Development of classification model based on neural networks for the process of iron ore beneficiation

• Main Authors: Anton Senko, Andrey Kupin, Bohdan Mysko
• Format: Article
• Language: English
• Published: PC Technology Center 2019-06-01
• Series: Tehnologìčnij Audit ta Rezervi Virobnictva
• Subjects: classification model; computer support system for solutions; neural network; ore beneficiation; clustering of statistical data
• Online Access: http://journals.uran.ua/tarp/article/view/176121

The object of research is the processes of beneficiation of iron ore in the conditions of a mining and processing plant. Iron ore beneficiation factory near parallel to existing production lines or concentration sections. One of the key characteristics that determine the operating mode of the grinding apparatus is the crushing of ore, directly related to its strength. But unlike other parameters, the problem is with constant monitoring of the strength value. The determination of this parameter requires a laboratory study of the technological ore sample from the conveyor of the beneficiation section. The specifics of the working conditions of the beneficiation section complicate the monitoring of the strength parameter by installing a hardware sensor directly on the conveyor. Therefore, it is proposed to determine it by forecasting. Based on Big Data information technologies, using the accumulated statistical data, it is possible to forecast data between the technological samples. The technological process of ore beneficiation in the conditions of a mining and processing plant is systematically analyzed. The generalized structure of the classification model is presented, which, based on the accumulated statistical data of the beneficiation section based on the current parameters of the section, is able to determine the parameters of incoming raw materials. The unknown parameter is determined using the counterpropagation neural network, which combines the following algorithms: a self-organizing Kohonen map and a Grossberg star. Their combination leads to an increase in the generalizing properties of the network. The training sample is formed as a result of clustering the statistical data of the beneficiation section and selecting the cluster to which the current status of the section works. The presented forecasting algorithm, based on a combination of clustering methods and the use of a predictive neural network, allows the specialist to more quickly receive recommendations for making decisions regarding the behavior of the object compared to obtaining laboratory test data.