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- W4286306511 abstract "Mud height and feed concentration are important variables of thickener in beneficiation process. However, real-time online detection of these parameters are difficult in actual process. Therefore, some soft sensing models are proposed to solve these production problems in this paper. Firstly, the internal image of thickener is collected, and CNN(Convolutional Neural Networks) is used to solve the problem of on-line detection of mud layer. Then, using production data, LSTM(Long Short-Term Memory) method is used to solve the problem of on-line detection of feed concentration. The simulation results and actual data verify that the machine learning method can effectively solve the problem of on-line detection of key machine parameters of thickener." @default.
- W4286306511 created "2022-07-21" @default.
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- W4286306511 date "2022-05-04" @default.
- W4286306511 modified "2023-09-27" @default.
- W4286306511 title "Machine learning-based soft measurement method in the dense process of hydrometallurgy" @default.
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- W4286306511 doi "https://doi.org/10.23919/ascc56756.2022.9828220" @default.
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