University of Science and Technology Beijing

Zicheng Xin

Hui Li

Jiangshan Zhang

Chunhui Zhang

Junguo Zhang

A method for controlling a temperature of a molten steel during ladle furnace (LF) refining based on interpretable machine learning includes: acquiring process data of the LF refining and a target temperature of the molten steel during the LF refining; acquiring a prediction model for the temperature of the molten steel during the LF refining; calculating a base value for prediction of the temperature of the molten steel, SHapley Additive explanations (SHAP) values of key factor parameters, and a relationship trend between the key factor parameters and the SHAP values; and calculating a predicted value of the temperature of the molten steel during the LF refining, and acquiring a control result for the temperature of the molten steel during the LF refining according to the relationship trend and the predicted value of the temperature of the molten steel during the LF refining.

Method and device for controlling temperature of molten steel during ladle furnace (LF) refining based on interpretable machine learning

A method and system for predicting an addition amount of slagging lime during ladle furnace (LF) refining, and an LF refining method are provided. The method includes: S: calculating an actual sulfur distribution ratio in combination with a Kungliga Tekniska H&#xf6;gskolan (KTH) model and a least square method by using LF refining parameters; S: calculating, according to a principle of sulfur mass conservation, a mass of final slag by using the LF refining parameters and the actual sulfur distribution ratio obtained in S; and S: calculating, according to a principle of material conservation during LF refining, an addition amount of slagging lime during the LF refining by using the LF refining parameters and the mass of the final slag obtained in S, thereby predicting the addition amount of the required slagging lime.

Growing community of inventors

Beijing, China

Zicheng Xin