基于NSGA-Ⅱ算法的高炉生产配料多目标优化模型建立  

Multi-objective optimization model for blast furnace production and ingredients based on NSGA-Ⅱ algorithm

作　　者：华长春[1] 王雅洁[1] 李军朋[1] 唐英干[1] 卢志刚[1] 关新平[1,2] 

机构地区：[1]燕山大学电气工程学院,河北秦皇岛066004 [2]上海交通大学自动化系,上海200240

出　　处：《化工学报》2016年第3期1040-1047,共8页CIESC Journal

基　　金：国家自然科学基金项目(61322303;61290322)~~

摘　　要：高炉炼铁是一种典型的高能耗、高排放、高污染工业,合理的配料方案对高炉节能减排至关重要。基于高炉炼铁过程中的物质与能量守恒和高炉炉料结构理论,建立了以最小化生产成本和CO2排放量为目标函数的高炉生产配料多目标优化模型,该模型采用非支配排序多目标遗传算法Ⅱ(NSGA-Ⅱ)进行求解,最终得到高炉生产配料多目标优化问题的Pareto最优解集。并将所得到的最优解与柳钢实际生产数据进行比较,结果表明建立的模型能使成本和CO2排放量都有相应程度的降低,验证了该模型及NSGA-Ⅱ算法的正确性。炉长可以根据该多目标优化结果针对不同的需求选择相应的炉料配比,实现更精确的操作。Primary steelmaking is one of the most energy intensive industrial processes in the world and many researches have been done to reduce production cost and CO2 emissions of blast furnace. This paper formulates the above task as a multi-objective optimization problem, the main purpose is to optimize the production cost and CO2 emissions in the process of blast furnace production and ingredients based on the nondominated sorting-based multi-objective genetic algorithm Ⅱ （NSGA-Ⅱ）. It is important to find the Pareto-optimal frontier （PF） and Pareto-optimal solutions （PS） for the multi-objective optimization problem of blast furnace, because different state of operator can be selected in PS to largely reduce the emissions and still keep the steelmaking economically feasible. Furthermore, simulation results verify the effectiveness of the proposed method for the multi-objective optimization model in the process of blast furnace production and ingredients. After optimization, the cost was reduced by about 144 CNY, and CO2 emissions were reduced by 67 kg.

关 键 词：高炉生产配料 NSGA-Ⅱ算法 成本 CO2排放量 多目标优化 PARETO最优解 

分 类 号：TF4[冶金工程—钢铁冶金]