Control of silicon content in blast furnace iron based on GRA–LSTM–BAS prediction methods

Abstract

In blast furnace smelting, the silicon content in molten iron is an important indicator of the temperature trend of the blast furnace. Due to the multi scale, non-linear, large time delay and strong coupling characteristics of the blast furnace smelting process, the control effect of silicon content in hot metal is often not ideal. Therefore, finding an effective and accurate method for controlling silicon content in hot metal is very important for blast furnace smelting. Based on this, this paper proposes a prediction and control model for silicon content in hot metal of blast furnace based on GRA–LSTM–BAS. Based on this, this paper proposes a prediction and control model for silicon content in hot metal of blast furnace based on GRA–LSTM–BAS. Firstly, the original data set is processed using wavelet analysis and normalisation processing methods. Secondly, the gray relational analysis (GRA) method is used to analyse the correlation between the input variables of the model to determine the input parameters of the model. Subsequently, a long short-term memory (LSTM) prediction model was established to obtain silicon content values at future times through feedback correction. The model was trained and tested by on-site collected data and compared with the support vector machine (SVM) model. The results show that the LSTM model can quickly and accurately predict the silicon content in hot metal, and has a good guiding significance for actual blast furnace production. Finally, the control model for silicon content in molten iron is optimised iteratively by combining the beetle antennae search algorithm (BAS algorithm). Feedback and update of the results in the model are done in real time according to errors, forming a closed-loop controller to maintain the silicon content in molten iron at an appropriate level and achieve optimal control of the silicon content.