Abstract
In the context of the aggravating environmental problems of the planet and the continuing to worsen global energy crisis, the role of optimizing systems increases, including by reducing energy consumption of production and, as a result, reducing the carbon load. This problem is also relevant for one of the flagships of the economy of Kazakhstan – the mining and processing sector. The purpose of the stated study is to study the possibility of optimizing one of the most energy-intensive stages of the beneficiation processing of ferrous ores – concentrate drying. In connection with economic aspects, the study was carried out on digital simulation models of the drying process of iron ore concentrate in BSA 3.5-27 drying drums developed by the authors in the Matlab visual modeling environment. The authors, based on the results of the conducted theoretical research and field experiments, constructed a model of the object under study with existing automation systems, proposed an adaptive optimizing process control system based on a neural network of radial basis functions. The value of gas consumption for obtaining a drying agent was chosen as a control criterion for the technological process. Based on the results of comparing the operation of digital models of the original concentrate drying system under study and the system with adaptive control, the quality of control was assessed. The obtained results can be applied to modernize the control systems for drying processes of beneficiation complexes of both iron ore mining and processing plants and other minerals using similar technologies.