MOISTURE ESTIMATION IN SUGAR DRYING BASED ON A HYBRID MATHEMATICAL MODEL
Abstract
The paper addresses the problem of moisture estimation in the sugar drying process, which is a critical stage of sugar production affecting product quality, storage stability, and energy efficiency. Direct continuous measurement of moisture in industrial conditions is difficult due to technological limitations and time delays associated with laboratory analysis. Therefore, the development of reliable indirect estimation methods is an important task for improving process control. A hybrid mathematical model for real-time moisture estimation is proposed. The model combines a physical description of heat and mass transfer dynamics with a neural network-based soft sensor used as a nonlinear correction element. The linear discrete model describes the main inertial behavior of the drying process, while the nonlinear component compensates for model uncertainties, parameter variations, and external disturbances. The structure of the model and the data processing algorithm are presented. Simulation studies were performed taking into account measurement delays typical for industrial conditions. The influence of the discretization step on estimation accuracy and control performance was also analyzed. The results demonstrate that the hybrid model reduces the moisture estimation error by approximately 25–30 % compared to the linear model and provides stable performance under varying operating conditions. The proposed approach enables reliable estimation of an unmeasured state variable and can be integrated into industrial control systems. The developed model is suitable for real-time applications and can be used for monitoring, stabilization, and optimization of the sugar drying process.
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