Open Access

ARTICLE

Machine Learning Based Random Forest Prediction for Solar Dryer under Thailand Climatic Conditions

Jakkrawut Techo¹, Panupon Trairat¹, Karthikeyan Velmurugan^2,*
1 Division of Industrial Technology, Faculty of Agricultural Technology and Industrial Technology, Nakhon Sawan Rajabhat University, Nakhon Sawan, Thailand
2 Department of Technology Engineering, Faculty of Industrial Technology, Kamphaeng Phet Rajabhat University, Kamphaeng Phet, Thailand
* Corresponding Author: Karthikeyan Velmurugan. Email:
(This article belongs to the Special Issue: Alternative Energy Sources for a Carbon-Free Society: Limitations and Futuristic Trends)

Energy Engineering https://doi.org/10.32604/ee.2026.080474

Received 10 February 2026; Accepted 08 April 2026; Published online 14 May 2026

Abstract

In this study, selective and non-selective absorber-coated trays were employed to dry carrots and pears. Two trays with a selective absorber coating (1 mm thickness) were used, each loaded with 600 g of sliced carrots and pears. Similarly, two additional trays with a non-selective absorber coating were utilised. Furthermore, the performance of both selective and non-selective absorber-coated trays was compared with conventional open sun drying. The selective absorber-coated tray demonstrated higher thermal energy absorption and enabled the drying of carrots within 2 days, resulting in a weight loss of 529 g. In contrast, owing to the higher fructose content in pears, approximately 7 days were required to achieve a weight loss of 480 g. Overall, more effective drying was observed in the selective absorber-coated tray compared with the non-selective absorber-coated tray and open sun drying. A two-way ANOVA was performed to evaluate the effectiveness of the drying process across the examined methods. The higher F-statistic value of 63.566 indicates significant biological differences between carrots and pears, validating the comparison of their drying behavior. In addition, the F-statistic value of 5.099 for the drying method suggests a notable variation between selective and non-selective absorber-coated trays. It is concluded that the selective absorber-coated tray facilitates enhanced heat transfer to both carrots and pears. Following the ANOVA analysis, a Random Forest (RF) regression model was applied to the selective absorber-coated tray. Notably, the solar dome inner cover temperature exhibited a feature importance of 0.958, indicating its dominant role in increasing the tray temperature and improving drying performance. The average difference between actual and predicted tray temperatures was 0.16°C, demonstrating that the RF model is stable and reliable, with an R² value of 0.975. The integration of machine learning approaches in solar dryers can support predictive maintenance and reduce operational costs.

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Keywords

Selective absorber; carrots and pears drying; two-way ANOVA; machine learning; random forest prediction

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