Neural network modeling of agglomeration firing process for polymetallic ores

While processing polymetallic ores at the non-ferrous metallurgy problems arises connecting with the excellence of production and the efficient applying the technological devices-firing furnace and crusher machine. In earlier time, similar questions were solved due to the big practice experiences and using a mathematical modeling method. The mathematical model for optimizing those operating mode is a very complex and hard to calculation. Performing computations is needed too much time and many resources. Because the control of the agglomeration furnaces and other machines are including complex multiparameter processes. The method of the math modeling for optimization the operating mode to the firing furnace is replaced with modeling based on the neural network that is here a new method. The results obtained have shown that proposed methods based on the neural network modeling of metallurgical processes allow determining more accurate and adequate results of calculations than mathematical modeling by the traditional program. The use of new approaches for modeling the technological processes at the non-ferrous metallurgy gives opportunity to enhance an effectiveness of production excellence and to enhance an efficient applying the heat-energy equipment while a firing the sulfide polymetallic ores of non-ferrous metallurg