A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction
Abstract
:1. Introduction
2. Pellet Production Process and Soft Sensor Model
2.1. Pellet Production Process
2.2. Soft Sensor Model Structure
3. Data Dimension Reduction Processing Based on Isomap
4. SVR Soft Sensor Modeling Optimized by Improved Bat Algorithm
4.1. SVR Algorithm
4.2. Bat Algorithm and Its Improvement
4.2.1. Bat Algorithm
4.2.2. Lévy Flight Strategy
4.2.3. Cauchy Mutation Strategy
4.3. Soft Sensor Model Optimization Based on IBA Algorithm
5. Simulation Analysis
- (1)
- For Isomap data segmentation method, the value of R2, Q2CV5 and Q2ext of the IBA-SVR algorithm proposed in this paper are better than those of the comparison algorithm. Therefore, the optimization performance of IBA algorithm for SVR model is better than that of other comparison algorithms.
- (2)
- By comparing the dimensionality reduction methods of Isomap and PCA data under IBA-SVR model, we can see that the model performance of dimensionality reduction using Isomap algorithm is better.
- (3)
- The joint to optimization of Isomap and SVR model using IBA algorithm effectively improves the prediction accuracy of the model. 98.6% of the prediction results meet the accuracy requirements of level 1.5 instruments, and 97.7% of the prediction results meet the accuracy requirements of level 2.5 instruments.
- (1)
- Considering the fitting ability, different data segmentation methods have little influence on the fitting ability of the model.
- (2)
- Considering the robustness of the model, the model established by the method of randomly segmented data fluctuates more than that established by the method of SOM neural network.
- (3)
- Considering the external prediction ability of the model, the external prediction ability of the model established by the method of SOM neural network segmentation data is more stable.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name | Function | D | Range | fopt |
---|---|---|---|---|
Shpere | 30 | [−10,10]D | 0 | |
Schwefel 2.22 | 30 | [−10,10]D | 0 | |
Ackley | 30 | [−32,32]D | 0 | |
Griewank | 30 | [−600,600]D | 0 | |
Shifted Sum Square | 30 | [−100,100]D | 0 | |
Rotated Griewank | 30 | [−600,600]D | 0 |
Algorithm | Reference | Parameters |
---|---|---|
BA | Ref. [23] | , , , |
PSO | Ref. [27] | , , , |
EBA | Ref. [28] | , |
dBA | Ref. [29] | , , , |
IBA | Present | , , , |
Dimensionality Reduction Method | Isomap | PCA | |||
---|---|---|---|---|---|
Modeling Method | GA-SVR | PSO-SVR | BA-SVR | IBA-SVR | IBA-SVR |
95.3% | 95.7% | 96.3% | 98.6% | 96.9% | |
98.3% | 98.8% | 98.8% | 99.7% | 99.1% | |
Min_R2 | 0.794 | 0.822 | 0.816 | 0.841 | 0.842 |
Max_R2 | 0.911 | 0.921 | 0.891 | 0.922 | 0.921 |
Mean_R2 | 0.855 | 0.853 | 0.852 | 0.862 | 0.859 |
Min_Q2CV5 | 0.741 | 0.757 | 0.755 | 0.813 | 0.786 |
Max_Q2CV5 | 0.866 | 0.839 | 0.821 | 0.871 | 0.857 |
Mean_Q2CV5 | 0.821 | 0.823 | 0.829 | 0.851 | 0.836 |
Min_Q2ext | 0.731 | 0.817 | 0.822 | 0.831 | 0.811 |
Max_Q2ext | 0.832 | 0.855 | 0.869 | 0.874 | 0.862 |
Mean_Q2ext | 0.811 | 0.831 | 0.847 | 0.851 | 0.836 |
Method of Data Segmentation | Random | SOM | |
---|---|---|---|
Training Data | Min_R2 | 0.835 | 0.841 |
Max_R2 | 0.912 | 0.922 | |
Mean_R2 | 0.859 | 0.862 | |
Min_Q2CV5 | 0.753 | 0.813 | |
Max_Q2CV5 | 0.891 | 0.871 | |
Mean_Q2CV5 | 0.821 | 0.851 | |
Test Data | Min_Q2ext | 0.741 | 0.831 |
Max_Q2ext | 0.896 | 0.874 | |
Mean_Q2ext | 0.816 | 0.851 |
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Liu, J.; Wang, Y.; Zhang, Y. A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction. Symmetry 2020, 12, 167. https://doi.org/10.3390/sym12010167
Liu J, Wang Y, Zhang Y. A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction. Symmetry. 2020; 12(1):167. https://doi.org/10.3390/sym12010167
Chicago/Turabian StyleLiu, Jialun, Yukun Wang, and Yong Zhang. 2020. "A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction" Symmetry 12, no. 1: 167. https://doi.org/10.3390/sym12010167
APA StyleLiu, J., Wang, Y., & Zhang, Y. (2020). A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction. Symmetry, 12(1), 167. https://doi.org/10.3390/sym12010167