This paper deals with a proposal of predictive control system for the magnesite thermal treatment in rotary furnace. The mathematical model based on the initial principles and elementary balances method providing a comprehensive view of the rotary furnace work was calibrated based on the measured operating data. This model was used as the data model for the development of the approximation model in the form of an artificial neural network after identifying the critical points of the production process of sintered magnesia production. The paper represents the process of the approximation model development and the principle of the seeking of the optimal values of the specified control variables in order to ensure the required quality throughout the whole period of the rotary furnace operation.

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Abstract. Mathematical modeling of heat aggregates is one of the fundamental methods of the mathematical modelling research. A mathematical model based on the method of elementary balances was created for the thermal treatment of granular and lumpy materials. The adaptation of the selected aggregate model is based on prior knowledge and experiments. The paper presents an adaptation of the mathematical model for the magnesite processing rotary furnace using the mode of caustic and clinker production. A simulation of the charge preheater impact based on the thin layer principle is implemented into the model. The main advantages of using this type of preheater of rotary furnace are smaller dimensions for a large exchange surface and low pressure losses.