Automating the Evaluation of the Efficiency of Averaging a Commercial Ore Concentrate

Investigations have been carried out based on the example of the data of the Stoilensky GOK enterprise using the fine wet-screening operation. An integrated approach is proposed that involves system-analysis principles and proven mathematical and computer modeling methods for building a dynamic and continuous mixing model, which reflects a change in the iron level and takes the time intervals during which the incoming batch completely displaces the bin volume into account. The proposed forecast model is based on a cell model consisting of individual cells, which simulate ideal mixing. In the proposed mathematical model, the procedure for calculating the weighted moving average is applied for smoothing and estimating the forecast accuracy. Using the developed software, the mixing-bin volume has been predicted that is required to achieve the desired standard deviation and recommendations have been made for choosing the mixing system parameters required to obtain the result that is optimal in terms of economic feasibility. The proposed model makes it possible to estimate a decrease in the standard deviation of the index of quality of the commercial concentrate after its averaging in the mixing bin.