AI-Driven Optimization of a 2.45 Ghz Microwave Drying System for Raw Corn: Enhancing Efficiency and Uniformity via Hybrid CNN-RNN Control

Yıl 2026, Cilt: 9 Sayı: 1, 114 - 123, 15.01.2026

Caner Murat

https://doi.org/10.34248/bsengineering.1766754

Cited By: 1

https://izlik.org/JA63FG62BA

Öz

This study integrates Machine Learning (ML) and Deep Learning (DL) approaches into an integrated methodology to model and optimize the microwave drying of raw corn, which is a multi-variable and non-linear process. The electromagnetic behaviour of the process was first simulated using CST Studio Suite software; it was found that a multi-microwave source provides more homogeneous and effective heating compared to a single source. In the experimental phase, classical ML models such as Logistic Regression and SVR, and DL models such as ANN, 1D CNN, and LSTM/GRU were trained using data collected under various input powers (200-500 W) and geometric configurations. The results demonstrated that the CNN-RNN model achieved the highest predictive accuracy for moisture content dynamics. Through systematic AI-driven analysis of experimental data, the optimal drying configuration was identified as 500 W microwave power, 8.1 cm waveguide distance, and 26 cm vertical placement. Under these conditions, 100 grams of raw corn was dehydrated to 40 grams in 5 minutes with minimal quality degradation. The ANN model demonstrated impressive performance metrics in this process, including 0.978 R², 0.041 RMSE, and 0.033 MAE. These results demonstrate the potential of physical simulation and artificial intelligence integration to create a powerful decision support system for improving the efficiency and control of complex industrial processes such as microwave drying.

Anahtar Kelimeler

Artificial intelligence , Microwave drying , 2.45 GHZ , Waveguide , Food drying

Etik Beyan

Ethics committee approval was not required for this study because of there was no study on animals or humans.

Destekleyen Kurum

TUBITAK

Proje Numarası

124E825

Teşekkür

This work has been supported by project 124E825 of TUBITAK (Scientific and Technological Research Council of Türkiye).

Kaynakça

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