Skip to main content
SpringerLink
Log in

Machine learning prediction approach for dynamic performance modeling of an enhanced solar still desalination system

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

An enhanced design for a solar still desalination system which has been proposed in the previously conducted study of the research team is considered here, and the experimental data obtained during a year are employed to develop ANN models for that. Different types of artificial neural network (ANN), as one of the most popular machine learning approaches, are developed and compared together to find the best of them to predict the hourly produced distillate and water temperature in the basin, which are two key performance criteria of the system. Feedforward (FF), backpropagation (BP), and radial basis function (RBF) types of ANN are examined. According to the results, by having the coefficients of determination of 0.963111 and 0.977057, FF and RBF types are the best ANN structures for estimation of the hourly water production and water temperature in the basin, respectively. In addition, the annual error analysis done for the data not used to develop ANN models demonstrates that the average error in prediction of the hourly distillate production and water temperature in the basin varies from 9.03 and 5.13% in January (the highest values) to 4.06 and 2.07% in July (the lowest values), respectively. Moreover, the error for prediction of the daily water production is in the range of 2.41 to 5.84% in the year.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Sohani A, Hoseinzadeh S, Berenjkar K. Experimental analysis of innovative designs for solar still desalination technologies; an in-depth technical and economic assessment. J Energy Storage. 2021;33:101862. https://doi.org/10.1016/j.est.2020.101862.

    Article  Google Scholar 

  2. Goshayeshi HR, Safaei MR. Effect of absorber plate surface shape and glass cover inclination angle on the performance of a passive solar still. Int J Numer Methods Heat Fluid Flow. 2019;30:3183–98.

    Article  Google Scholar 

  3. Doranehgard MH, Samadyar H, Mesbah M, Haratipour P, Samiezade S. High-purity hydrogen production with in situ CO2 capture based on biomass gasification. Fuel. 2017;202:29–35. https://doi.org/10.1016/j.fuel.2017.04.014.

    Article  CAS  Google Scholar 

  4. Dhahri M, Nekoonam S, Hana A, Assad MEH, Arıcı M, Sharifpur M et al. Thermal performance modeling of modified absorber wall of solar chimney-shaped channels system for building ventilation. J Therm Anal Calorim. 2020;1–13.

  5. Hoseinzadeh S, Sohani A, Samiezadeh S, Kariman H, Ghasemi MH. Using computational fluid dynamics for different alternatives water flow path in a thermal photovoltaic (PVT) system. Int J Numer Methods Heat Fluid Flow. 2020.

  6. Mohammadian A, Chehrmonavari H, Kakaee A, Paykani A. Effect of injection strategies on a single-fuel RCCI combustion fueled with isobutanol/isobutanol + DTBP blends. Fuel. 2020;278:118219. https://doi.org/10.1016/j.fuel.2020.118219.

    Article  Google Scholar 

  7. Safaei MR, Goshayeshi HR, Chaer I. Solar still efficiency enhancement by using graphene oxide/paraffin nano-pcm. Energies. 2019;12(10):2002.

    Article  CAS  Google Scholar 

  8. Köse Ö, Koç Y, Yağlı H. Energy, exergy, economy and environmental (4E) analysis and optimization of single, dual and triple configurations of the power systems: rankine cycle/kalina cycle, driven by a gas turbine. Energy Convers Manag. 2021;227:113604. https://doi.org/10.1016/j.enconman.2020.113604.

    Article  Google Scholar 

  9. Razmi AR, Arabkoohsar A, Nami H. Thermoeconomic analysis and multi-objective optimization of a novel hybrid absorption/recompression refrigeration system. Energy. 2020;210:118559. https://doi.org/10.1016/j.energy.2020.118559.

    Article  Google Scholar 

  10. Nami H, Anvari-Moghaddam A, Arabkoohsar A, Razmi AR. 4E analyses of a hybrid waste-driven CHP–ORC plant with flue gas condensation. Sustainability. 2020;12(22):9449.

    Article  CAS  Google Scholar 

  11. Sarafraz MM, Tlili I, Tian Z, Bakouri M, Safaei MR. Smart optimization of a thermosyphon heat pipe for an evacuated tube solar collector using response surface methodology (RSM). Physica A. 2019;534:122146. https://doi.org/10.1016/j.physa.2019.122146.

    Article  Google Scholar 

  12. Maithani R, Kumar A, Zadeh PG, Safaei MR, Gholamalizadeh E. Empirical correlations development for heat transfer and friction factor of a solar rectangular air passage with spherical-shaped turbulence promoters. J Therm Anal Calorim. 2020;139(2):1195–212.

    Article  CAS  Google Scholar 

  13. Sohani A, Sayyaadi H. Providing an accurate method for obtaining the efficiency of a photovoltaic solar module. Renew Energy. 2020;156:395–406. https://doi.org/10.1016/j.renene.2020.04.072.

    Article  Google Scholar 

  14. Yağli H. Examining the receiver heat loss, parametric optimization and exergy analysis of a solar power tower (SPT) system. Energy Sources Part A Recovery Util Environ Eff. 2020;42(17):2155–80.

    Article  Google Scholar 

  15. Al-Yasiri Q, Szabó M, Arıcı M. Single and hybrid nanofluids to enhance performance of flat plate solar collectors: application and obstacles. Period Polytech Mech Eng. 2020;65(1):86–102.

    Article  Google Scholar 

  16. Sedaghatizadeh N, Arjomandi M, Cazzolato B, Kelso R. Wind farm noises: mechanisms and evidence for their dependency on wind direction. Renew Energy. 2017;109:311–22. https://doi.org/10.1016/j.renene.2017.03.046.

    Article  Google Scholar 

  17. Sedaghatizadeh N, Arjomandi M, Kelso R, Cazzolato B, Ghayesh MH. Modelling of wind turbine wake using large eddy simulation. Renew Energy. 2018;115:1166–76. https://doi.org/10.1016/j.renene.2017.09.017.

    Article  Google Scholar 

  18. Toghyani S, Afshari E, Baniasadi E, Shadloo MS. Energy and exergy analyses of a nanofluid based solar cooling and hydrogen production combined system. Renew Energy. 2019;141:1013–25. https://doi.org/10.1016/j.renene.2019.04.073.

    Article  CAS  Google Scholar 

  19. Yang R, Li D, Salazar SL, Rao Z, Arıcı M, Wei W. Photothermal properties and photothermal conversion performance of nano-enhanced paraffin as a phase change thermal energy storage material. Sol Energy Mater Sol Cells. 2021;219:110792. https://doi.org/10.1016/j.solmat.2020.110792.

    Article  CAS  Google Scholar 

  20. Ghalandari M, Maleki A, Haghighi A, Safdari Shadloo M, Alhuyi Nazari M, Tlili I. Applications of nanofluids containing carbon nanotubes in solar energy systems: a review. J Mol Liq. 2020;313:113476. https://doi.org/10.1016/j.molliq.2020.113476.

    Article  CAS  Google Scholar 

  21. Ma Y, Rashidi MM, Mohebbi R, Yang Z. Nanofluid natural convection in a corrugated solar power plant using the hybrid LBM-TVD method. Energy. 2020;199:117402. https://doi.org/10.1016/j.energy.2020.117402.

    Article  CAS  Google Scholar 

  22. Essa FA, Abdullah AS, Omara ZM. Rotating discs solar still: new mechanism of desalination. J Clean Prod. 2020;275:123200. https://doi.org/10.1016/j.jclepro.2020.123200.

    Article  Google Scholar 

  23. Abd Elbar AR, Hassan H. An experimental work on the performance of new integration of photovoltaic panel with solar still in semi-arid climate conditions. Renew Energy. 2020;146:1429–43. https://doi.org/10.1016/j.renene.2019.07.069.

    Article  Google Scholar 

  24. Parsa SM, Rahbar A, Koleini MH, Davoud Javadi Y, Afrand M, Rostami S, et al. First approach on nanofluid-based solar still in high altitude for water desalination and solar water disinfection (SODIS). Desalination. 2020;491:114592. https://doi.org/10.1016/j.desal.2020.114592.

    Article  CAS  Google Scholar 

  25. Hassan H, Ahmed MS, Fathy M, Yousef MS. Impact of salty water medium and condenser on the performance of single acting solar still incorporated with parabolic trough collector. Desalination. 2020;480:114324. https://doi.org/10.1016/j.desal.2020.114324.

    Article  CAS  Google Scholar 

  26. Modi KV, Nayi KH, Sharma SS. Influence of water mass on the performance of spherical basin solar still integrated with parabolic reflector. Groundw Sustain Dev. 2020;10:100299. https://doi.org/10.1016/j.gsd.2019.100299.

    Article  Google Scholar 

  27. Madiouli J, Lashin A, Shigidi I, Badruddin IA, Kessentini A. Experimental study and evaluation of single slope solar still combined with flat plate collector, parabolic trough and packed bed. Sol Energy. 2020;196:358–66. https://doi.org/10.1016/j.solener.2019.12.027.

    Article  Google Scholar 

  28. Omara AAM, Abuelnuor AAA, Mohammed HA, Khiadani M. Phase change materials (PCMs) for improving solar still productivity: a review. J Therm Anal Calorim. 2020;139(3):1585–617.

    Article  CAS  Google Scholar 

  29. Panchal H, Hishan SS, Rahim R, Sadasivuni KK. Solar still with evacuated tubes and calcium stones to enhance the yield: an experimental investigation. Process Saf Environ Prot. 2020;142:150–5. https://doi.org/10.1016/j.psep.2020.06.023.

    Article  CAS  Google Scholar 

  30. Hassan H, Yousef MS, Fathy M, Ahmed MS. Assessment of parabolic trough solar collector assisted solar still at various saline water mediums via energy, exergy, exergoeconomic, and enviroeconomic approaches. Renew Energy. 2020;155:604–16. https://doi.org/10.1016/j.renene.2020.03.126.

    Article  Google Scholar 

  31. El-Said EMS, Elshamy SM, Kabeel AE. Performance enhancement of a tubular solar still by utilizing wire mesh packing under harmonic motion. Desalination. 2020;474:114165. https://doi.org/10.1016/j.desal.2019.114165.

    Article  CAS  Google Scholar 

  32. Shoeibi S, Rahbar N, Abedini Esfahlani A, Kargarsharifabad H. Application of simultaneous thermoelectric cooling and heating to improve the performance of a solar still: an experimental study and exergy analysis. Appl Energy. 2020;263:114581. https://doi.org/10.1016/j.apenergy.2020.114581.

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Manokar AM, Vimala M, Sathyamurthy R, Kabeel AE, Winston DP, Chamkha AJ. Enhancement of potable water production from an inclined photovoltaic panel absorber solar still by integrating with flat-plate collector. Environ Dev Sustain. 2020;22(5):4145–67. https://doi.org/10.1007/s10668-019-00376-7.

    Article  Google Scholar 

  35. Sadeghi HM, Babayan M, Chamkha A. Investigation of using multi-layer PCMs in the tubular heat exchanger with periodic heat transfer boundary condition. Int J Heat Mass Transf. 2020;147:118970. https://doi.org/10.1016/j.ijheatmasstransfer.2019.118970.

    Article  CAS  Google Scholar 

  36. Dogonchi AS, Nayak MK, Karimi N, Chamkha AJ, Ganji DD. Numerical simulation of hydrothermal features of Cu–H2O nanofluid natural convection within a porous annulus considering diverse configurations of heater. J Therm Anal Calorim. 2020;141(5):2109–25. https://doi.org/10.1007/s10973-020-09419-y.

    Article  CAS  Google Scholar 

  37. Hashemi-Tilehnoee M, Dogonchi AS, Seyyedi SM, Chamkha AJ, Ganji DD. Magnetohydrodynamic natural convection and entropy generation analyses inside a nanofluid-filled incinerator-shaped porous cavity with wavy heater block. J Therm Anal Calorim. 2020;141(5):2033–45. https://doi.org/10.1007/s10973-019-09220-6.

    Article  CAS  Google Scholar 

  38. Bahri B, Shahbakhti M, Aziz AA. Real-time modeling of ringing in HCCI engines using artificial neural networks. Energy. 2017;125:509–18. https://doi.org/10.1016/j.energy.2017.02.137.

    Article  Google Scholar 

  39. Jeppesen C, Araya SS, Sahlin SL, Thomas S, Andreasen SJ, Kær SK. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation. J Power Sources. 2017;359:37–47. https://doi.org/10.1016/j.jpowsour.2017.05.021.

    Article  CAS  Google Scholar 

  40. Moradi MH, Sohani A, Zabihigivi M, Wirbser H. A comprehensive approach to find the performance map of a heat pump using experiment and soft computing methods. Energy Convers Manag. 2017;153:224–42. https://doi.org/10.1016/j.enconman.2017.09.070.

    Article  Google Scholar 

  41. Sohani A, Shahverdian MH, Sayyaadi H, Garcia DA. Impact of absolute and relative humidity on the performance of mono and poly crystalline silicon photovoltaics; applying artificial neural network. J Clean Prod. 2020;276:123016. https://doi.org/10.1016/j.jclepro.2020.123016.

    Article  CAS  Google Scholar 

  42. Sohani A, Zabihigivi M, Moradi MH, Sayyaadi H, Hasani BH. A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches. Appl Therm Eng. 2017;110:1589–608. https://doi.org/10.1016/j.applthermaleng.2016.08.216.

    Article  CAS  Google Scholar 

  43. Sohani A, Sayyaadi H, Hasani Balyani H, Hoseinpoori S. A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems. Appl Therm Eng. 2016;107:227–52. https://doi.org/10.1016/j.applthermaleng.2016.06.121.

    Article  Google Scholar 

  44. Abujazar MSS, Fatihah S, Ibrahim IA, Kabeel AE, Sharil S. Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model. J Clean Prod. 2018;170:147–59. https://doi.org/10.1016/j.jclepro.2017.09.092.

    Article  Google Scholar 

  45. Wang Y, Kandeal AW, Swidan A, Sharshir SW, Abdelaziz GB, Halim MA et al. Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm. 2020. arXiv preprint arXiv:2002.03886.

  46. Mashaly AF, Alazba AA. Assessing the accuracy of ANN, ANFIS, and MR techniques in forecasting productivity of an inclined passive solar still in a hot, arid environment. Water SA. 2019;45(2):239–50.

    Google Scholar 

  47. Sharshir SW, Abd Elaziz M, Elkadeem MR. Enhancing thermal performance and modeling prediction of developed pyramid solar still utilizing a modified random vector functional link. Sol Energy. 2020;198:399–409. https://doi.org/10.1016/j.solener.2020.01.061.

    Article  Google Scholar 

  48. 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;1–8.

  49. Bahiraei M, Nazari S, Moayedi H, Safarzadeh H. Using neural network optimized by imperialist competition method and genetic algorithm to predict water productivity of a nanofluid-based solar still equipped with thermoelectric modules. Powder Technol. 2020;366:571–86. https://doi.org/10.1016/j.powtec.2020.02.055.

    Article  CAS  Google Scholar 

  50. 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;277:123232. https://doi.org/10.1016/j.jclepro.2020.123232.

    Article  Google Scholar 

  51. 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;30:101408. https://doi.org/10.1016/j.est.2020.101408.

    Article  Google Scholar 

  52. 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;170:115020. https://doi.org/10.1016/j.applthermaleng.2020.115020.

    Article  Google Scholar 

  53. Elsheikh AH, Katekar VP, Muskens OL, Deshmukh SS, Elaziz MA, Dabour SM. Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate. Process Saf Environ Prot. 2021;148:273–82. https://doi.org/10.1016/j.psep.2020.09.068.

    Article  CAS  Google Scholar 

  54. Sohani A, Sayyaadi H. Employing genetic programming to find the best correlation to predict temperature of solar photovoltaic panels. Energy Convers Manag. 2020;224:113291. https://doi.org/10.1016/j.enconman.2020.113291.

    Article  Google Scholar 

  55. Ghalambaz M, Mehryan SAM, Mashoofi N, Hajjar A, Chamkha AJ, Sheremet M, et al. Free convective melting-solidification heat transfer of nano-encapsulated phase change particles suspensions inside a coaxial pipe. Adv Powder Technol. 2020;31(11):4470–81. https://doi.org/10.1016/j.apt.2020.09.022.

    Article  CAS  Google Scholar 

  56. Ghalambaz M, Mehryan SAM, Zahmatkesh I, Chamkha A. Free convection heat transfer analysis of a suspension of nano-encapsulated phase change materials (NEPCMs) in an inclined porous cavity. Int J Therm Sci. 2020;157:106503. https://doi.org/10.1016/j.ijthermalsci.2020.106503.

    Article  Google Scholar 

  57. Chamkha A, Veismoradi A, Ghalambaz M, Talebizadehsardari P. Phase change heat transfer in an L-shape heatsink occupied with paraffin-copper metal foam. Appl Therm Eng. 2020;177:115493. https://doi.org/10.1016/j.applthermaleng.2020.115493.

    Article  CAS  Google Scholar 

  58. Noghrehabadi A, Mirzaei R, Ghalambaz M, Chamkha A, Ghanbarzadeh A. Boundary layer flow heat and mass transfer study of Sakiadis flow of viscoelastic nanofluids using hybrid neural network-particle swarm optimization (HNNPSO). Therm Sci Eng Prog. 2017;4:150–9. https://doi.org/10.1016/j.tsep.2017.09.003.

    Article  Google Scholar 

  59. Ghalambaz M, Noghrehabadi AR, Behrang MA, Assareh E, Ghanbarzadeh A, Hedayat N. A hybrid neural network and gravitational search algorithm (HNNGSA) method to solve well known Wessinger’s equation. Int J Mech Mechatron Eng. 2011;5(1):147–51.

    Google Scholar 

  60. Rashidi MM, Aghagoli A, Raoofi R. Thermodynamic analysis of the ejector refrigeration cycle using the artificial neural network. Energy. 2017;129:201–15. https://doi.org/10.1016/j.energy.2017.04.089.

    Article  CAS  Google Scholar 

  61. Graupe D. Principles of artificial neural networks. Singapore: World Scientific; 2013.

    Book  Google Scholar 

  62. Abraham A. Artificial neural networks. Handbook of measuring system design. London: Wiley; 2005.

    Google Scholar 

  63. Krenker A, Bester J, Kos A. Introduction to the artificial neural networks. Artificial neural networks-methodological advances and biomedical applications. London: IntechOpen; 2011.

    Google Scholar 

  64. Da Silva IN, Spatti DH, Flauzino RA, Liboni LHB, dos Reis Alves SF. Artificial neural network architectures and training processes. Artificial neural networks. Berlin: Springer; 2017. p. 21–8.

    Google Scholar 

  65. Dumka P, Jain A, Mishra DR. Energy, exergy, and economic analysis of single slope conventional solar still augmented with an ultrasonic fogger and a cotton cloth. J Energy Storage. 2020;30:101541. https://doi.org/10.1016/j.est.2020.101541.

    Article  Google Scholar 

  66. Kabeel AE, Abdelgaied M. Performance enhancement of a photovoltaic panel with reflectors and cooling coupled to a solar still with air injection. J Clean Prod. 2019;224:40–9. https://doi.org/10.1016/j.jclepro.2019.03.199.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering-Energy Division, K.N. Toosi University of Technology, Vanak Sq, No. 15-19, Pardis St., Mollasadra Ave, P.O. Box:19395-1999, 1999 143344, Tehran, Iran

    Ali Sohani

  2. Department of Mechanical and Aeronautical Engineering, Centre for Asset Integrity Management, University of Pretoria, Pretoria, 0081, South Africa

    Siamak Hoseinzadeh

  3. School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran

    Saman Samiezadeh

  4. EMIB Research Group, University of Antwerp, Groenenborgerlaan 171, B-2020, Antwerp, Belgium

    Ivan Verhaert

Authors
  1. Ali Sohani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siamak Hoseinzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saman Samiezadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan Verhaert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siamak Hoseinzadeh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-021-10744-z

Keywords

Search

Navigation