Neural net-based softsensor for dynamic particle size estimation in grinding circuits

Abstract A softsensor was developed to estimate the hydrocyclone overflow dynamic particle size distribution of a grinding circuit using neural network models. Because of the inherent dynamics associated with the grinding process, the time histories of the four variables measured around the hydrocyclone along with the past value of the cyclone overflow particle size or the past percent passing 53 μm were utilized as the inputs to formulate the neural model for the estimation of the present value of the particle size. The fact that the neural net-based softsensor performs well in the particle size estimation suggests that the model developed has captured the essence of the process dynamics. To cope with the time-varying nature of the grinding circuit, on-line adaptation of the neural network was considered. A simplified neural net-based softsensor was thus developed by making use of the principal component analysis for the data reduction so as to simplify the neural model structure and to render it more suitable for on-line adaptation.

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