期刊
TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS
卷 75, 期 9, 页码 2281-2288出版社
SPRINGER INDIA
DOI: 10.1007/s12666-022-02603-8
关键词
End-point control model; Twin support vector regression; Whale optimization algorithm; Basic oxygen furnace
资金
- 14th Five-Year National Key RD Plan [2021YFB3702005]
- Liaoming Province Science and Technology Major Special Project [2020JH1/10100001]
- Liaoning Province PhD start-up Fund [2021-BS-244]
- Liaoning Province Education Department of China [2020LNZD05]
This paper proposes a hybrid end-point static control model for accurate control of the basic oxygen furnace (BOF) end-point in steelmaking. By establishing a prediction model and optimizing the objective function, the optimal values for oxygen blowing volume and lime weight are calculated, meeting the requirements of actual field production.
Accurate control of the basic oxygen furnace (BOF) end-point can effectively improve the quality of steel. A hybrid end-point static control model is proposed to calculate the oxygen blowing volume and lime weight, and realizes the control of the end-point of BOF. Firstly, the prediction model is established based on twin support vector regression (TSVR). Secondly, the difference between the predicted value and actual value of the obtained model is used as the objective function, combined with the whale optimization algorithm (WOA) and incremental algorithm to optimize the objective function. Finally, the optimal value vector of oxygen blowing volume and lime weight is obtained. The simulation calculation is carried out by using the actual production sample of an 80 tons BOF. The results show that the proposed prediction model has high prediction accuracy, and the calculated oxygen blowing volume and lime weight can meet the actual field production.
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