Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade
In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide ac...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/401380 |
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| _version_ | 1850169825111834624 |
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| author | YaLin Wang XiaoFang Chen XiaoLing Zhou WeiHua Gui Louis Caccetta Honglei Xu |
| author_facet | YaLin Wang XiaoFang Chen XiaoLing Zhou WeiHua Gui Louis Caccetta Honglei Xu |
| author_sort | YaLin Wang |
| collection | DOAJ |
| description | In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications. |
| format | Article |
| id | doaj-art-a7393d1e25234fc4a67dab985f63cd89 |
| institution | OA Journals |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-a7393d1e25234fc4a67dab985f63cd892025-08-20T02:20:38ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/401380401380Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate GradeYaLin Wang0XiaoFang Chen1XiaoLing Zhou2WeiHua Gui3Louis Caccetta4Honglei Xu5School of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaDepartment of Mathematics and Statistics, Curtin University, Perth, WA 6845, AustraliaDepartment of Mathematics and Statistics, Curtin University, Perth, WA 6845, AustraliaIn flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications.http://dx.doi.org/10.1155/2014/401380 |
| spellingShingle | YaLin Wang XiaoFang Chen XiaoLing Zhou WeiHua Gui Louis Caccetta Honglei Xu Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade Abstract and Applied Analysis |
| title | Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade |
| title_full | Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade |
| title_fullStr | Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade |
| title_full_unstemmed | Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade |
| title_short | Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade |
| title_sort | hybrid intelligence model based on image features for the prediction of flotation concentrate grade |
| url | http://dx.doi.org/10.1155/2014/401380 |
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