Study On Multi-objective Optimization Model For The Process Of Blast Furnace Production And Burden

Blast furnace iron making as the upstream process of the iron and steel industry is the main process of CO2 emissions and energy consumption, so it is the main potential of energy saving and emission reduction in iron and steel industry. In this paper, the process of blast furnace burden is studied, the multi-objective optimization model for the process of blast furnace production and burden is established with the cost of per ton iron and the CO2 emissions of per ton iron as the objective functions, the constraint multi-objective evolutionary algorithm based on decomposition and new environment Pareto dominance is applied for solving the multi-objective optimization model for the process of blast furnace production and burden and get the optimal ratio of burden, then provide guidance for blast furnace energy saving and emission reduction as well as blast furnace operation.The research content of this paper has theoretical significance and application value, the main contents are as follows:Firstly, this paper establishes the multi-objective optimization model for the process of blast furnace production and burden, which regard the mechanism of blast furnace smelting process as the guide and use the material balance, heat balance, hot metal production, hot metal quality, the requirement of blast furnace as the premise. The decision variables of this model are ingredient resourses(iron burdens) and auxiliary resources(fuel, flux, blast etc.), the objective functions are the cost of per ton iron and the CO2 emissions of per ton iron, a new method is proposed for the first time to calculate the CO2 emissions of this model based on emission factors.Secondly, Aiming at the deficiency of improved MOEA/D algorithm MOEA/D-DE in dealing with constrained multi-objective optimization problems, the constraint multi-objective evolutionary algorithm based on decomposition and new environment Pareto dominance MOEA/D-DE-EPDS is proposed in this paper, MOEA/D-DE-EPDS mainly uses the new environment Pareto dominance selection strategy to deal with the constraints, the strategy can make full use of the information of excellent not feasible solutions to obtain the best and even Pareto front quickly. In order to verify theoptimization result of the improved algorithm, this paper uses 13 classical testing functions to test the improved algorithm, and compared with two classical constrained multi-objective optimization algorithm, simulation results show that the improved algorithm proposed in this paper can show a better optimization result for most test functions, especially for constrained multi-objective optimization problems with many constraint conditions or complex PS.Finally, the proposed MOEA/D-DE-EPDS algorithm is used to solve the multi-objective optimization model for the process of blast furnace production and burden,and the Pareto front and Pareto optimal solutions are obtained for this model. On the basis of above we can obtain a set of compromise solution as the optimal proportion of burden,and compared with actual field data to verify the correctness of this model. Further provide reference for iron and steel enterprises to realize energy saving and emission reduction.