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...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Hindawi Publishing Corporation
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/47835 |
| _version_ | 1848757943860723712 |
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| author | Wang, Y. Chen, X. Zhou, X. Gui, W. Caccetta, Louis Xu, Honglei |
| author_facet | Wang, Y. Chen, X. Zhou, X. Gui, W. Caccetta, Louis Xu, Honglei |
| author_sort | Wang, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T09:36:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-47835 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:36:08Z |
| publishDate | 2014 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-478352017-09-13T14:16:25Z Hybrid intelligence model based on image features for the prediction of flotation concentrate grade Wang, Y. Chen, X. Zhou, X. Gui, W. Caccetta, Louis Xu, Honglei 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/47835 10.1155/2014/401380 Hindawi Publishing Corporation fulltext |
| spellingShingle | Wang, Y. Chen, X. Zhou, X. Gui, W. Caccetta, Louis Xu, Honglei Hybrid intelligence model based on image features for the prediction of flotation concentrate grade |
| 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://hdl.handle.net/20.500.11937/47835 |