：喷煤量预测 改进粒子群算法 极限学习机 高炉 

分 类 号：TF512[冶金工程—钢铁冶金]