Prediction Model of Converter Oxygen
Consumption Based on Recursive
Classification and Feature Selection
Zhang Liu, Zheng Zhong, Zhang Kaitian, Shen Xinyue, and Wang Yongzhou
Abstract Oxygen consumption prediction for steelmaking converter is essential for
optimal scheduling and energy saving of oxygen systems. To improve the prediction accuracy of oxygen consumption, an integrated prediction method based on
feature space recursive division and feature selection is proposed. The feature space
containing the whole converter production data is recursively divided into several
feature subspaces containing the training subset. And the complexity of the data
distribution will be reduced in each subspace. The simple data distribution will be
more easily fitted by the prediction model. Based on recursive feature elimination, the
appropriate feature variable combination and the corresponding oxygen consumption prediction models of the converter will be selected for each subset. For the test
sample, it will be matched to a corresponding feature space by recursive division
conditions. Then oxygen consumption is predicted by the corresponding prediction
model based on the optimal combination of feature variables. A converter production
data of a steel enterprise are used for testing. SVR and MLP will be used, respectively, for comparison in two groups of comparative experiments. The results show
that the prediction performance of the integrated model is better than that of a single
prediction model in multiple indicators.
Keywords Steelmaking converter · Oxygen consumption · Integrated prediction ·
Feature space · Recursive partition · Feature selection
Introduction
At present, oxygen blowing steelmaking is the main steelmaking process in the
world [1], so oxygen is an important energy substance in converter steelmaking.
The oxygen consumption of the converter is about half of the oxygen consumption
of steelmaking. And the power consumption in the production of oxygen accounts
for about 1/5 of the total power consumption of steel enterprises. The prediction of
Z. Liu · Z. Zhong (B) · Z. Kaitian · S. Xinyue · W. Yongzhou
College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
e-mail: zhengzh@cqu.edu.cn
© The Minerals, Metals & Materials Society 2021
A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals
& Materials Series, https://doi.org/10.1007/978-3-030-65257-9_10
95
Consumption Based on Recursive
Classification and Feature Selection
Zhang Liu, Zheng Zhong, Zhang Kaitian, Shen Xinyue, and Wang Yongzhou
Abstract Oxygen consumption prediction for steelmaking converter is essential for
optimal scheduling and energy saving of oxygen systems. To improve the prediction accuracy of oxygen consumption, an integrated prediction method based on
feature space recursive division and feature selection is proposed. The feature space
containing the whole converter production data is recursively divided into several
feature subspaces containing the training subset. And the complexity of the data
distribution will be reduced in each subspace. The simple data distribution will be
more easily fitted by the prediction model. Based on recursive feature elimination, the
appropriate feature variable combination and the corresponding oxygen consumption prediction models of the converter will be selected for each subset. For the test
sample, it will be matched to a corresponding feature space by recursive division
conditions. Then oxygen consumption is predicted by the corresponding prediction
model based on the optimal combination of feature variables. A converter production
data of a steel enterprise are used for testing. SVR and MLP will be used, respectively, for comparison in two groups of comparative experiments. The results show
that the prediction performance of the integrated model is better than that of a single
prediction model in multiple indicators.
Keywords Steelmaking converter · Oxygen consumption · Integrated prediction ·
Feature space · Recursive partition · Feature selection
Introduction
At present, oxygen blowing steelmaking is the main steelmaking process in the
world [1], so oxygen is an important energy substance in converter steelmaking.
The oxygen consumption of the converter is about half of the oxygen consumption
of steelmaking. And the power consumption in the production of oxygen accounts
for about 1/5 of the total power consumption of steel enterprises. The prediction of
Z. Liu · Z. Zhong (B) · Z. Kaitian · S. Xinyue · W. Yongzhou
College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
e-mail: zhengzh@cqu.edu.cn
© The Minerals, Metals & Materials Society 2021
A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals
& Materials Series, https://doi.org/10.1007/978-3-030-65257-9_10
95