Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid. Issue 5 (17th June 2019)

• Record Type: Journal Article
• Authors: Sadeghzadeh, Milad; Ahmadi, Mohammad Hossein; Kahani, Mostafa; Sakhaeinia, Hossein; Chaji, Hossein; Chen, Lingen
• Is Part Of: Energy science & engineering. Volume 7:Issue 5(2019)
• Page Start: 1649
• Page End: 1658
• Publication Date: 2019-06-17
• Subjects: flat‐plate solar collector -- nanofluid -- neural network -- thermal efficiency; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.381
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: 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°N, 59.6168°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°. Three levels of inlet temperature (ambient air temperature, 52 and 74°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. Abstract : Application of TiO2 /water nanofluid in flat‐plate solar collector. The maximum obtained thermal efficiency is around 55.76%. Machine‐learning methods to predict the thermal efficiency of solar system. MLP, Elman, and RBF methods are suitable ways to predict the performance of system. Best prediction of thermal performance was obtained by MLP‐ANN model.