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

Abstract

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.