Optimization of the Thermal Performance of the Solar Water Heater (SWH) Using Stochastic Technique

Badr Ouhammou, Mohammed AGGOUR, Brahim DAOUCHI

Abstract


The efficiency of Solar Water Collector (SWH) is low and in order to increase its thermal performances various optimization techniques were used. In this paper, a stochastic method (Genetic Algorithm (GA)) was adopted to increase the efficiency of the active SWH under various climatic conditions and for a various operation parameters. The optimization model was introducing a lot of objectives in order to evaluate the optimality of the SWH. For the dynamic Reynolds number, solar radiation intensity and ambient temperature, different values of plate emissivity, a different number of the glass cover and velocity of air, the thermal performance was obtained and it is compared with the experimental results of [16]. It is established by the studies carried out based upon this algorithm that the maximum thermal efï¬ciency comes out to be 75,28 %. The application of our results was applied in such application as heating anaerobic digester by SWH [17]. The goal is to support decision makers in the deï¬nition of the optimal thermal performance and of the optimal collector flat area in order to give a good compromise between the collector efï¬ciency and the output water temperature in the country.

Keywords


Genetic algorithms Optimization; Thermal performance; solar water heater; Thermal solar application.

Full Text:

PDF

References


Konak A, Coit DW, Smith AE. Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety 2006;91:992–1007. doi:10.1016/j.ress.2005.11.018.

Kalogirou SA. Optimization of solar systems using artificial neural-networks and genetic algorithms. Applied Energy 2004;77:383–405. doi:10.1016/s0306-2619(03)00153-3.

Loomans M, Visser H. Application of the genetic algorithm for optimisation of large solar hot water systems. Solar Energy 2002;72:427–39. doi:10.1016/s0038-092x(02)00020-8.

Varun. Thermal performance optimization of a flat plate solar air heater using genetic algorithm. Applied Energy 2010;87:1793–9. doi:10.1016/j.apenergy.2009.10.015.

Varun, Sharma N, Bhat IK, Grover D. Optimization of a smooth flat plate solar air heater using stochastic iterative perturbation technique. Solar Energy 2011;85:2331–7. doi:10.1016/j.solener.2011.06.022.

Kulkarni GN, Kedare SB, Bandyopadhyay S. Determination of design space and optimization of solar water heating systems. Solar Energy 2007;81:958–68. doi:10.1016/j.solener.2006.12.003.

Gholap AK, Khan JA. Design and multi-objective optimization of heat exchangers for refrigerators. Applied Energy 2007;84:1226–39. doi:10.1016/j.apenergy.2007.02.014.

Doodman AR, Fesanghary M, Hosseini R. A robust stochastic approach for design optimization of air-cooled heat exchangers. Applied Energy 2009;86:1240–5. doi:10.1016/j.apenergy.2008.08.021.

Klein SA. Calculation of flat-plate collector loss coefficients. Solar Energy 1975;17:79–80. doi:10.1016/0038-092x(75)90020-1.

Kalogirou SA. Solar thermal collectors and applications. Progress in Energy and Combustion Science 2004;30:231–95. doi:10.1016/j.pecs.2004.02.001.

UNESCO documents and publications 1985. http://unesdoc.unesco.org/ulis/cgi-bin/ulis.pl?database=ged&req=2&look=all&no=123413 (accessed December 19, 2016).

Genetic algorithms in search, optimization, and machine learning. Choice Reviews Online 1989;27:27–0936 – 27–0936. doi:10.5860/choice.27-0936.

Kunakote T, Bureerat S. Multi-objective topology optimization using evolutionary algorithms. Engineering Optimization 2011;43:541–57. doi:10.1080/0305215x.2010.502935.

Sharma N, Varun. Stochastic techniques used for optimization in solar systems: A review. Renewable and Sustainable Energy Reviews 2012;16:1399–411. doi:10.1016/j.rser.2011.11.019.

Genetic algorithms in search, optimization, and machine learning. Choice Reviews Online 1989;27:27–0936 – 27–0936. doi:10.5860/choice.27-0936.

Dagdougui H, Ouammi A, Robba M, Sacile R. Thermal analysis and performance optimization of a solar water heater flat plate collector: Application to Tétouan (Morocco). Renewable and Sustainable Energy Reviews 2011;15:630–8. doi:10.1016/j.rser.2010.09.010

Ouhammou. B, Aggour. M; A. Frimane Feasibility study of integrating solar energy into anaerobic digester reactor for improved performances using TRNSYS simulation: Application kenitra Morocco. Energy Procedia-2017. 402-407. doi.org/10.1016/j.egypro.2017.10.255.

M.S. Hossain Lipu; Feature Selection and Optimal Neural Network Algorithm for the State of Charge Estimation of Lithium-ion Battery for Electric Vehicle Application; IJRER vol.7-(4); 2017.

Yousef Allahvirdizadeh , Mustafa Mohamadian et al,;Optimization of a Fuzzy Based Energy Management Strategy for a PV/WT/FC Hybrid Renewable System; IJRER vol.7-(4);2017

Abdullahi Abubakar Mas’ud; An Optimal Sizing Algorithm for a Hybrid Renewable Energy System; vol.7-(4); 2017.

H. Loutfi, A. Bernatchou et al; Generation of Horizontal Hourly Global Solar Radiation From Exogenous Variables Using an Artificial Neural Network in Fes (Morocco); IJRER vol.7-(3), 2017.

Hichem Othmani, Fares Sassi; Fuzzy Optimization of a Photovoltaic Pumping System: Implementation and Measurements; IJRER vol.7-(3), 2017.




DOI (PDF): https://doi.org/10.20508/ijrer.v8i3.7666.g7439

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is indexed in EI Compendex, SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics)and CrossRef.

IJRER has been indexed in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4