DTCWT-based zinc fast roughing working condition identification

doi:10.1016/j.cjche.2018.06.028

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (8): 1721-1726.DOI: 10.1016/j.cjche.2018.06.028

• Selected Papers from the 28th Chinese Process Control Conference • Previous Articles Next Articles

DTCWT-based zinc fast roughing working condition identification

Zhuo He¹, Zhaohui Tang¹, Zhihao Yan¹, Jinping Liu²

1 College of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China, College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China

Received:2017-11-01 Revised:2018-04-20 Online:2018-09-21 Published:2018-08-28
Contact: Zhaohui Tang,E-mail address:zhtang@csu.edu.cn

DTCWT-based zinc fast roughing working condition identification

Zhuo He¹, Zhaohui Tang¹, Zhihao Yan¹, Jinping Liu²

1 College of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China, College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China

通讯作者: Zhaohui Tang,E-mail address:zhtang@csu.edu.cn

Abstract

Abstract: The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process. The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform (DTCWT) is proposed for process monitoring of zinc fast roughing. Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification (iRFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

Key words: DTCWT, Working condition, Integrated classification model, Zinc fast roughing

摘要： The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process. The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform (DTCWT) is proposed for process monitoring of zinc fast roughing. Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification (iRFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

关键词: DTCWT, Working condition, Integrated classification model, Zinc fast roughing

Zhuo He, Zhaohui Tang, Zhihao Yan, Jinping Liu. DTCWT-based zinc fast roughing working condition identification[J]. Chin.J.Chem.Eng., 2018, 26(8): 1721-1726.

Zhuo He, Zhaohui Tang, Zhihao Yan, Jinping Liu. DTCWT-based zinc fast roughing working condition identification[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1721-1726.

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DTCWT-based zinc fast roughing working condition identification

DTCWT-based zinc fast roughing working condition identification

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