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-SiO 2 -MgO-Al 2 O 3 slag system. The results demonstrated that the proposed model is robust for the prediction of sulphide capacity under different conditions. The coefficient of determination (R 2 ), correlation coefficient (r), rootmean-square error (RMSE) of the optimal model reached 0.9763, 0.9881, 0.113, respectively, which outperform the results of the reported models.

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

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Physicochemical properties and structure of titania-containing metallurgical slags: a review

“…This model claims a high heuristic capability without compositional limitations. Some models are not currently applicable to titania-containing slags, but there is a potential to extend it to titania-containing slags in future work, such as the KTH model [121], molecule coexistence theory model [122], and intelligent algorithms models [123][124]. The KTH model [121] predictions have shown a good agreement with the experimental results, but the calculation process needs lots of interaction parameters.…”

“…The rapid development of intelligent algorithms has attracted more and more researchers in the field of metallurgy. Ma et al [123] and Xin et al [124] established the sulfide capacity models based on artificial neural network and extreme learning machine for CaO-SiO2-MgO-Al2O3 system, respectively. At the same time, the intelligent algorithms method based on big data has great limitations currently on titania slag with insufficient data.…”

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