USING OF NEURAL CONTROL SYSTEMS IN CONCENTRATION PROCESS AUTOMATION

Abstract

. Ball drum mill are widely used on concentrating complexes of iron ore on mining and processing plant. Largely thanks to the beneficial properties of neural networks that are common to different types of nonlinear dynamic objects and solved the problem of identification, analysis, synthesis and hardware implementation of complex process control systems in non-stationary terms and with incomplete and fuzzy information. The implementation of the majority part of considered existing industrial automated control systems are based on proportional, integrating and differential regulators or their combinations (P, PI, PID controllers).However, systems with PID controllers can’t always provide the necessary quality of controling, especially in terms of difficult technological processes with nonlinearity, nonstationarity, delay in time, random perturbations presence of fuzzy and incomplete information. For such technological processes can be attributed most part of mineral processing stages. Milling of iron ore is the preparatory process for the magnetic concentration of materials. The basic technological process of magnetic-concentration complexes is magnetic separation. For the main ways of preparing the ore on magnetic-concentration complexes can be attributed milling and classification of iron ore to controling particle size of ore, that incoming to the magnetic concentration. Considering the large number of components in the composition of the ore and it’s variable characteristics, it is necessary to control of milling processes to achieve optimal size of milling ore and the optimum fractional composition of raw materials that incoming to the magnetic separation. The purpose : Is to prove the possibility of using neurocybernetics approaches for controlling of technological process concentration technology in terms of mining and processing complexes. Methods : identifying patterns of increasing efficiency of technological process controlling through synthesis and implementation of optimal controlling in the operation of control systems based on identification and forecasting condition of controlled processes with controlling of major disturbances. Scientific and the traditional automated control systems based on regulators. Practical relevance: the main potential of fuzzy logic is in implementing the functions of supervisory control. Using fuzzy logic makes possible to fully automate the technological process and review the formed rules and their interpretation for further analysis. Results: review the researchers works allowed to organize and present the main technological units as a control objects. This made possible to define the direction of creating future neural control systems in concentration process automation.