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针对钢铁企业二次配料工艺,本文采用将硫含量折算为可比成本,兼顾节能减排目标和配料成本,建立了二次配料多目标优化模型;提出了一种基于线性规划和遗传–粒子群算法(GA–PSO)的钢铁烧结配料优化方法.首先采用线性规划算法进行求解,若线性规划方法无法求得最优解,则采用GA–PSO算法进行搜索.该方法应用于某钢铁企业360m2生产线的“配料优化与决策支持系统”中,实际运行结果表明,该算法在保证烧结矿质量的前提下,能够有效地减少二氧化硫排放,降低配料成本.
Aiming at the secondary compounding process of iron and steel enterprises, a multi-objective optimization model of secondary compounding was established by converting the sulfur content into a comparable cost and taking into account the energy saving and emission reduction targets and the cost of ingredients. A linear programming and genetic-particle swarm optimization (GA-PSO) .At first, a linear programming algorithm is used to solve the problem. If the linear programming method can not find the optimal solution, the GA-PSO algorithm is used to search the method. This method is applied to the 360m2 production line of a steel company The results show that the algorithm can effectively reduce sulfur dioxide emissions and reduce the cost of ingredients under the premise of ensuring the quality of sinter.