An improved flue gas analysis model based on the correction of the converter mouth flame spectrum

Abstract

Converter steelmaking is a key technology in steel production. Precise prediction of the endpoint carbon content is of great significance for improving steel quality, optimising production efficiency, and reducing costs. In response to the limitations of traditional prediction methods, this study proposes a multi-stage endpoint carbon content prediction model for converter steelmaking based on the grey wolf optimisation (GWO) algorithm and bidirectional long short-term memory (BiLSTM) networks. First, a dynamic cumulative quantity modelling method based on function fitting is employed to divide the steelmaking process into three stages according to CO and CO₂ emission volumes. Next, the autoencoder combined with the optuna optimisation algorithm is used to reduce the dimensionality of 2048-dimensional spectral data. Finally, the GWO algorithm is utilised to optimise the parameters of the BiLSTM model. Experimental results demonstrate that compared with back propagation, long short-term memory, BiLSTM and transformer models, the proposed model exhibits relatively lower mean absolute error (MAE), mean absolute percentage error, and root mean square error (RMSE) across all stages of CO and CO₂ emission prediction. Especially in the third stage, the prediction errors are significantly reduced. For CO and CO₂ gas, the MAE decreases to 18.0017 and 1.7307, respectively, and the RMSE decreases to 20.2151 and 2.1321, respectively. The contributions of various features to the prediction results are clarified through the analysis using the Shapley Additive exPlanations method, thereby enhancing the interpretability of the model. This research provides an efficient and reliable method for the precise prediction of the endpoint carbon content in converter steelmaking, which has important practical significance for optimising the steel-making production process.