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This paper presents an application of computational intelligence in modeling   and optimization of parameters of two related production processes - ore   flotation and production of balls for ore flotation. It is proposed that   desired chemical composition of flotation balls (Mn=0.69%; Cr=2.247%;   C=3.79%; Si=0.5%), which ensures minimum wear rate (0.47 g/kg) during copper   milling is determined by combining artificial neural network (ANN) and   genetic algorithm (GA). Based on the results provided by neuro-genetic   combination, a second neural network was derived as an ‘intelligent soft   sensor’ in the process of white cast iron production. The proposed ANN   12-16-12-4 model demonstrated favourable prediction capacity, and can be   recommended as a ‘intelligent soft sensor’ in the alloying process intended   for obtaining favourable chemical composition of white cast iron for   production of flotation balls. In the development of intelligent soft sensor   data from the two real production processes was used. [Projekat Ministarstva   nauke Republike Srbije, br. TR35037 i br. TR35015

Optimization of chemical composition in the manufacturing process of flotation balls based on intelligent soft sensing