Denoising Multiscale Spectral Graph Wavelet Neural Networks for Gas Utilization Ratio Prediction in Blast Furnace

Given the crucial role of the gas utilization ratio (GUR) in reflecting blast furnace operation and energy consumption, accurately predicting its development trend holds significant value for blast furnace operators. However, in the harsh ironmaking environment, GUR-affecting variables are prone to significant nonstationary noise. Moreover, these variables are coupled and correlated, meaning that improper regulation of one variable can destabilize the furnace and lead to substantial GUR fluctuations. This poses a major challenge for achieving accurate GUR prediction. To tackle this issue, this article proposes a denoising multiscale spectral graph wavelet neural network (DMSGWNN) for online dynamic forecasting of the GUR, which is an end-to-end learning method that removes variable noise and captures complex variable correlations simultaneously. First, a regularized self-representation (RSR) model is constructed to eliminate nonstationary noise in blast furnace process variables. Then, a novel multiscale spectral graph wavelet neural network (MSGWNN) is proposed to capture the complex correlations among input variables and extract their multiscale representations through spectral graph wavelet (SGW) transform with the heat kernel scaling function and Gaussian kernel wavelet functions. Finally, the effectiveness of the proposed DMSGWNN method is verified using actual blast furnace ironmaking process data from a blast furnace in China, achieving an average predictive hit rate (HR) as high as 98.06% for GUR prediction.