Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Yao; Selamat, Ali; Zhang, Yuxin; Alrabaiah, Hussam; Hisam Omar, Abdullah
• Is Part Of: Sustainable energy technologies and assessments. Volume 52:Part A(2022)
• Publication Date: 2022-08
• Subjects: Bf Basefluid -- Nf Nanofluid -- ANN Artificial Neural Network -- HVAC Heating, Ventilation, and Air Conditioning -- rGO Reduced Graphene Oxide -- DW Distilled Water -- T Temp., Temperature -- Vf Vol., Volume fraction -- TC Thermal Conductivity -- VIS Viscosity; Thermo-Rheology behavior -- Flat Plate Solar collector -- Reduced Graphene Oxide -- Artificial Neural Networks -- Fuzzy system; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102062
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Reduced graphene oxide (rGO) has applications in Water purification, and Energy conversion. In this study, a water-based nanofluid containing rGO was formed at specific concentrations. The nanofluid Thermo-Rheology behavior was studied at room temperature to 50 °C. Viscosity was detected at specific RPMs from 10 to 100. The results showed that this nanofluid has excellent thermo-rheology properties. Flat plate solar collectors could heat the fluid inside using sunlight from a wide range of varied angles. Thus, the prepared nanofluid was used as the working fluid in the solar collector tubes. The results showed that this nanofluid can be used instead of water. The aims of this study are to optimize the process and lessen the examination costs, thus, Artificial Neural Networks algorithms of Orthogonal Distance Regression (ODR), Levenberg Marquardt (LM), and Fuzzy system of Recursive Least Squares were trained. Results proved that Artificial Neural Network and Fuzzy systems should be trained to predict the data of thermal conductivity and viscosity with acceptable coefficient of determination.