NSGA-II algorithm-based sintering batching multi-objective optimization method, device and equipment

Abstract

The invention discloses a sintering burdening multi-objective optimization method, device and equipment based on an NSGA-II algorithm and a computer readable storage medium; the sintering burdening multi-objective optimization method based on the NSGA-II algorithm comprises the following steps: determining decision variables and constraint conditions according to the varieties and proportioning factors of sintering burdening; according to the optimization target of the sintered ore, establishing a target function corresponding to the optimization target; constructing a multi-objective optimization model according to the decision variable, the constraint condition and the objective function; and according to a preset iteration termination condition, solving the multi-objective optimization model by using an NSGA-II algorithm to obtain a Pareto optimal solution set. On the premise of ensuring the quality of the sintered ore, the batching cost is reduced, the coke powder consumption is reduced, and energy conservation and emission reduction are realized.

Description

technical field [0001] The invention relates to the technical field of blast furnace ironmaking, in particular to a multi-objective optimization method, device and equipment for sintering batching based on NSGA-II algorithm. Background technique [0002] Sintering is one of the key processes in the production of blast furnace ironmaking concentrates. The main function of sintering is to turn powdery iron-containing ore (iron concentrate and fine ore) into large sintered ore with a certain strength after sintering, so as to meet the requirements of the blast furnace. Requirements for the particle size of the charge. Sintering batching is the first process of sintering. Its purpose is to accurately and reasonably match the ingredients of different varieties, so that the chemical composition of the mixed ore meets the requirements of sintering production, meets the requirements of the blast furnace for the composition of the furnace charge, and improves the performance of the m...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, foreign iron and steel enterprises have stable and sufficient ore sources. They mainly study the microscopic structural characteristics of sintered ore after sintering ore formation with different proportions of ore, and then find the relationship between ore ratio and sinter metallurgical properties, but do not pay attention to cost optimization. Therefore, the composition is stable, the sulfur content is low, and the price is high

However, domestic iron and steel enterprises have many and unstable sources of ore. The main research is to establish a batching optimization model and solve the optimal ratio to achieve the purpose of reducing costs under the premise of ensuring the metallurgical performance of sintered ore. However, in the study of batching optimization, expert experience and linear programming are widely used in batching optimization. However, expert experience is highly subjective, and linear programming algorithms are difficult to adapt to the solution of nonlinear optimization problems. More importantly, the existing batching optimization model uses a single optimization objective to make Cost reductions come at the expense of increased fuel and higher sulfur levels

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