Journal
ENERGY SCIENCE & ENGINEERING
Volume 7, Issue 5, Pages 1649-1658Publisher
WILEY
DOI: 10.1002/ese3.381
Keywords
flat-plate solar collector; nanofluid; neural network; thermal efficiency
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In the current study, Multilayer Perceptron Artificial Neural Network (MLP-ANN) mode, Radial Basis Function Artificial Neural Network (RBF-ANN), and Elman Back Propagation Neural Network (Elamn BP-ANN) are developed to predict the thermal efficiency of a flat-plate solar collector. TiO2 (20 nm)/water nanofluids are prepared using two-step method and used in the designed solar system. All experiments are done in Mashhad city, Iran (Longitude/Latitude: 36.2605 degrees N, 59.6168 degrees E), according to EUROPEAN STANDARD EN 12975-2 as a quasi-dynamic test (QDT) method, and the solar collector is exposed to the south with the tilt angle of 55 degrees. Three levels of inlet temperature (ambient air temperature, 52 and 74 degrees C), 3 levels of volumetric flow rate (36, 72, and 108 L/(m(2).h)), and 4 levels of nanofluid concentrations (0, 0.1, 0.2, and 0.3 wt.%) are considered as the input data, and the thermal efficiency of the solar system is calculated. According to the output results of developed models, the best prediction of thermal performance is obtained by MLP-ANN model, although other generated models are also able to predict the efficiency of the solar collector with appropriated accuracy.
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