A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction
Soft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/1/167 |
| _version_ | 1818008333916831744 |
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| author | Jialun Liu Yukun Wang Yong Zhang |
| author_facet | Jialun Liu Yukun Wang Yong Zhang |
| author_sort | Jialun Liu |
| collection | DOAJ |
| description | Soft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we propose a new soft sensor model, Isomap-SVR. First, the sample data set is divided into training set and testing set by using self-organizing map (SOM) neural network to ensure the fairness and symmetry of data segmentation. Isometric feature mapping (Isomap) method is used for dimensionality reduction of the model input data, which could not only reduce the structure complexity of the proposed model but speed up learning speed, and then the Support Vector Machine Regression (SVR) is applied to the regression model. A novel bat algorithm based on Cauchy mutation and Lévy flight strategy is used to optimize parameters of Isomap and SVR to improve the accuracy of the proposed model. Finally, the model is applied to the prediction of the temperature of rotary kiln calcination zone, which is difficult to measure directly. The simulation results show that the proposed soft sensor modeling method has higher learning speed and better generalization ability. Compared with other algorithms, this algorithm has obvious advantages and is an effective modeling method. |
| first_indexed | 2024-04-14T05:27:50Z |
| format | Article |
| id | doaj.art-0855ac42f4e44b0c92b8f600b54baadf |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-14T05:27:50Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-0855ac42f4e44b0c92b8f600b54baadf2022-12-22T02:09:54ZengMDPI AGSymmetry2073-89942020-01-0112116710.3390/sym12010167sym12010167A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature PredictionJialun Liu0Yukun Wang1Yong Zhang2School of Electronic and Information Engineering, University of Science and Technology Liaoning, No. 185, Qianshan, Anshan 114051, Liaoning, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, No. 185, Qianshan, Anshan 114051, Liaoning, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, No. 185, Qianshan, Anshan 114051, Liaoning, ChinaSoft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we propose a new soft sensor model, Isomap-SVR. First, the sample data set is divided into training set and testing set by using self-organizing map (SOM) neural network to ensure the fairness and symmetry of data segmentation. Isometric feature mapping (Isomap) method is used for dimensionality reduction of the model input data, which could not only reduce the structure complexity of the proposed model but speed up learning speed, and then the Support Vector Machine Regression (SVR) is applied to the regression model. A novel bat algorithm based on Cauchy mutation and Lévy flight strategy is used to optimize parameters of Isomap and SVR to improve the accuracy of the proposed model. Finally, the model is applied to the prediction of the temperature of rotary kiln calcination zone, which is difficult to measure directly. The simulation results show that the proposed soft sensor modeling method has higher learning speed and better generalization ability. Compared with other algorithms, this algorithm has obvious advantages and is an effective modeling method.https://www.mdpi.com/2073-8994/12/1/167self-organizing map neural networkisometric feature mappingsupport vector regressionnovel bat algorithmrotary kiln |
| spellingShingle | Jialun Liu Yukun Wang Yong Zhang A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction Symmetry self-organizing map neural network isometric feature mapping support vector regression novel bat algorithm rotary kiln |
| title | A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction |
| title_full | A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction |
| title_fullStr | A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction |
| title_full_unstemmed | A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction |
| title_short | A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction |
| title_sort | novel isomap svr soft sensor model and its application in rotary kiln calcination zone temperature prediction |
| topic | self-organizing map neural network isometric feature mapping support vector regression novel bat algorithm rotary kiln |
| url | https://www.mdpi.com/2073-8994/12/1/167 |
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