An improved RNN modeling algorithm for flotation process of copper-silver ore

Wang, Kang; Pang, Liwei; Li, Xiaoli; Xin, Zhao

doi:10.1109/ccdc55256.2022.10034059

Cited by 4 publications

(2 citation statements)

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“…A set of binary numbers is obtained by threshold operation between the gray values of the P pixels around the central pixel (x c , y c ) in the field of R radius and the gray values of the central pixel, and then the LBP characteristic values of the central point are obtained. Equation (1) describes the calculation of the LBP characteristic value for the central pixel [27].…”

Section: Local Binary Patternsmentioning

confidence: 99%

“…Therefore, the rapid and accurate identification of process conditions plays a crucial role in optimizing and controlling flotation operations. Nevertheless, the flotation production process is intricate, subject to numerous physical and chemical factors, constituting a complex industrial engineering system with characteristics such as nonlinearity, hysteresis, and strong coupling [ 1 ]. Consequently, systematically describing the froth characteristics within the flotation cell and achieving precise condition identification pose significant challenges.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition

Fan,

Feng,

Wang

et al. 2024

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Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations.

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Section: Local Binary Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%