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Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material

Year 2024, , 199 - 218, 24.06.2024
https://doi.org/10.58559/ijes.1420875

Abstract

Accurate prediction of melting time is crucial in designing Thermal Energy Storage (TES) systems based on cylindrically encapsulated Phase Change Materials (PCMs). The melting time of a cylindrical encapsulated PCM directly correlates with the energy stored in the system. This study introduces a precise prediction model for the total melting time of cylindrically encapsulated PCM, utilizing a machine learning algorithm. The model, developed with the Multilayer Perceptron (MLP) method, demonstrated superior performance compared to the correlation equation proposed in the literature. The Mean Absolute Percentage Error (MAPE) value for the correlation equation was 16.68%, while the MLP model achieved a significantly lower MAPE of 4.07%, indicating its success in capturing the intricate relationship between input parameters and melting time. Furthermore, optimization results using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) underscore the importance of striking a balance between stored energy and power during the design process. Maximizing stored energy (81.78 kJ) minimizes power (12.69 W), and vice versa, maximizing power (73.38 W) minimizes stored energy (37.10 kJ). In the case of equal weighting for stored energy and power in the design (56.05 kJ and 38.89 W, respectively), a 31.5% decrease in energy and a 206.5% increase in power were observed compared to the scenario where energy is maximized. Additionally, a 44% decrease in power and a 51.1% increase in energy were noted compared to the case where power is maximized. These findings collectively highlight the robustness and effectiveness of the developed MLP model in accurately predicting melting time and providing optimal solutions for energy storage parameters.

References

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  • [8] Alva G, Lin Y, Fang G. An overview of thermal energy storage systems. Energy 2018; 144: 341–78.
  • [9] Kalidasan B, Pandey AK, Shahabuddin S, Samykano M, Thirugnanasambandam M, Saidur R. Phase change materials integrated solar thermal energy systems: Global trends and current practices in experimental approaches. Journal of Energy Storage 2020; 27: 101118.
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  • [22] Verma TN, Nashine P, Singh DV, Singh TS, Panwar D. ANN: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes. Applied Thermal Engineering 2017; 120: 219–27.
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  • [25] Ren G, Chuttar A, Banerjee D. Exploring efficacy of machine learning (artificial neural networks) for enhancing reliability of thermal energy storage platforms utilizing phase change materials. International Journal of Heat and Mass Transfer 2022; 189: 122628.
  • [26] Amudhalapalli GK, Devanuri JK. Prediction of transient melt fraction in metal foam-nanoparticle enhanced PCM hybrid shell and tube heat exchanger: A machine learning approach. Thermal Science and Engineering Progress 2023; 46: 102241.
  • [27] Krishnayatra G, Tokas S, Kumar R. Numerical heat transfer analysis & predicting thermal performance of fins for a novel heat exchanger using machine learning. Case Studies in Thermal Engineering 2020; 21: 100706.
  • [28] Hinton GE. Connectionist learning procedures. Artificial Intelligence 1989; 40: 185–234.
  • [29] Li Y, Huang X, Huang X, Gao X, Hu R, Yang X, et al. Machine learning and multilayer perceptron enhanced CFD approach for improving design on latent heat storage tank. Applied Energy 2023; 347: 121458.
  • [30] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 2011; 12: 2825–30.
  • [31] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Computat 2002; 6: 182–97.
  • [32] Blank J, Deb K. Pymoo: Multi-Objective Optimization in Python. IEEE Access 2020; 8: 89497–509.
Year 2024, , 199 - 218, 24.06.2024
https://doi.org/10.58559/ijes.1420875

Abstract

References

  • [1] Palacios A, Barreneche C, Navarro ME, Ding Y. Thermal energy storage technologies for concentrated solar power–A review from a materials perspective. Renewable Energy 2020; 156: 1244–65.
  • [2] Enescu D, Chicco G, Porumb R, Seritan G. Thermal energy storage for grid applications: Current status and emerging trends. Energies 2020; 13: 340.
  • [3] Saffari M, de Gracia A, Fernández C, Belusko M, Boer D, Cabeza LF. Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV. Applied Energy 2018; 211: 604–16.
  • [4] Yang T, Liu W, Kramer GJ, Sun Q. Seasonal thermal energy storage: A techno-economic literature review. Renewable and Sustainable Energy Reviews 2021; 139: 110732.
  • [5] Guelpa E, Verda V. Thermal energy storage in district heating and cooling systems: A review. Applied Energy 2019; 252: 113474.
  • [6] Liu M, Riahi S, Jacob R, Belusko M, Bruno F. Design of sensible and latent heat thermal energy storage systems for concentrated solar power plants: Thermal performance analysis. Renewable Energy 2020; 151: 1286–97.
  • [7] Koçak B, Fernandez AI, Paksoy H. Review on sensible thermal energy storage for industrial solar applications and sustainability aspects. Solar Energy 2020; 209: 135–69.
  • [8] Alva G, Lin Y, Fang G. An overview of thermal energy storage systems. Energy 2018; 144: 341–78.
  • [9] Kalidasan B, Pandey AK, Shahabuddin S, Samykano M, Thirugnanasambandam M, Saidur R. Phase change materials integrated solar thermal energy systems: Global trends and current practices in experimental approaches. Journal of Energy Storage 2020; 27: 101118.
  • [10] Zhang N, Yuan Y, Cao X, Du Y, Zhang Z, Gui Y. Latent heat thermal energy storage systems with solid–liquid phase change materials: a review. Advanced Engineering Materials 2018; 20: 1700753.
  • [11] Wang X, Li W, Luo Z, Wang K, Shah SP. A critical review on phase change materials (PCM) for sustainable and energy efficient building: Design, characteristic, performance and application. Energy and Buildings 2022; 260: 111923.
  • [12] Arshad A, Jabbal M, Sardari PT, Bashir MA, Faraji H, Yan Y. Transient simulation of finned heat sinks embedded with PCM for electronics cooling. Thermal Science and Engineering Progress 2020; 18: 100520.
  • [13] Höhlein S, König-Haagen A, Brüggemann D. Macro-encapsulation of inorganic phase-change materials (PCM) in metal capsules. Materials 2018; 11: 1752.
  • [14] Farid M, Kim Y, Honda T, Kanzawa A. The role of natural convection during melting and solidification of PCM in a vertical cylinder. Chemical Engineering Communications 1989; 84: 43–60.
  • [15] Shmueli H, Ziskind G, Letan R. Melting in a vertical cylindrical tube: Numerical investigation and comparison with experiments. International Journal of Heat and Mass Transfer 2010; 53: 4082–91.
  • [16] Bechiri M, Mansouri K. Study of heat and fluid flow during melting of PCM inside vertical cylindrical tube. International Journal of Thermal Sciences 2019; 135: 235–46.
  • [17] Ebadi S, Tasnim SH, Aliabadi AA, Mahmud S. Melting of nano-PCM inside a cylindrical thermal energy storage system: Numerical study with experimental verification. Energy Conversion and Management 2018; 166: 241–59.
  • [18] Pan C, Charles J, Vermaak N, Romero C, Neti S, Zheng Y, et al. Experimental, numerical and analytic study of unconstrained melting in a vertical cylinder with a focus on mushy region effects. International Journal of Heat and Mass Transfer 2018; 124: 1015–24.
  • [19] Jones BJ, Sun D, Krishnan S, Garimella SV. Experimental and numerical study of melting in a cylinder. International Journal of Heat and Mass Transfer 2006; 49: 2724–38.
  • [20] Fraiman L, Benisti E, Ziskind G, Letan R. Experimental Investigation of Melting in Vertical Circular Tubes, Haifa, Israel: ASMEDC; 2008, 193–8.
  • [21] Mallya N, Haussener S. Buoyancy-driven melting and solidification heat transfer analysis in encapsulated phase change materials. International Journal of Heat and Mass Transfer 2021; 164: 120525.
  • [22] Verma TN, Nashine P, Singh DV, Singh TS, Panwar D. ANN: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes. Applied Thermal Engineering 2017; 120: 219–27.
  • [23] Zhu S, Hrnjica B, Ptak M, Choiński A, Sivakumar B. Forecasting of water level in multiple temperate lakes using machine learning models. Journal of Hydrology 2020; 585: 124819.
  • [24] Jin W, Atkinson TA, Doughty C, Neupane G, Spycher N, McLing TL, et al. Machine-learning-assisted high- temperature reservoir thermal energy storage optimization. Renewable Energy 2022; 197: 384–97.
  • [25] Ren G, Chuttar A, Banerjee D. Exploring efficacy of machine learning (artificial neural networks) for enhancing reliability of thermal energy storage platforms utilizing phase change materials. International Journal of Heat and Mass Transfer 2022; 189: 122628.
  • [26] Amudhalapalli GK, Devanuri JK. Prediction of transient melt fraction in metal foam-nanoparticle enhanced PCM hybrid shell and tube heat exchanger: A machine learning approach. Thermal Science and Engineering Progress 2023; 46: 102241.
  • [27] Krishnayatra G, Tokas S, Kumar R. Numerical heat transfer analysis & predicting thermal performance of fins for a novel heat exchanger using machine learning. Case Studies in Thermal Engineering 2020; 21: 100706.
  • [28] Hinton GE. Connectionist learning procedures. Artificial Intelligence 1989; 40: 185–234.
  • [29] Li Y, Huang X, Huang X, Gao X, Hu R, Yang X, et al. Machine learning and multilayer perceptron enhanced CFD approach for improving design on latent heat storage tank. Applied Energy 2023; 347: 121458.
  • [30] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 2011; 12: 2825–30.
  • [31] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Computat 2002; 6: 182–97.
  • [32] Blank J, Deb K. Pymoo: Multi-Objective Optimization in Python. IEEE Access 2020; 8: 89497–509.
There are 32 citations in total.

Details

Primary Language English
Subjects Energy Generation, Conversion and Storage (Excl. Chemical and Electrical)
Journal Section Research Article
Authors

Burak İzgi 0000-0001-9491-8653

Publication Date June 24, 2024
Submission Date January 17, 2024
Acceptance Date June 5, 2024
Published in Issue Year 2024

Cite

APA İzgi, B. (2024). Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material. International Journal of Energy Studies, 9(2), 199-218. https://doi.org/10.58559/ijes.1420875
AMA İzgi B. Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material. Int J Energy Studies. June 2024;9(2):199-218. doi:10.58559/ijes.1420875
Chicago İzgi, Burak. “Machine Learning Predictions and Optimization for Thermal Energy Storage in Cylindrical Encapsulated Phase Change Material”. International Journal of Energy Studies 9, no. 2 (June 2024): 199-218. https://doi.org/10.58559/ijes.1420875.
EndNote İzgi B (June 1, 2024) Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material. International Journal of Energy Studies 9 2 199–218.
IEEE B. İzgi, “Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material”, Int J Energy Studies, vol. 9, no. 2, pp. 199–218, 2024, doi: 10.58559/ijes.1420875.
ISNAD İzgi, Burak. “Machine Learning Predictions and Optimization for Thermal Energy Storage in Cylindrical Encapsulated Phase Change Material”. International Journal of Energy Studies 9/2 (June 2024), 199-218. https://doi.org/10.58559/ijes.1420875.
JAMA İzgi B. Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material. Int J Energy Studies. 2024;9:199–218.
MLA İzgi, Burak. “Machine Learning Predictions and Optimization for Thermal Energy Storage in Cylindrical Encapsulated Phase Change Material”. International Journal of Energy Studies, vol. 9, no. 2, 2024, pp. 199-18, doi:10.58559/ijes.1420875.
Vancouver İzgi B. Machine learning predictions and optimization for thermal energy storage in cylindrical encapsulated phase change material. Int J Energy Studies. 2024;9(2):199-218.