The current investigation reveals the need for combinatorial and geometric optimization for parabolic trough solar collectors (PTSCs) and proposes methods to perform them. An analytical model of PTSC was drafted, which emerged to be quite accurate when exhaustively validated using experimental results. The analysis reveals that superior properties of design components (solar selective absorber coatings (SSACs), heat transfer fluids (HTFs), etc.) cannot guarantee better performance, as there are many interacting factors. Also, a particular combination of components can perform better at a certain temperature while lagging at another. To acquire an optimal combination of components, combinatorial optimization is introduced and carried out for PTSCs, using genetic algorithm (GA). Six SSACs, three absorber materials, and five HTFs are considered, significant efficiency improvements of 8% at 150 °C and 6% at 300 °C are observed. This study discloses that geometrical parameters (length and width of collector, focal length, etc.) possess positive as well negative impacts on efficiency. By varying these in a reasonable range, optimal values that lead to improved efficiency can be obtained. Particle swarm optimization (PSO) is used to attain this geometric optimization, and improvement of ≥3% in efficiency is noticed by only ±5% variation in dimensions.

Parabolic Trough Solar Collector Analytical Modeling Geometric Optimization Combinatorial Optimization Particle Swarm Optimization Genetic Algorithm

Jebasingh VK, Herbert GMJ, Renew. Sust. Energ. Rev., 54, 1085 (2016)
Goel A, Manik G, Mahadeva R, Advances in intelligent systems and computing, Springer, Singapore (2020).
Kalogirou S, Appl. Energy, 76(4), 337 (2003)
Silva R, Javier CF, Perez-garcia M, Energy Procedia., 48, 1210 (2014)
Verma OP, Manik G, Mohammed TH, Korean J. Chem. Eng., 34(10), 2570 (2017)
Fernandez-Garcia A, Zarza E, Valenzuela L, Perez M, Renew. Sust. Energ. Rev., 14, 1695 (2010)
Ajbar W, Parrales A, Cruz-Jacobo U, Conde-Gutierrez RA, Bassam A, Jaramillo OJ, Hernandez JA, Appl. Therm. Eng., 189, 116651 (2021)
Habibi H, Zoghi M, Chitsaz A, Shamsaiee M, Appl. Therm. Eng., 180, 115827 (2020)
Liu QB, Yang ML, Lei J, Jin HG, Gao ZC, Wang YL, Sol. Energy, 86(7), 1973 (2012)
Zadeh PM, Sokhansefat T, Kasaeian AB, Kowsary F, Akbarzadeh A, Energy, 82, 857 (2015)
Boukelia TE, Arslan O, Mecibah MS, Appl. Therm. Eng., 107, 1210 (2016)
Silva R, Berenguel M, Perez M, Fernandez-Garcia A, Appl. Energy, 113, 603 (2014)
Wang W, Li M, Hassanien RHE, Ji ME, Feng Z, Int. J. Green Energy, 14, 819 (2017)
Tzuc OM, Bassam A, Soberanis MAE, et al., J. Renew. Sustain. Energy, 9, 1 (2017)
Bellos E, Tzivanidis C, Energy, 149, 47 (2018)
Hoseinzadeh H, Kasaeian A, Shafii MB, Energy Sci. Eng., 7, 2950 (2019)
Ehyaei MA, Ahmadi A, El Haj Assad M, Salameh T, J. Clean Prod., 234, 285 (2019)
Cheng ZD, He YL, Du BC, Wang K, Liang Q, Appl. Energy, 148, 282 (2015)
Lopez-Martin R, Valenzuela L, Case Stud. Therm. Eng., 12, 414 (2018)
Forristall R, Heat Transfer Analysis and Modeling of a Parabolic Trough Solar Receiver Implemented in Engineering Equation Solver, NREL/TP-550-34169 (2003).
Duffie JA, Beckman WA, Solar engineering of thermal processes, 4th Ed., John Wiley & Sons, Inc. (2013).
Dudley VE, Kolb GJ, Sloan M, Kearney D, Test results -SEGS LS2 collector. Sandia National Laboratories (1994).
Dudley VE, Evans LR, Matthews CW, Test results, Industrial Solar Technology parabolic trough solar collector (1995).
Valenzuela L, Lopez-Martin R, Zarza E, Energy, 70, 456 (2014)
Lippke F, Simulation of the part-load behaviour of a 30MW SEGS plant (1995).
Incropera F, Dewitt D, Fundamentals of heat and mass transfer, 6th Ed., John Wiley & Sons (2006).
Cengel YA, Ghajar AJ, Heat and mass transfer, 5th Ed., McGraw-Hill Education (2015).
Swinbank WC, Q. J. R. Meteorol. Soc., 89, 339 (1963)
Rohsenow WM, Hartnett JR, Handbook of heat transfer, 3rd Ed., McGraw-Hill Education (1999).
Gnielinski V, Int. J. Heat Mass Transf., 63, 134 (2013)
Ratzel AC, Hickox CE, Gartling DK, J. Heat Transf. Trans ASME, 101, 108 (1979)
Kuehn TH, Goldstein RJ, Int. J. Heat Mass Transf., 19, 1127 (1976)
Churchill SW, Usagi R, AIChE J., 18, 1121 (1972)
Morgan VT, Adv. Heat Transf., 11, 199 (1975)
Yilmaz IH, Soylemez MS, Energy Conv. Manag., 88, 768 (2014)
Padilla RV, Simplified methodology for designing parabolic trough solar power plants, University of South Florida (2011).
Aguilar R, Valenzuela L, Avila-Marin AL, Garcia-Ybarra PL, Energy Conv. Manag., 196, 807 (2019)
Sivanandam SN, Deepa SN, Principles of soft computing, Wiley India Pvt. Ltd, New Delhi (2017).
Valencia JJ, Quested PN, Thermophysical Properties, in: ASM Handb., ASM international, 468 (2008).
Dow Corning Corporation, SYLTHERM 800 Heat Transfer Fluid: Product Technical Data, 1997. http://www.dow.com/heattrans.
Eastman, Therminol VP-1, Technical Bulletin TF9141, 2019. https://www.eastman.com/Literature_Center/T/TF9141.pdf.
HEAT TRANSFER FLUIDS XCELTHERM ® 600 - Engineering Properties (2015).
Fluid HT, DOWTHERM J Heat Transfer Fluid, 1 (1977).
Heat S, Conductivity T, Pressure V, PARATHERMTM HR SYNTHETIC-AROMATIC HEAT, 1 (2020).
Hojjati A, Monadi M, Faridhosseini A, Mohammadi M, J. Hydrol. Hydromechanics, 66, 323 (2018)
Kennedy J, Eberhart R, Particle swarm optimization, in: Proc. ICNN'95 - Int. Conf. Neural Networks, IEEE, n.d.: pp. 1942-1948.
Kumar M, Sharma SC, Goel S, Mishra SK, Husain A, Neural Comput. Appl., 32, 18285 (2020)
Shi Y, Russell Eberhart, A modified particle swarm optimizer algorithm, 2007 8th Int. Conf. Electron. Meas. Instruments, ICEMI.
Van Den Bergh F, Engelbrecht AP, Inf. Sci., 176, 937 (2006)
Eberhart RC, Shi Y, Comparing inertia weights and constriction factors in particle swarm optimization, Proc. 2000 Congr. Evol. Comput. CEC 2000.