Experimental investigation and machine learning modelling of phase change material-based receiver tube for natural circulated solar parabolic trough system under various weather conditions

This research demonstrates the performance of a novel parabolic trough collector (PTC) that uses a naturally circulated open loop, and it is evaluated experimentally for several classes of solar radiation data. A concentric absorber tube packed with phase change material (PCM) is used in the development of an indoor PTC system. A comparative study has been performed for both bare and PCM-based receiver tubes for various classes of solar radiation data. According to the findings, the overall thermal efficiency of the naturally circulated PTC system in bare receiver tubes is 0.66, 0.57, 0.63, 0.64, 0.66, and 0.63, respectively, at classes 2, 3, 5, 6, 8, 9, and 10. Meanwhile, the overall thermal efficiency of PCM-based receiver tubes is 0.70, 0.62, 0.69, 0.65, 0.68, and 0.66, respectively. However, the instantaneous thermal efficiency of the developed system at various classes of solar radiation can be predicted by using different machine learning (ML) models. These models include linear regression (LR), polynomial regression (PR), decision tree (DT), random forest (RF), artificial neural network (ANN), KNN regression, and Gaussian process regression (GPR). Among all of these, the GPR model is showing higher prediction performance (i.e. RMSE = 0.0049, R-2 = 0.9977) than that of the remaining developed ML models.

Automated summary

This research evaluates the performance of a novel parabolic trough collector (PTC) with a naturally circulated open loop under different classes of solar radiation data. The use of phase change material (PCM) in the receiver tubes improves the overall thermal efficiency compared to bare tubes. Additionally, machine learning models, particularly the Gaussian process regression (GPR) model, can accurately predict the instantaneous thermal efficiency of the developed system under different solar radiation classes.