Formation of a complex method for analyzing multidimensional production data of a processing plant

Abstract

We discuss the development of a comprehensive data analysis method which allows increasing the accuracy of predicting the performance of roller mill of processing plant (RP) mining and processing plant (GOK) when you change the properties of incoming raw materials for processing. The described method includes primary data processing, determination by statistical analysis methods and data mining algorithms of the most significant factors affecting the resulting parameter, development of mathematical models based on correlation regression and factor analysis, analysis and confirmation of the quality of forecasting by a neural network. The systematization and coordination of production data was carried out, the generated database was processed using statistical and intellectual analysis methods, the physical and chemical parameters of the input raw materials and processed ore that have the greatest impact on the mill productivity were determined, mathematical models were formed to determine the expected productivity, their quality and applicability limits were evaluated, the error in predicting the mill productivity for various mineralogical compositions of the processed ore was determined. The significance of the parameters used in the models is checked by the algorithms of intelligent analysis. The verification of the used models for predicting mill performance by an artificial neural network is carried out by comparing the series of predicted values of the resulting factor obtained by different models