Simulation of Predictive Control Algorithm for Rotary Furnace Producing Magnesite Sinter

Abstract

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.

The Influences of Addition of MgCO₃ on the Properties of the Magnesia Prepared Using Magnesite

“…Sintered magnesia has been broadly applied as refractory due to its advantages such as high melting point and excellent slag resistance performances [1,2,3,4,5]. The service life of magnesia refractory in steel smelting process mainly depends on properties of sintered magnesia including the bulk density, grain size and grain boundary bonding distribution [6,7,8,9,10,11].…”