Recognition of flotation working conditions through froth image statistical modeling for performance monitoring. (February 2016)

• Record Type: Journal Article
• Authors: Zhang, Jin; Tang, Zhaohui; Liu, Jinping; Tan, Zhen; Xu, Pengfei
• Is Part Of: Minerals engineering. Volume 86(2016)
• Page Start: 116
• Page End: 129
• Publication Date: 2016-02
• Subjects: Froth flotation process -- Working condition recognition -- Gabor wavelet transformation -- t location-scale distribution -- Gamma distribution; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2015.12.008
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: Statistical modeling of froth image-based froth class identification is proposed. Real and imaginary Gabor filter responses are modeled by t location-scale model. Magnitude response of Gabor filters are modeled by Gamma distribution model. Iterative procedures for statistical model parameter estimation are presented. Output model-parameters are used for froth-working-condition recognition. Abstract: Accurate identification of the working conditions of froth flotation remains challenging because of the inherent chaotic nature of the underlying microscopic phenomenon. The froth surface is generally used as an effective indicator of the working condition and performance of flotation. In this study, we developed a novel method for determining the complex working conditions of flotation through statistical modeling of froth images. Gabor wavelet transformation was used for modeling because of the optimal localization properties in both spatial and frequency domain of the Gabor functions. The characteristic parameters of the probability density functions of the Gabor filter responses of the froth image, rather than conventional statistics (mean and variance), were then modeled using the empirical probability distribution models, t location-scale and gamma distributions. A simple learning vector quantization-neural network (LVQ-NN) was adopted to obtain an effective classifier for identifying the working conditions of froth phases under different production phenomena. The proposed model was validated through experiments on a bauxite flotation plant located in China and compared with commonly used determination methods.