Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms

• Published in: Solar energy 2021-04, Vol.218, p.57-67
• Main Authors: Karaağaç, Mehmet Onur, Ergün, Alper, Ağbulut, Ümit, Gürel, Ali Etem, Ceylan, İlhan
• Format: Article
• Language: English
• Subjects: Algorithms; Aluminum oxide; Dry matter; Drying; Energy efficiency; Exergy; Learning algorithms; Machine learning; Moisture content; Nano-enhanced PCM; Nanoparticle; Paraffin; Paraffin wax; Parameters; Photovoltaics; Prediction algorihtms; Predictions; Solar dryers; Solar Drying; Solar energy; Solar radiation; Support vector machines; Thermal energy; Thermal energy storage; Water content
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2021.02.028

•The electrical efficiency of the CPV/TSD system varies between 10.82% and 11.32%.•Drying rate was calculated to be 0.436 g matter/g dry matter.min for this system.•Overall thermal and exergy efficiency were found to be 20% and 8%, respectively.•Prediction results of ANN and SVM algorithms were compared with four metrics.•It is noticed that ANN and SVM have the ability to successfully predict the drying parameters. In this paper, a concentrated photovoltaic-thermal solar dryer (CPV/TSD) using nano-enhanced PCM (Al2O3-Paraffin wax) is experimentally studied. A comprehensive thermodynamic analysis of the system according to the first and second laws is discussed. Besides, the drying parameters (moisture content and moisture ratio) are predicted using the two machine learning algorithms (ANN and SVM) and compared the prediction success with four evaluation metrics (R2, rRMSE, MBE, and rMAE). The overall thermal energy efficiency and exergy efficiency of the CPV/TSD system are found to be 20% and 8%, respectively. Although solar radiation to the environment has decreased a lot, it has been found that the thermal energy transferred to the nano-enhanced PCM prevents the decrease in greenhouse temperature for the first 100 min. In the system, mushrooms are dried from the initial moisture content of 17.45 g water/g dry matter to the final moisture content of 0.0515 g water/g dry matter. Then the drying rate value for CPV/TSD system is calculated to be 0.436 g matter/g dry matter.min. On the other hand, even if both ANN and SVM algorithms have exhibited very satisfying results, ANN is coming to the fore in the prediction of the drying parameters considering all evaluation metrics together.