High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Abstract

The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

“…Optical sensors provide high detection precision and are unaffected by the electrical characteristics of the medium; however, they cannot penetrate in dusty and smoky environments and their use is limited in liquid media detection. Radar sensors are highly accurate and can adapt to extreme conditions, featuring non-contact measurement, making them suitable for the detection environment of high-viscosity and corrosive liquids, and are the preferred choice in industries such as chemical smelting [5,6].…”