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Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network

Author

Listed:
  • Tomasz Halon

    (Faculty of Mechanical and Power Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

  • Ewa Pelinska-Olko

    (Faculty of Mechanical and Power Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

  • Malgorzata Szyc

    (Fortum Power and Heat Polska, ul. Antoniego Slonimskiego 1A, 50-304 Wroclaw, Poland)

  • Bartosz Zajaczkowski

    (Faculty of Mechanical and Power Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

Abstract

In this paper, the feasibility of a multi-layer artificial neural network to predict both the cooling capacity and the COP of an adsorption chiller working in a real pilot plant is presented. The ANN was trained to accurately predict the performance of the device using data acquired over several years of operation. The number of neurons used by the ANN should be selected individually depending on the size of the training base. The optimal number of datasets in a training base is suggested to be 35. The predicted cooling capacity curves for a given adsorption chiller driven by the district heating are presented. Predictions of the artificial neural network used show good correlation with experimental results, with the mean relative deviation as low as 1.36%. The character of the cooling capacity curve is physically accurate, and during normal operation for cooling capacities ≥8 kW, the errors rarely exceed 1%.

Suggested Citation

  • Tomasz Halon & Ewa Pelinska-Olko & Malgorzata Szyc & Bartosz Zajaczkowski, 2019. "Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network," Energies, MDPI, vol. 12(17), pages 1-11, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3328-:d:261945
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    References listed on IDEAS

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    1. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    2. Hou, Zhijian & Lian, Zhiwei & Yao, Ye & Yuan, Xinjian, 2006. "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique," Applied Energy, Elsevier, vol. 83(9), pages 1033-1046, September.
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Teng, W.S. & Leong, K.C. & Chakraborty, A., 2016. "Revisiting adsorption cooling cycle from mathematical modelling to system development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 315-332.
    5. Ommen, Torben & Markussen, Wiebke Brix & Elmegaard, Brian, 2016. "Lowering district heating temperatures – Impact to system performance in current and future Danish energy scenarios," Energy, Elsevier, vol. 94(C), pages 273-291.
    6. Lazrak, Amine & Boudehenn, François & Bonnot, Sylvain & Fraisse, Gilles & Leconte, Antoine & Papillon, Philippe & Souyri, Bernard, 2016. "Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation," Renewable Energy, Elsevier, vol. 86(C), pages 1009-1022.
    7. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    8. Labus, J. & Hernández, J.A. & Bruno, J.C. & Coronas, A., 2012. "Inverse neural network based control strategy for absorption chillers," Renewable Energy, Elsevier, vol. 39(1), pages 471-482.
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    Cited by:

    1. Maciej Chorowski & Piotr Pyrka & Zbigniew Rogala & Piotr Czupryński, 2019. "Experimental Study of Performance Improvement of 3-Bed and 2-Evaporator Adsorption Chiller by Control Optimization," Energies, MDPI, vol. 12(20), pages 1-17, October.
    2. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    3. Bartlomiej Nalepa & Tomasz Halon, 2021. "Recommendations for Running a Tandem of Adsorption Chillers Connected in Series and Powered by Low-Temperature Heat from District Heating Network," Energies, MDPI, vol. 14(16), pages 1-17, August.
    4. Miguel A. Jaramillo-Morán & Agustín García-García, 2019. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors," Energies, MDPI, vol. 12(23), pages 1-18, November.
    5. Blanca Foliaco & Antonio Bula & Peter Coombes, 2020. "Improving the Gordon-Ng Model and Analyzing Thermodynamic Parameters to Evaluate Performance in a Water-Cooled Centrifugal Chiller," Energies, MDPI, vol. 13(9), pages 1-20, April.
    6. Hossein Moayedi & Amir Mosavi, 2021. "Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings," Energies, MDPI, vol. 14(6), pages 1-19, March.

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