Natarajan Rajamurugu; Baskaran Srimanickam; K. Elangovan; P. Sarasu; Kulmani Mehar; Ritesh Pratap Singh; Kamakshi Priya K · 10.1016/j.rineng.2025.108218 https://doi.org/10.1016/j.rineng.2025.108218
Results in Engineering
Performance evaluation and machine learning-based prediction of PCM-integrated solar chimney drying for black dates Natarajan Rajamurugu; Baskaran Srimanickam; K. Elangovan; P. Sarasu; Kulmani Mehar; Ritesh Pratap Singh; Kamakshi Priya K • Black date drying assessed under open sun, solar chimney, and PCM-assisted modes. • PCM integration boosted peak thermal efficiency to 49% and drying efficiency to 59%. • Latent heat storage sustained 25% efficiency at 16:00 h, extending drying hours. • MLP model (R2 = 0.90) outperformed RF and SVR in predicting drying performance. • Solar irradiance and airflow identified as key predictors for drying optimization. This study presents a comparative evaluation of black date drying using three solar-based methods: open sun drying, a solar chimney dryer, and a solar chimney dryer integrated with phase change material (PCM). In parallel, machine learning (ML) models were employed to predict and optimize system performance. Experimental findings reveal that the PCM-integrated solar chimney significantly outperformed conventional approaches, achieving peak thermal and drying efficiencies of 49% and 59%, respectively, compared to 20% for open sun drying and 41% for the standalone solar chimney. The latent heat storage of PCM extended effective drying into late hours, sustaining 25% efficiency at 16:00 h against only 11% under open sun drying. Among the tested ML models—multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR)—the MLP demonstrated the highest predictive accuracy (training: RMSE = 0.85, R2 = 0.92; testing: RMSE = 1.10, R2 = 0.90). Feature importance analysis further identified solar irradiance and airflow as dominant parameters governing drying performance. By integrating PCM-based thermal management with AI-driven prediction, this work establishes a scalable, energy-efficient drying solution to mitigate agricultural post-harvest losses, directly supporting global initiatives on sustainable food processing and renewable energy utilization.
Drying
Experimental
Machine learning / AI
Thermal storage / PCM
2025
Results in Engineering
no
2025-11-17 12:00:00 00001973
performance evaluation and machine learning-based prediction of pcm-integrated solar chimney drying for black dates
2025
Results in Engineering
Natarajan Rajamurugu; Baskaran Srimanickam; K. Elangovan; P. Sarasu; Kulmani Mehar; Ritesh Pratap Singh; Kamakshi Priya K
10.1016/j.rineng.2025.108218
Abstract
• Black date drying assessed under open sun, solar chimney, and PCM-assisted modes. • PCM integration boosted peak thermal efficiency to 49% and drying efficiency to 59%. • Latent heat storage sustained 25% efficiency at 16:00 h, extending drying hours. • MLP model (R² = 0.90) outperformed RF and SVR in predicting drying performance. • Solar irradiance and airflow identified as key predictors for drying optimization. This study presents a comparative evaluation of black date drying using three solar-based methods: open sun drying, a solar chimney dryer, and a solar chimney dryer integrated with phase change material (PCM). In parallel, machine learning (ML) models were employed to predict and optimize system performance. Experimental findings reveal that the PCM-integrated solar chimney significantly outperformed conventional approaches, achieving peak thermal and drying efficiencies of 49% and 59%, respectively, compared to 20% for open sun drying and 41% for the standalone solar chimney. The latent heat storage of PCM extended effective drying into late hours, sustaining 25% efficiency at 16:00 h against only 11% under open sun drying. Among the tested ML models—multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR)—the MLP demonstrated the highest predictive accuracy (training: RMSE = 0.85, R² = 0.92; testing: RMSE = 1.10, R² = 0.90). Feature importance analysis further identified solar irradiance and airflow as dominant parameters governing drying performance. By integrating PCM-based thermal management with AI-driven prediction, this work establishes a scalable, energy-efficient drying solution to mitigate agricultural post-harvest losses, directly supporting global initiatives on sustainable food processing and renewable energy utilization.
Abstract by the authors, reproduced under the CC BY-NC-ND 4.0 licence. Original publication via the link below.
Open source