Experimental Evaluation and Development of Artificial Neural Network Model for the Solar Stills Augmented with the Permanent Magnet and Sandbag


Solar Still
LM algorithm
Permanent magnets

How to Cite

Chauhan R, Dumka P, Mishra DR. Experimental Evaluation and Development of Artificial Neural Network Model for the Solar Stills Augmented with the Permanent Magnet and Sandbag. J. Adv. Therm. Sci. Res. [Internet]. 2022 Apr. 14 [cited 2023 Jan. 31];9:9-23. Available from: https://www.avantipublishers.com/index.php/jatsr/article/view/1190


The availability of potable water is reducing day by day due to rapid growth in the human population and un-planned industrialization around the globe. Although human beings cannot think of survival in the absence of water, the global leadership can still not implement their pacts in reality. Solar still is one of the prominent ways of getting potable water from contaminated water. This manuscript reports the experimental evaluation and developed ANN model for the single basin solar stills having augmentations with the sand-filled cotton bags and ferrite ring permanent magnets. Root mean square error (RMSE), efficiency coefficient (E), the overall index of model performance (OI), and coefficient of residual mass (CRM) values are in good agreement with the proposed developed model of ANN. The proposed ANN model can be utilized to predict distillate yield with a variation of 5% for the reported modified stills. Overall correlation coefficient of CSS, MSS-1&2 are 0.98171, 0.9867, and 0.99542, respectively.



Ayoub GM, Malaeb L. Developments in solar still desalination systems: A critical review. Crit Rev Environ Sci Technol., 2012; 42(19): pp. 2078-2112. https://doi.org/10.1080/10643389.2011.574104

2] Dumka P, Mishra DR. Influence of salt concentration on the performance characteristics of passive solar still. Int J Ambient Energy, 2019. https://doi.org/10.1080/01430750.2019.1611638

Xiao G, et al. A review on solar stills for brine desalination. Appl Energy, 2013; 103: pp. 642-652. https://doi.org/10.1016/j.apenergy.2012.10.029

Muftah AF, Alghoul MA, Fudholi A, Abdul-Majeed MM, Sopian K. Factors affecting basin type solar still productivity: A detailed review. Renew Sustain Energy Rev, 2014; 32: pp. 430-447. https://doi.org/10.1016/j.rser.2013.12.052

Panchal HN, Patel S. An extensive review on different design and climatic parameters to increase distillate output of solar still. Renew Sustain Energy Rev, 2017; 69(December 2015): pp. 750-758, Mar. https://doi.org/10.1016/j.rser.2016.09.001

Kabeel AE, Manokar AM, Sathyamurthy R, Winston DP, El-Agouz SA, Chamkha AJ. A Review on Different Design Modifications Employed in Inclined Solar Still for Enhancing the Productivity. J Sol Energy Eng, 2018; 141(3): p. 031007. https://doi.org/10.1115/1.4041547

Dumka P, Mishra DR. Energy and exergy analysis of conventional and modified solar still integrated with sand bed earth: Study of heat and mass transfer. Desalination, 2018; 437(July 2018): pp. 15-25. https://doi.org/10.1016/j.desal.2018.02.026

Dumka P, Mishra DR. Experimental investigation of modified single slope solar still integrated with earth (I) &(II):Energy and exergy analysis. Energy, 2018; 160: pp. 1144-1157, Oct. https://doi.org/10.1016/j.energy.2018.07.083

9] Kabeel AE, Taamneh Y, Sathyamurthy R, Kumar PN, Manokar AM, Arunkumar T. Experimental study on conventional solar still integrated with inclined solar still under different water depth. Heat Transf - Asian Res, 2019; 48(1): pp. 100-114. https://doi.org/10.1002/htj.21370

Zanganeh P, et al. Productivity enhancement of solar stills by nano-coating of condensing surface. Desalination, 2019; 454(December 2018): pp. 1-9. https://doi.org/10.1016/j.desal.2018.12.007

Das D, Bordoloi U, Kalita P, Boehm RF, Kamble AD. Solar still distillate enhancement techniques and recent developments. Groundw Sustain Dev, 2020; 10(March): p. 100360. https://doi.org/10.1016/j.gsd.2020.100360

Mevada D, et al. Effect of fin configuration parameters on performance of solar still: A review. Groundw Sustain Dev, 2020; 10(September 2019): p. 100289. https://doi.org/10.1016/j.gsd.2019.100289

Saleh B, et al. Investigating the performance of dish solar distiller with phase change material mixed with Al2O3 nanoparticles under different water depths. Environ Sci Pollut Res, 2022. https://doi.org/10.1007/s11356-021-18295-4

Dumka P, Gautam H, Sharma S, Gunawat C, Mishra DR. Impact of Sand Filled Glass Bottles on Performance of Conventional Solar Still. J Basic Appl Sci, 2022; 18: pp. 8-15. https://doi.org/10.29169/1927-5129.2022.18.02

Dumka P, Sharma S, Gautam H, Gunawat C. Impact of Solar Powered Fountain on The Performance of Conventional Solar Still. Int J Eng Res Technol, 2021; 10(11): pp. 109-112.

Mahmood F, Al-Ansari T. Design and analysis of a renewable energy driven greenhouse integrated with a solar still for arid climates. Energy Convers Manag, 2022; 258: p. 115512. https://doi.org/10.1016/j.enconman.2022.115512

Dunkle RV. Solar water distillation: the roof type still and a multiple effect diffusion still. in International Developments in Heat Transfer, ASME, Proc International Heat Transfer, Part V, University of Colorado, 1961, pp. 895-902.

Clark JA. The steady-state performance of a solar still. Sol Energy, 1990; 44(1): pp. 43-49. https://doi.org/10.1016/0038-092X(90)90025-8

Kiatsiriroat T, Bhattacharya SC, Wibulswas P. Prediction of mass transfer rates in solar stills. Energy, 1986; 11(9): pp. 881-886, Sep. https://doi.org/10.1016/0360-5442(86)90007-1

Tsilingiris PT. Combined heat and mass transfer analyses in solar distillation systems - The restrictive conditions and a validity range investigation. Sol Energy, 2012; 86(11): pp. 3288-3300. https://doi.org/10.1016/j.solener.2012.08.009

Tsilingiris PT. Parameters affecting the accuracy of Dunkle ' s model of mass transfer phenomenon at elevated temperatures. Appl Therm Eng, 2015; 75: pp. 203-212. https://doi.org/10.1016/j.applthermaleng.2014.09.010

Dumka P, Chauhan R, Mishra DR. Experimental and theoretical evaluation of a conventional solar still augmented with jute covered plastic balls. J Energy Storage, 2020; 32(June): p. 101874. https://doi.org/10.1016/j.est.2020.101874

Kalogirou SA, Mathioulakis E, Belessiotis V. Arti fi cial neural networks for the performance prediction of large solar systems. Renew Energy, 2014; 63: pp. 90-97. https://doi.org/10.1016/j.renene.2013.08.049

Essa FA, Abd Elaziz M, Elsheikh AH. An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer. Appl Therm Eng, 2020; 170(August 2019): p. 115020. https://doi.org/10.1016/j.applthermaleng.2020.115020

Kalogirou SA, Panteliou S, Dentsoras A. Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev, 2001; 5(4): pp. 373-401. https://doi.org/10.1016/S1364-0321(01)00006-5

Nazari S, Bahiraei M, Moayedi H, Safarzadeh H. A proper model to predict energy efficiency, exergy efficiency, and water productivity of a solar still via optimized neural network. J Clean Prod, 2020; 277: p. 123232. https://doi.org/10.1016/j.jclepro.2020.123232

Sohani A, Hoseinzadeh S, Samiezadeh S, Verhaert I. Machine learning prediction approach for dynamic performance modeling of an enhanced solar still desalination system. J Therm Anal Calorim, 2022; 147(5): pp. 3919-3930. https://doi.org/10.1007/s10973-021-10744-z

Santos NI, Said AM, James DE, Venkatesh NH. Modeling solar still production using local weather data and artificial neural networks. Renew Energy, 2012; 40(1): pp. 71-79. https://doi.org/10.1016/j.renene.2011.09.018

Chauhan R, Dumka P, Mishra DR. Modelling conventional and solar earth still by using the LM algorithm-based artificial neural network. Int J Ambient Energy, 2020; pp. 1-8. https://doi.org/10.1080/01430750.2019.1707113

Mashaly AF, Alazba AA. Comparative investigation of artificial neural network learning algorithms for modeling solar still production. J Water Reuse Desalin, 2015; 5(4): pp. 480-493. https://doi.org/10.2166/wrd.2015.009

Mashaly AF, Alazba AA. Comparison of ANN, MVR, and SWR models for computing thermal efficiency of a solar still. Int J Green Energy, 2016; 13(10): pp. 1016-1025. https://doi.org/10.1080/15435075.2016.1206000

Mashaly AF, Alazba AA. Thermal performance analysis of an inclined passive solar still using agricultural drainage water and artificial neural network in arid climate. Sol Energy, 2017; 153: pp. 383-395. https://doi.org/10.1016/j.solener.2017.05.083

Hidouri K, Mishra DR, Benhmidene A, Chouachi B. Experimental and theoretical evaluation of a hybrid solar still integrated with an air compressor using ANN. Desalin Water Treat, 2017; 88(June 2018): pp. 52-59. https://doi.org/10.5004/dwt.2017.21333

Chauhan R, Sharma S, Pachauri R, Dumka P, Mishra DR. Experimental and theoretical evaluation of thermophysical properties for moist air within solar still by using different algorithms of artificial neural network. J Energy Storage, 2020; 30(February): p. 101408. https://doi.org/10.1016/j.est.2020.101408

Dumka P, Kushwah Y, Sharma A, Mishra DR. Comparative analysis and experimental evaluation of single slope solar still augmented with permanent magnets and conventional solar still. Desalination, 2019; 459. https://doi.org/10.1016/j.desal.2019.02.012

Dumka P, Sharma A, Kushwah Y, Raghav AS, Mishra DR. Performance evaluation of single slope solar still augmented with sand-filled cotton bags. J Energy Storage, 2019; 25: p. 100888, Oct. https://doi.org/10.1016/j.est.2019.100888

Dumka P, Mishra DR. Performance evaluation of single slope solar still augmented with the ultrasonic fogger. Energy, 2020; 190: p. 116398, Oct. https://doi.org/10.1016/j.energy.2019.116398

Essa FA, Abdullah AS, Omara ZM, Kabeel AE, Gamiel Y. Experimental study on the performance of trays solar still with cracks and reflectors. Appl Therm Eng, 2021; 188: p. 116652. https://doi.org/10.1016/j.applthermaleng.2021.116652

Tang S, Yang Y. Why neural networks apply to scientific computing?. Theor Appl Mech Lett, 2021; 11(3): p. 100242. https://doi.org/10.1016/j.taml.2021.100242

Katal A, Singh N. Artificial Neural Network: Models, Applications, and Challenges. in Innovative Trends in Computational Intelligence, R. Tomar, M. D. Hina, R. Zitouni, and A. Ramdane-Cherif, Eds. Cham: Springer International Publishing, 2022; pp. 235-257. https://doi.org/10.1007/978-3-030-78284-9_11

Jawad J, Hawari AH, Zaidi SJ. Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review. Chem Eng J, 2021; 419: p. 129540. https://doi.org/10.1016/j.cej.2021.129540

Dombi J, Jónás T. The generalized sigmoid function and its connection with logical operators. Int J Approx Reason, 2022; 143: pp. 121-138. https://doi.org/10.1016/j.ijar.2022.01.006

Bilski J, Kowalczyk B, Marchlewska A, Zurada JM. Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks. J Artif Intell Soft Comput Res, 2020; 10(4): pp. 299-316. https://doi.org/10.2478/jaiscr-2020-0020

Zare H, Hajarian M. An efficient Gauss-Newton algorithm for solving regularized total least squares problems. Numer Algorithms, 2022; 89(3): pp. 1049-1073. https://doi.org/10.1007/s11075-021-01145-2

Ozyildirim BM, Kiran M. Levenberg-Marquardt multi-classification using hinge loss function. Neural Networks, 2021; 143: pp. 564-571. https://doi.org/10.1016/j.neunet.2021.07.010

Hagan MT, Demuth HB, Beale MH. Neural Network Design, 2nd ed. CENGAGE Learning, 1995.

Meng H, Yuan F, Yan T, Zeng M. Indoor Positioning of RBF Neural Network Based on Improved Fast Clustering Algorithm Combined with LM Algorithm. IEEE Access, 2019; 7: pp. 5932-5945. https://doi.org/10.1109/ACCESS.2018.2888616

Gao J, Zhang Y, Du Y, Li Q. Optimization of the tire ice traction using combined Levenberg-Marquardt (LM) algorithm and neural network. J Brazilian Soc Mech Sci Eng, 2019; 41(1): p. 40. https://doi.org/10.1007/s40430-018-1545-2

Arbat G, Puig-Bargués J, Barragán J, Bonany J, de Cartagena FR. Monitoring soil water status for micro-irrigation management versus modelling approach. Biosyst Eng, 2008; 100(2): pp. 286-296. https://doi.org/10.1016/j.biosystemseng.2008.02.008

Alazba AA, Mattar MA, ElNesr MN, Amin MT. Field assessment of friction head loss and friction correction factor equations. J Irrig Drain Eng, 2011; 138(2): pp. 166-176. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000387

Jalal FE, Xu Y, Iqbal M, Javed MF, Jamhiri B. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP. J Environ Manage, 2021; 289: p. 112420. https://doi.org/10.1016/j.jenvman.2021.112420

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2022 Rishika Chauhan, Pankaj Dumka, Dhananjay R. Mishra