Iron and steel smelting is an industry with high energy consumption and high pollution. In the process of industrial transformation, it is of great theoretical significance and practical application value to carry out the concept of "green manufacturing" in the main links such as sintering, coking, iron making and steel making, and use advanced control technology to improve production efficiency and reduce pollution emission. This paper mainly studies the prediction and optimization of blast furnace parameters based on machine learning and genetic algorithm. In this paper, the optimization method based on genetic algorithm is established by deeply learning genetic algorithm in inverse calculation of distribution matrix. In this paper, the error of charge distribution is taken as the optimization objective, and the genetic algorithm is used to solve the model, and the purpose of charging the bellless blast furnace according to the expected ore/coke ratio distribution is realized.

Jing Zhang. Prediction and Optimization of Blast Furnace Parameters Based on Machine Learning and Genetic Algorithm. Machine Learning Theory and Practice (2020), Vol. 1, Issue 1: 33-40. https://doi.org/10.38007/ML.2020.010104.

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