Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features
<p>Research framework of the blast furnace burden level prediction and detection.</p> "> Figure 2
<p>Detection data of radar and mechanical probe.</p> "> Figure 3
<p>Schematic of the data processing process of the sliding window.</p> "> Figure 4
<p>Space feature regression model curve of radar data.</p> "> Figure 5
<p>Structure of the long-term focus memory network.</p> "> Figure 6
<p>Network structure of ESST-RBFNN.</p> "> Figure 7
<p>Trend of burden level prediction under normal working conditions.</p> "> Figure 8
<p>Trend of burden level prediction under abnormal working conditions.</p> "> Figure 9
<p>Comparison chart of different methods for measuring blast furnace burden level under normal working conditions.</p> "> Figure 10
<p>Comparison chart of different methods for measuring blast furnace burden level under abnormal working conditions.</p> "> Figure 11
<p>Compassion diagram of absolute error.</p> "> Figure 12
<p>Compassion diagram of relative error.</p> "> Figure 13
<p>The 45° line diagram of the measurement effect of the three methods.</p> "> Figure 14
<p>Operation effect diagram of the detection method of ESST-RBFNN based on the validation data.</p> ">
Abstract
:1. Introduction
2. Space Dimensional Feature Extraction
2.1. Analysis of the Mechanical Probe Data and Radar Data
2.2. Space Feature Regression Model of the Radar Data
3. Time Dimensional Feature Extraction and Burden Level Prediction Based on Long-Term Focus Memory Network
4. ESST-RBFNN
4.1. Fast Eigenvector Space Clustering Algorithm (ESC)
4.2. Efficient Structure Self-Tuning RBF Neural Network (ESST-RBFNN)
5. The Simulation and Industrial Verification Results
5.1. Verification Results of the Blast Furnaces Burden Level Prediction and the Time Features Attraction Based on LFMN
5.2. Verification Results of the Blast Furnace Burden Level Detection Based on ESST-RBFNN
6. Conclusions and Discussion
6.1. Conclusions
6.2. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Abbreviations | |
PRNN | piecewise regression nonlinear |
LFMN | long-term focus memory network |
ESC | eigenvector space clustering |
RBFNN | radial basis function neural network |
ESST | efficient structure self-tuning |
MRE | mean relative error |
RMSE | root mean square error |
Symbols | |
time sequence of the radar data in a single period (-) | |
piecewise nonlinear regression fitting of radar data ( ) | |
the initial position ( ) | |
falling speed of the burden level ( ) | |
falling acceleration of the burden level ( ) | |
falling jerk of the burden level ( ) | |
input sample datasets (-) | |
Categories (-) | |
Centralized input data set (-) | |
number of input sample in category (-) | |
original indicator vector matrix of the category feature space (-) | |
corresponding sample belonging to the category (-) | |
transformation matrix (-) | |
jerk changing rate of burden level ( ) | |
high-order term of burden level ( ) | |
beginning time of the th cycle ( ) | |
ending time of the th cycle ( ) | |
dividing time between feeding and idle period of th cycle ( ) | |
measurement error ( ) | |
burden level value in the falling period of the th cycle ( ) | |
burden level value in the rising period of the th cycle ( ) | |
input samples of LFMN network (-) | |
opening material flow valve ( ) | |
furnace top temperature ( ) | |
furnace top air volume ( ) | |
state variables saving the short-term memory of LFMN network (-) | |
state variables saving the long-term focus memory of LFMN network (-) | |
the weight matrix of forgetting gate in network training (-) | |
the weight matrix of input gate in network training (-) | |
the bias of the forgetting gate (-) | |
the bias of the input gate (-) | |
input sample set belonging to category (-) | |
th output burden level data of the th cycle (-) | |
width of the th basis function (-) | |
maximum distance from clustering center to the sample point (-) | |
network training set (-) | |
training input sample set (-) | |
real symmetric matrix (-) |
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Method | Statistical Indices | ||
---|---|---|---|
MRE | RMSE | Cycle-Fit | |
Radar Probe | 7.182% | 0.176 | 99.53% |
LFMN | 8.574% | 0.236 | 98.87% |
Method | Statistical Indices | |||
---|---|---|---|---|
MRE | RMSE | Error-2% | Error-5% | |
Radar | 8.573% | 0.1847 | 13.97% | 32.53% |
RBFNN | 7.291% | 0.1825 | 28.91% | 66.50% |
Proposed | 2.361% | 0.0480 | 91.17% | 99.33% |
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Chen, Y.; Chen, Z.; Gui, W.; Yang, C. Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features. Sensors 2022, 22, 5412. https://doi.org/10.3390/s22145412
Chen Y, Chen Z, Gui W, Yang C. Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features. Sensors. 2022; 22(14):5412. https://doi.org/10.3390/s22145412
Chicago/Turabian StyleChen, Yanli, Zhipeng Chen, Weihua Gui, and Chunhua Yang. 2022. "Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features" Sensors 22, no. 14: 5412. https://doi.org/10.3390/s22145412