Artificial neural network modeling and optimization of thermophysical behavior of MXene Ionanofluids for hybrid solar photovoltaic and thermal systems. (1st August 2022)

• Record Type: Journal Article
• Authors: Shaik, Nagoor Basha; Inayat, Muddasser; Benjapolakul, Watit; Bakthavatchalam, Balaji; Barewar, Surendra D.; Asdornwised, Widhyakorn; Chaitusaney, Surachai
• Is Part Of: Thermal science and engineering progress. Volume 33(2022)
• Publication Date: 2022-08-01
• Subjects: MXene ionanofluids -- Artificial neural networks -- Response surface methodology -- Solar energy -- Thermophysical properties; Heat engineering -- Periodicals; Heat engineering; Thermodynamics; Periodicals; 621.402
• DOI: 10.1016/j.tsep.2022.101391
• Languages: English
• ISSNs: 2451-9049

Abstract

Highlights: Thermophysical properties of MXene Ionanofluids are presented for PV/T system. LMBPNN approach was used to predict the thermophysical behavior of MXene Ionanofluids. LMBPNN model performance in predicting the thermophysical property behavior. Optimization and Parametric analysis of thermophysical properties was performed using RSM. Abstract: Newly developed MXene materials are excellent contender for improving thermal systems' high energy and power density. MXene Ionanofluids are novel materials; their optimum thermophysical behavior at various synthesis conditions has not been addressed yet. The aim of this study is to investigate the effect of synthesis conditions (temperature 303–343 K and nanofluids concentration 0.1–0.4 wt%) on the thermophysical properties (thermal conductivity, specific heat capacity, thermal stability, and viscosity) of MXene Ionanofluids. Levenberg Marquardt based Artificial Neural Network (ANN) model and Response Surface Methodology (RSM) based optimization techniques have been adopted for systematic parametric analysis of MXene Ionanofluids thermophysical properties using experimental data. ANN and RSM have predicted the thermophysical behavior of MXene ionanofluids at optimized conditions. The experimental data were used to train, test, and validate the ANN model. The neural network could correctly predict the outcomes for the four properties based on the numerical performance with R 2 values close to 1, and a prediction error is 2%. The performance of the proposed LM-based back-propagation algorithm demonstrates that the error involved has been minimal and acceptable. RSM has developed correction among input parameters and thermophysical properties of MXene Ionanofluids. The comparison between experimental results and the proposed correlations revealed excellent practical compatibility. Optimized thermophysical properties of MXene Ionanofluids thermal conductivity of 0.776 W/m.K, specific heat capacity of 2.5 J/g.K, thermal stability of 0.33931 wt loss %, and viscosity of 11.696 mPa.s were obtained at a temperature of 343 K and nanofluids concentration of 0.3 wt%. MXene Ionanofluids with optimal thermophysical properties could be used for the greatest performance of hybrid solar photovoltaic and thermal system applications.