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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 401380, 17 pages
Research Article

Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2Department of Mathematics and Statistics, Curtin University, Perth, WA 6845, Australia

Received 7 March 2014; Accepted 14 May 2014; Published 30 June 2014

Academic Editor: Xinguang Zhang

Copyright © 2014 YaLin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.