Predicting blast furnace permeability index: a deep learning approach with limited time-series data

Abstract

The blast furnace permeability index is one of the crucial technical indicators in the ironmaking process of a blast furnace. Given that the conventional models are not entirely suitable for accommodating the intricate characteristics of blast furnace production, this paper explores a comprehensive approach that involves data mining, the sparrow search algorithm (SSA), convolutional neural networks (CNNs), and gated recurrent unit networks (GRUs) for predicting the blast furnace permeability index. Initially, to address the multi-noise nature of blast furnaces, outliers are eliminated, and a Kalman filter is devised for denoising purposes. Subsequently, in consideration of the nonlinear and substantial time-delay features of blast furnaces, the maximal information coefficient (MIC) method is employed for time-delay alignment, followed by the selection of model input variables based on process analysis and relevance. Subsequent to this, the SSA-CNN-GRU model is established. Within the modeling process, a one-dimensional convolutional neural network is utilized to extract distinct process variable features, thus further resolving the interdependence among blast furnace data. Ultimately, the effectiveness, accuracy, and advancement of the proposed method are validated using real production data.