Abstract
This study compares the thermal performance between a conventional and a newly configured solar flat plate water collector system. The principal goal of this newly configured system is to increase the heat transfer rate between the tube and sheet assembly by increasing the contact surface length. The comparative experiments between the conventional and newly configured system were conducted in Tiruchirappalli, India. The experimental outcomes indicated that the overall performance delivered by the modified system with increased contact surface length is 12.41% higher than the existing system. Further, this research attempts to predict the thermal performance delivered by the system using the XGBoost algorithm, a machine-learning technique. The XGBoost algorithm proposed in this study includes four features (three inputs and one output). Real-time data were used to measure the performance delivered by the XGBoost algorithm. The results recorded by this XGBoost algorithm are closer to real-time values. The accuracy of the proposed XGBoost algorithm in predicting the thermal power of a flat plate collector is 99.80%.
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Al-Oran O, Lezsovits F, Aljawabrah A (2020) Exergy and energy amelioration for parabolic trough collector using mono and hybrid nanofluids. J Therm Anal Calorim 140(3):1579–1596. https://doi.org/10.1007/s10973-020-09371-x
Arani RP, Sathyamurthy R, Chamkha A, Kabeel AE, Deverajan M, Kamalakannan K et al (2021) Effect of fins and silicon dioxide nanoparticle black paint on the absorber plate for augmenting yield from tubular solar still. Environ Sci Pollut Res 28(26):35102–35112. https://doi.org/10.1007/s11356-021-13126-y
Charles Franklin J, Chandrasekar M, Sridharan M (2021) Performance of a downstream Finned solar photovoltaic thermal air system. J Therm Sci Eng Appl 13:1–12. https://doi.org/10.1115/1.4049426
Das D, Bordoloi U, Dilip A, Hihu H, Krishna R, Kalita P (2021) Performance investigation of a rectangular spiral flow PV/T collector with a novel form-stable composite material. Appl Therm Eng 182((December 2019)):116035. https://doi.org/10.1016/j.applthermaleng.2020.116035
Dhif K, Mebarek-Oudina F, Chouf S, Vaidya H, Chamkha AJ (2021) Thermal analysis of the solar collector cum storage system using a hybrid-nanofluids. J Nanofluids 10(4):616–626. https://doi.org/10.1166/jon.2021.1807
Eltaweel M, Abdel-Rehim AA (2021) Energy and exergy analysis for stationary solar collectors using nanofluids: a review. Int J Energy Res 45(3):3643–3670. https://doi.org/10.1002/er.6107
Fadaei M, Izadi M, Assareh E, Ershadi A (2023) Conjugated non-Newtonian phase change process in a shell and tube heat exchanger: aparametric-geometric analysis. Appl Therm Eng 220:119795. https://doi.org/10.1016/j.applthermaleng.2022.119795
Fudholi A, Sopian K, Yazdi MH, Hafidz M, Ibrahim A, Kazem HA (2014) Performance analysis of photovoltaic thermal (PVT) water collectors. Energy Convers Manag 78:641–651. https://doi.org/10.1016/j.enconman.2013.11.017
Ibrahim A, Sopian K, Othman MY, Alghoul MA, Zaharim A (2008) Simulation of different configuration of hybrid photovoltaic thermal solar collector (PVTS) designs. In: WSEAS proceeding: communications & information technology 2008, circuits, systems and Signals 2008, applied mathematics, simulation, modelling. Marathon Beach, Attica, Greece, pp 44–49
Izadi M, El Haj Assad M (2021) Chapter 15 - Use of nanofluids in solar energy systems. In: Assad MEH, Rosen MA (eds) Design and performance optimization of renewable energy systems. Academic Press, pp 221–250. https://doi.org/10.1016/B978-0-12-821602-6.00017-1
Lari MO, Sahin AZ (2017) Design, performance and economic analysis of a nano fluid-based photovoltaic/thermal system for residential applications. Energy Convers Manag 149(April):467–484. https://doi.org/10.1016/j.enconman.2017.07.045
Li Z, Ji J, Yuan W, Zhao B, Zhou F, Uddin M et al (2021) Experimental & numerical investigation and optimization on a novel flat-plate PV/T system using CdfTe thin-film solar modules of sandwich structure. Sol Energy 223(February):261–277. https://doi.org/10.1016/j.solener.2021.02.009
Pero C, Leonforte F (2014) Water flat plate PV—thermal collectors: a review. Sol Energy 102:98–115. https://doi.org/10.1016/j.solener.2014.01.025
Said Z, Sharma P, Sundar LS, Li C, Tran DC, Khoa Pham ND, Nguyen XP (2022) Improving the thermal efficiency of a solar flat plate collector using MWCNT-Fe3O4/water hybrid nanofluids and ensemble machine learning. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2022.102448
Sathyanarayanan S, Suresh S, Sridharan M (2022) Effect of sucrose catalyst in the catalytic converter on performance and emission of spark ignition engine. J Therm Sci Eng Appl 14(April):1–10. https://doi.org/10.1115/1.4052692
Sridharan M (2019) Application of generalized regression neural network in predicting the performance of natural convection solar dryer. J Sol Energy Eng. https://doi.org/10.1115/1.4045384
Sridharan M (2020) Application of generalized regression neural network in predicting the performance of solar photovoltaic thermal water collector. Ann Data Sci. https://doi.org/10.1007/s40745-020-00273-1
Sridharan M (2021a) Short review on various applications of fuzzy logic-based expert systems in the field of solar energy. Int J Ambient Energy. https://doi.org/10.1080/01430750.2021.1927839
Sridharan M (2021b) Application of Mamdani fuzzy inference system in predicting the thermal performance of solar distillation still. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02810-5
Sridharan M (2022) Performance comparison study on differently configured solar photovoltaic thermal water collector systems. Int J Ambient Energy. https://doi.org/10.1080/01430750.2022.2102066
Taffese WZ, Espinosa-Leal L (2022) A machine learning method for predicting the chloride migration coefficient of concrete. Constr Build Mater 348(July):128566. https://doi.org/10.1016/j.conbuildmat.2022.128566
Verma SK, Sharma K, Gupta NK, Soni P, Upadhyay N (2020) Performance comparison of innovative spiral shaped solar collector design with conventional flat plate solar collector. Energy. https://doi.org/10.1016/j.energy.2019.116853
Xiong Q, Tayebi T, Izadi M, Siddiqui AA, Ambreen T, Li LKB (2021) Numerical analysis of porous flat plate solar collector under thermal radiation and hybrid nanoparticles using two-phase model. Sustain Energy Technol Assess 47(April):101404. https://doi.org/10.1016/j.seta.2021.101404
Zhang Z, Huang Y, Qin R, Ren W, Wen G (2021) XGBoost-based on-line prediction of seam tensile strength for Al–Li alloy in laser welding: experiment study and modelling. J Manuf Process 64((September 2020)):30–44. https://doi.org/10.1016/j.jmapro.2020.12.004
Zhou L, Garg D, Qiu Y, Kim SM, Mudawar I, Kharangate CR (2020) Machine learning algorithms to predict flow condensation heat transfer coefficient in mini/micro-channel utilizing universal data. Int J Heat Mass Transf 162:120351. https://doi.org/10.1016/j.ijheatmasstransfer.2020.120351
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Sridharan, M. Performance Augmentation Study on a Solar Flat Plate Water Collector System with Modified Absorber Flow Design and its Performance Prediction Using the XGBoost Algorithm: A Machine Learning Approach. Iran J Sci Technol Trans Mech Eng 48, 133–144 (2024). https://doi.org/10.1007/s40997-023-00648-8
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DOI: https://doi.org/10.1007/s40997-023-00648-8