Abstract
This study is aimed at getting simplified model of mill filling technological process of fine crushing in a closed-circuit grinding with screen separation. Optimal and simple model structure are supposed to be used in adaptive predictive control loop. The minor factors that directly affect the mill load indicator are not taken into account, since some of them cannot be directly measured, and other ones affect the process only in the long term. In this paper the athors considered mill filling process identification in the center-discharge ball mill by the method of neural networks (NN). The method includes the identification of the nonlinear process using nonlinear autoregressive with external input (NARX) neural network. The most accurate model was found by varying the structural parameters of the network. The best models were tested in the course of the actual grinding process. The best estimation of the NN model to the real object is obtained with 72.1% match.
Highlights
Optimal control remains a complex problem in the mining industry for many years due to various uncertainties in the models of control objects, nonlinearities, changes in parameters and their interdependences [1]
We focused on finding the best fitting neural network and in the end compared the best fitting neural network with others Matlab System Identification Toolbox techniques for nonlinear identification: tree partition method, wavelet method, Hammerstein-Wiener model
Experiments were carried out to search for the optimal model of a ball mill as a control object over the channel “flowrate of ore - noise” using nonlinear autoregressive with external input (NARX) neural networks
Summary
Optimal control remains a complex problem in the mining industry for many years due to various uncertainties in the models of control objects, nonlinearities, changes in parameters and their interdependences [1]. Published under licence by IOP Publishing Ltd points of the grinding cycle in order not to clog the chutes and changing the density of the output pulp product to the flotation process. The topsize product flow is a dependent parameter on the main input material flows for a stationary process, because the constancy of the technical characteristics of vibrating screens. Other inputs are short-duration perturbations such as ore moisture, ambient vibration and long-term perturbations such as volume of balls in the mill and working volume of the mill [2, 3].
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