Predictive Modeling of Parameter Variations in Conveyor Belt Dryers using Machine Learning for Improved Drying Performance
DOI:
https://doi.org/10.21015/vtm.v13i2.2105Abstract
This study takes an in-depth look at the mathematical model governing the operation of a tangential flow conveyor dryer operating in a co-current configuration. The Conveyor Belt Dryer (CBD) is represented as an Ordinary Differential Equation (ODE), and our research focuses on studying the influence of parameter variation and symmetry on the Rate of Exchange of Moisture Content (RMC). To achieve this, we employ a system studying framework based on artificial Neural Networks (NN) and the Levenberg-Marquardt Training (LMT) algorithm, enabling the examination of symmetry within surrogate solutions. The RK-four method is applied to generate target data points for supervised learning in the NN-LMT structure. Furthermore, we investigate various scenarios of the mathematical model related to the rate of change of moisture content. Detailed graphical representations, including histograms, absolute error plots, curve fitting graphs, and regression graphs, are employed to facilitate comprehensive explanations. Additionally, a comparative analysis between the numerical solutions obtained through the machine learning technique is provided, followed by graphical and statistical representations of the determined errors.
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