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Sintered ore is the most important incoming iron material for blast furnaces in China. The foundation characteristics of sintering are an important basis for the sintering process of iron ore powder and a key indicator for evaluating the quality of iron ore powder. The chemical composition of iron ore powder directly affects the foundation characteristics of sintering. Based on the chemical composition and sintering characteristics of iron ore powder, the article adopts the FOA-Catboost algorithm to establish a prediction model for the foundation characteristics of single iron ore powder and mixed iron ore powder. Among the foundation characteristics prediction indicators of a single type of iron ore powder, the accuracy of assimilation temperature prediction is 95.65 %, the accuracy of fluidity index prediction is 95.23 %, and the accuracy of bonding phase strength prediction is 97.46. Among the foundation characteristics prediction indicators of mixed iron ore powder, the accuracy of assimilation prediction is 95.82 %, the accuracy of fluidity index prediction is 91.66 %, and the accuracy of bonding phase strength prediction is 93.7 %. On the basis of predicting the foundation characteristics of a single type of iron ore powder, the model further predicts the foundation characteristics of mixed iron ore powder, which can reduce the workload of laboratory experiments on the foundation characteristics of iron ore powder and reduce the amount of experiments in the process of finding the optimal proportion of mixed ore. Based on the foundation characteristics model of mixed ore, determine the optimal ratio of sintered ore powder to improve the quality of sintered ore. The establishment of the model has a significant contribution to improving experimental and production efficiency, reducing costs, improving sinter quality, and reducing environmental impact.

Sintered ore is the most important incoming iron material for blast furnaces in China. The foundation characteristics of sintering are an important basis for the sintering process of iron ore powder and a key indicator for evaluating the quality of iron ore powder. The chemical composition of iron ore powder directly affects the foundation characteristics of sintering. Based on the chemical composition and sintering characteristics of iron ore powder, the article adopts the FOA-Catboost algorithm to establish a prediction model for the foundation characteristics of single iron ore powder and mixed iron ore powder. Among the foundation characteristics prediction indicators of a single type of iron ore powder, the accuracy of assimilation temperature prediction is 95.65 %, the accuracy of fluidity index prediction is 95.23 %, and the accuracy of bonding phase strength prediction is 97.46. Among the foundation characteristics prediction indicators of mixed iron ore powder, the accuracy of assimilation prediction is 95.82 %, the accuracy of fluidity index prediction is 91.66 %, and the accuracy of bonding phase strength prediction is 93.7 %. On the basis of predicting the foundation characteristics of a single type of iron ore powder, the model further predicts the foundation characteristics of mixed iron ore powder, which can reduce the workload of laboratory experiments on the foundation characteristics of iron ore powder and reduce the amount of experiments in the process of finding the optimal proportion of mixed ore. Based on the foundation characteristics model of mixed ore, determine the optimal ratio of sintered ore powder to improve the quality of sintered ore. The establishment of the model has a significant contribution to improving experimental and production efficiency, reducing costs, improving sinter quality, and reducing environmental impact. [ABSTRACT FROM AUTHOR]

The iron and steel industry has made an important contribution to China's economic development, and sinter accounts for 70–80% of the blast furnace feed charge. However, the average grade of domestic iron ore is low, and imported iron ore is easily affected by transportation and price. The intelligent ore blending model with an intelligent algorithm as the core is studied. It has a decisive influence on the development of China's steel industry. This paper first analyzes the current situation of iron ore resources, the theory of sintering ore blending, and the difficulties faced by sintering ore blending. Then, the research status of the neural network algorithms, genetic algorithms, and particle swarm optimization algorithms in the intelligent ore blending model is analyzed. On the basis of the neural network algorithm, genetic algorithm and particle swarm algorithm, linear programming method, stepwise regression analysis method, and partial differential equation are adopted. It can optimize the algorithm and make the model achieve better results, but it is difficult to adapt to the current complex situation of sintering ore blending. From the sintering mechanism, sintering foundation characteristics, liquid phase formation capacity of the sinter, and the influencing factors of sinter quality were studied, it can carry out intelligent ore blending more accurately and efficiently. Finally, the research of intelligent sintering ore blending model has been prospected. On the basis of sintering mechanism research, combined with an improved intelligent algorithm. An intelligent ore blending model with raw material parameters, equipment parameters, and operating parameters as input and physical and metallurgical properties of the sinter as output is proposed. [ABSTRACT FROM AUTHOR]

The iron and steel industry has made an important contribution to China’s economic development, and sinter accounts for 70–80% of the blast furnace feed charge. However, the average grade of domestic iron ore is low, and imported iron ore is easily affected by transportation and price. The intelligent ore blending model with an intelligent algorithm as the core is studied. It has a decisive influence on the development of China’s steel industry. This paper first analyzes the current situation of iron ore resources, the theory of sintering ore blending, and the difficulties faced by sintering ore blending. Then, the research status of the neural network algorithms, genetic algorithms, and particle swarm optimization algorithms in the intelligent ore blending model is analyzed. On the basis of the neural network algorithm, genetic algorithm and particle swarm algorithm, linear programming method, stepwise regression analysis method, and partial differential equation are adopted. It can optimize the algorithm and make the model achieve better results, but it is difficult to adapt to the current complex situation of sintering ore blending. From the sintering mechanism, sintering foundation characteristics, liquid phase formation capacity of the sinter, and the influencing factors of sinter quality were studied, it can carry out intelligent ore blending more accurately and efficiently. Finally, the research of intelligent sintering ore blending model has been prospected. On the basis of sintering mechanism research, combined with an improved intelligent algorithm. An intelligent ore blending model with raw material parameters, equipment parameters, and operating parameters as input and physical and metallurgical properties of the sinter as output is proposed.