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To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the issues mentioned above. This method combines multi-dimensional CNN (convolutional neural networks) characteristics and improved LBP (local binary patterns) characteristics. We have divided the foam flotation conditions into six categories. First, the multi-directional and multi-scale selectivity and anisotropy of nonsubsampled shearlet transform (NSST) are used to decompose the flotation foam images at multiple frequency scales, and a multi-channel CNN network is designed to extract static features from the images at different frequencies. Then, the flotation video image sequences are rotated and dynamic features are extracted by the LBP-TOP (local binary patterns from three orthogonal planes), and the CNN-extracted static picture features are fused with the LBP dynamic video features. Finally, classification decisions are made by a PSO-RVFLNs (particle swarm optimization-random vector functional link networks) algorithm to accurately identify the foam flotation performance states. Experimental results show that the detection accuracy of the new method is significantly improved by 4.97% and 6.55%, respectively, compared to the single CNN algorithm and the traditional LBP algorithm, respectively. The accuracy of flotation performance state classification was as high as 95.17%, and the method reduced manual intervention, thus improving production efficiency. [ABSTRACT FROM AUTHOR]

Arsenopyrite is a common arsenic-containing mineral that is often closely associated with sulfide minerals, such as pyrite, chalcopyrite, pyrrhotite, galena, and sphalerite, and with precious metals, such as gold and silver. The selective inhibition of arsenopyrite is an important method used to reduce the arsenic content of processed products, the cost of arsenic removal in metallurgical processes, and its impact on the environment. In this study, we discovered a chemical sodium, m-nitrobenzoate (m-NBO), that can effectively inhibit the flotation behaviors of arsenopyrite via sodium butyl xanthate (NaBX), and these effects were studied by flotation experiments. The results showed that, using NaBX as a collector, arsenopyrite had good floatability under acidic conditions, but the floatability decreased under alkaline conditions. Furthermore, the organic inhibitor m-NBO had a significant inhibitory effect on arsenopyrite under alkaline conditions. In addition, the adsorption between m-NBO and NaBX was competitive, and a hydrophilic layer formed on the surface of arsenopyrite. The passivation film prevents dixanthogen from being adsorbed on the surface of the mineral. Due to the effect of m-NBO on arsenopyrite, the redox potential and oxide content of the arsenopyrite surface increased, the hydrophobicity of the arsenopyrite surface was reduced, and the flotation of arsenopyrite was inhibited. These results provide options for separating multimetal sulfide minerals and arsenic-containing minerals. [ABSTRACT FROM AUTHOR]