Abstract
The stockline, which describes the measured depth of the blast furnace (BF) burden surface with time, is significant to the operator executing an optimized charging operation. For the harsh BF environment, noise interferences and aberrant measurements are the main challenges of stockline detection. In this paper, a novel encoder–decoder architecture that consists of a convolution neural network (CNN) and a long short-term memory (LSTM) network is proposed, which suppresses the noise interferences, classifies the distorted signals, and regresses the stockline in a learning way. By leveraging the LSTM, we are able to model the longer historical measurements for robust stockline tracking. Compared to traditional hand-crafted denoising processing, the time and efforts could be greatly saved. Experiments are conducted on an actual eight-radar array system in a blast furnace, and the effectiveness of the proposed method is demonstrated on the real recorded data.
Highlights
Blast furnaces (BFs) are the key reactors of iron and steel smelting, which consumes about70% of the energy in the steel-making process [1,2].In iron-making, solid raw materials, e.g., iron ore, coke, limestone, are violently burned and consumed from time to time, and the charging operation needs to be executed by accurately estimating the current depth of the burden surface
The traditional peak searching method with pass-band finite impulse response (FIR) filters and Kalman filters were implemented for comparison
The single convolution neural network (CNN) model and single long short-term memory (LSTM) model were constructed for another baseline comparison and their structures were identical to the corresponding part of the hybrid model
Summary
Blast furnaces (BFs) are the key reactors of iron and steel smelting, which consumes about. In the study of BFs, a variety of algorithms of noisy data processing have been developed so far, covering principal component analysis [6], support vector machines (SVM) [7,8], neural network models [9], extreme learning machines [10], etc. It is acknowledged by remarkable previous works that the noises occurring within the BF reactor are still an extremely complex issue. To present a novel encoder–decoder architecture to improve stockline detection, which learns desired features from noisy data adaptively. A conclusion is provided in Section 5 to summarize this work
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