Sulphide capacity prediction of CaO–SiO₂–MgO–Al₂O₃ slag system by using regularized extreme learning machine

ABSTRACT

Desulphurization is essential in the steelmaking process for high-quality steel production, and sulphide capacity has proven to be an effective index to evaluate the desulphurization ability of molten slag or flux. Several analytical or empirical models have been proposed to calculate the sulphide capacity. However, these models usually show insufficient generalization ability when new variables/data are introduced, which limits their practical application. In this work, experimental data were collected from the literature and a regularized extreme learning machine (RELM) model was established to predict the sulphide capacity of the CaO–SiO₂–MgO–Al₂O₃ slag system. The results demonstrated that the proposed model is robust for the prediction of sulphide capacity under different conditions. The coefficient of determination (R²), correlation coefficient (r), root-mean-square error (RMSE) of the optimal model reached 0.9763, 0.9881, 0.113, respectively, which outperform the results of the reported models.

KEYWORDS:

• Steelmaking
• slagging
• sulphide capacity
• regularized extreme learning machine
• Python
• desulphurization
• statistical evaluation
• intelligent algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was financially supported by the subject of Tangshan Branch, Hebei Iron and Steel Co., Ltd., China [grant number 2018713] and National Natural Science Foundation of China [grant number 51974023].