基于微粒群算法的转炉合金加入量多目标优化模型  

Multi-objective optimization model for alloy addition of BOF based on particle swarm optimization algorithm

作　　者：韩敏[1] 徐俏[1] 赵耀[1] 杨溪林[2] 林东 

机构地区：[1]大连理工大学电子与信息工程学院,辽宁大连116023 [2]北京金自天正智能控制股份有限公司,北京100070 [3]本钢板材股份有限公司炼钢厂,辽宁本溪117021

出　　处：《控制与决策》2010年第12期1901-1904,共4页Control and Decision

基　　金：国家863计划项目(2007AA04Z158);国家自然科学基金项目(60674073)

摘　　要：针对转炉炼钢过程中钢水出钢环节脱氧合金剂加入量确定的问题,建立了合金加入量优化计算模型.首先,利用支持向量机建立钢包元素含量预测模型,将该模型的预测结果与元素规定含量的误差作为优化模型的一个目标函数,合金成本作为另一个目标函数;然后利用预测收得率方法计算合金加入量并作为优化模型的初始值,使用改进的动态邻域多目标微粒群算法对优化模型进行求解,有效地找出Pareto最优解集,并根据实际需要得出较好的优化结果.仿真实验表明了该优化模型的有效性.In the process of BOF steelmaking,in order to figure out and optimize the amount of alloy addition during deoxidizing and alloying of tapping,alloy addition multiobjective optimization model is developed.Firstly,the prediction model of ladle element content is developed with support vector machine（SVM）.One objective function of multiobjective problem is the error between the regulations and the result of the prediction model of ladle element content,and the other objective function is the cost of alloy addition.Then the result of alloy addition calculation is made as initial value of the optimization model,and the alloy addition is optimized by using modified dynamic neighborhood multiobjective optimization-particle swarm optimization（MO-PSO） so as to determine the Pareto optimal solutions effectively.The final amount of alloy addition is decided according to the practical requirement.Finally,experimental results show the effectiveness of the proposed model.

关 键 词：合金加入量 支持向量机 微粒群优化算法 多目标优化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]