Experimental and theoretical evaluation of thermophysical properties for moist air within solar still by using different algorithms of artificial neural network

Abstract In this research article, an attempt has been made to predict the thermophysical properties of moist air in a solar still cavity with the help of Artificial Neural Network (ANN) modelling. Six training algorithms have been used to train, test, and validate the ANN model (viz. OSS, CGP, CGF, RP, SCG, and LM). Water and inner glass cover temperatures were selected as the input parameters whereas, the model output are: thermal conductivity, partial vapour pressure at water & glass surface, thermal conductivity, volumetric expansivity, specific heat, latent heat of vaporization, and dynamic viscosity. The findings have revealed that the proposed ANN model can be used to predict the thermophysical properties of moist air with excellent accuracy. The results of ANN model were tested against the well-established relations of Tsilingiris. Out of all the training algorithms used LM was found to be the best in all the stages of ANN modelling, as the results are well within an accuracy level of more than 95%. Hence, the developed LM algorithm-based ANN model is one of the most suitable algorithm for the prediction of the thermophysical properties of moist air.

[1]  Martin T. Hagan,et al.  Neural network design , 1995 .

[2]  A. A. Alazba,et al.  Comparative investigation of artificial neural network learning algorithms for modeling solar still production , 2015 .

[3]  Ke Cheng,et al.  A new approach to performance analysis of a seawater desalination system by an artificial neural network , 2007 .

[4]  P. T. Tsilingiris,et al.  The influence of binary mixture thermophysical properties in the analysis of heat and mass transfer processes in solar distillation systems , 2007 .

[5]  Dhananjay R. Mishra,et al.  Performance evaluation of single slope solar still augmented with the ultrasonic fogger , 2020 .

[6]  Soteris A. Kalogirou,et al.  Artificial neural networks for the performance prediction of large solar systems , 2014 .

[7]  Amaya Martínez-Gracia,et al.  Exergy costs analysis of water desalination and purification techniques by transfer functions , 2016 .

[8]  A. E. Kabeel,et al.  Techniques used to improve the performance of the stepped solar still—A review , 2015 .

[9]  G. Tiwari,et al.  Estimation of convective mass transfer in solar distillation systems , 1996 .

[10]  Ali Azadeh,et al.  Optimum estimation and forecasting of renewable energy consumption by artificial neural networks , 2013 .

[11]  A. E. Kabeel,et al.  Cost analysis of different solar still configurations , 2010 .

[12]  Mohammad N. Elnesr,et al.  Field Assessment of Friction Head Loss and Friction Correction Factor Equations , 2012 .

[13]  Ahmed F. Mashaly,et al.  Thermal performance analysis of an inclined passive solar still using agricultural drainage water and artificial neural network in arid climate , 2017 .

[14]  Shigeki Toyama,et al.  SIMULATION OF A MULTIEFFECT SOLAR STILL AND THE STATIC CHARACTERISTICS , 1987 .

[15]  Aly Marei Said,et al.  Modeling solar still production using local weather data and artificial neural networks , 2012 .

[16]  Tanongkiat Kiatsiriroat,et al.  Prediction of mass transfer rates in solar stills , 1986 .

[17]  Fei Wang,et al.  Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters , 2012 .

[18]  N. Al Mahdi,et al.  Performance prediction of a multi-basin solar still , 1992 .

[19]  Gerard Arbat,et al.  Monitoring soil water status for micro-irrigation management versus modelling approach , 2008 .

[20]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[21]  Arvind Tiwari,et al.  Solar Distillation Practice For Water Desalination Systems , 2008 .

[22]  Jim A. Clark,et al.  The steady-state performance of a solar still , 1990 .

[23]  A. S. Abdullah,et al.  Improving the productivity of solar still by using water fan and wind turbine , 2017 .

[24]  Dhananjay R. Mishra,et al.  Comparative analysis and experimental evaluation of single slope solar still augmented with permanent magnets and conventional solar still , 2019, Desalination.

[25]  A. E. Kabeel,et al.  The cooling techniques of the solar stills' glass covers – A review , 2017 .

[26]  Swellam W. Sharshir,et al.  Enhancing the solar still performance using nanofluids and glass cover cooling: Experimental study , 2017 .

[27]  Nader Rahbar,et al.  Utilization of thermoelectric cooling in a portable active solar still — An experimental study on winter days , 2011 .

[28]  Khaoula Hidouri,et al.  Experimental and theoretical evaluation of a hybrid solar still integrated with an air compressor using ANN , 2017 .

[29]  P. T. Tsilingiris.  Parameters affecting the accuracy of Dunkle's model of mass transfer phenomenon at elevated temperatures , 2015 .

[30]  A. A. Alazba,et al.  Neural network approach for predicting solar still production using agricultural drainage as a feedwater source , 2016 .

[31]  A. E. Kabeel,et al.  Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model , 2018 .

[32]  G. N. Tiwari,et al.  Review on the energy and economic efficiencies of passive and active solar distillation systems , 2017 .

[33]  P. P. Tripathy,et al.  Neural network approach for food temperature prediction during solar drying , 2009 .

[34]  Dhananjay R. Mishra,et al.  Performance evaluation of single slope solar still augmented with sand-filled cotton bags , 2019, Journal of Energy Storage.

[35]  Rishika Chauhan,et al.  Modelling conventional and solar earth still by using the LM algorithm-based artificial neural network , 2020 .