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dc.contributor.authorWang, Y.
dc.contributor.authorChen, X.
dc.contributor.authorZhou, X.
dc.contributor.authorGui, W.
dc.contributor.authorCaccetta, Louis
dc.contributor.authorXu, Honglei
dc.identifier.citationWang, Y. and Chen, X. and Zhou, X. and Gui, W. and Caccetta, L. and Xu, H. 2014. Hybrid intelligence model based on image features for the prediction of flotation concentrate grade. Abstract and Applied Analysis. 2014: Article ID 401380.

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.

dc.publisherHindawi Publishing Corporation
dc.titleHybrid intelligence model based on image features for the prediction of flotation concentrate grade
dc.typeJournal Article
dcterms.source.titleAbstract and Applied Analysis

This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons License Please refer to the licence to obtain terms for any further reuse or distribution of this work

curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusOpen access

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