| Science and technology are primary productive forces.With the advancement of science and technology,the excessive consumption of global resources and environmental problems have become increasingly severe.Under the current strategic goal of sustainable development,enterprises must not only expand production scale and increase benefits,but also reduce costs and reduce environmental pollution.Sintering is one of the important processes in the production of iron and steel.It has a large demand for iron-containing ore raw materials and high production costs.In addition,due to the intensified reduction of domestic mineral resources and the unstable source of iron ore types,it is necessary to import iron ore from abroad.Imported iron ore has many types and high prices.Enterprises must constantly adjust the proportion of iron ore ingredients.Therefore,it is of great significance to study the proportion of sintering ingredients that meet the above requirements.The research content of this article mainly includes:(1)By analyzing the characteristics of the sintering batching process and studying the sintering batching process,firstly,a linear programming(LP)based sintering primary and secondary batching optimization model is built.The principle of conservation of matter is obtained.The latter is obtained through the burn loss rate and algebraic transformation based on the pre-batch optimization model.The mathematical model not only aims to reduce the total cost of sintering,but also strives to reduce the emission of sulfur-containing substances.At the same time,it is constrained by the chemical composition indicators to ensure that the chemical composition of the sintered ore produced meets the requirements and the metallurgical performance reaches the expected state.(2)Based on the established batching optimization model,the advantages and disadvantages of the existing sintering batching optimization methods are analyzed,and a flock optimization algorithm is proposed.The algorithm has a strong optimization ability and can find the optimal solution accurately and quickly.By introducing the working principle of the Chicken Swarm Optimization and analyzing the advantages and disadvantages of the algorithm,it is found that although the optimization algorithm has shown good optimization performance,it has the disadvantage of easily falling into local optimization,and the optimization speed needs to be further improved.Based on this,this paper proposes an improved strategy to improve the convergence speed and optimization accuracy of the algorithm through the elite reverse learning strategy.The adaptive Cauchy mutation strategy is applied to avoid the algorithm falling into the local optimum in the late iteration,namely adaptive Cauchy.Mutated elite reverse learning flock algorithm.(3)The optimization function of the improved algorithm is verified by the test function.The numerical analysis of the simulation experiment shows that the improved algorithm has more powerful optimization ability than other improved algorithms.Another way to improve the flock algorithm is to combine the genetic algorithm with the flock algorithm and analyze its working process.The test function proves that the improved genetic flock hybrid algorithm has a stronger ability to find optimization and is used in the optimization of sintering ingredients.In the model,search for the best raw material ratio of the first and second batch optimization models which compared with the other ratios obtained by other methods.The results prove that the intelligent hybrid optimization algorithm applied in the optimization of sintering batching ratio has theoretical feasibility and practical reliability. |
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