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Inverse neural network based control strategy for absorption chillers

Author

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  • Labus, J.
  • Hernández, J.A.
  • Bruno, J.C.
  • Coronas, A.

Abstract

This paper proposes a novel, model-based control strategy for absorption cooling systems. First, a small-scale absorption chiller was modelled using artificial neural networks (ANNs). This model takes into account inlet and outlet temperatures as well as the flow rates of the external water circuits. The configuration 9–6–2 (9 inputs, 6 hidden and 2 output neurons) showed excellent agreement between the prediction and the experimental data (R2>0.99 and RMSE<0.05%). This type of ANN model is used to explain the behaviour of the system when operating conditions are measured and these measurements are available. A control strategy was also developed by using the inverse artificial neural network (ANNi) method. For a particular output (cooling load) the ANNi calculates the optimal unknown parameter(s) (controlling temperatures and flow rates). An optimization method was used to fit the unknown parameters of the ANNi method. The very low percentage of error and short computing time make this methodology suitable for the on-line control of absorption cooling systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:39:y:2012:i:1:p:471-482
    DOI: 10.1016/j.renene.2011.08.036
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    References listed on IDEAS

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    1. Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    2. Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
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    5. Rosiek, S. & Batlles, F.J., 2010. "Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network," Renewable Energy, Elsevier, vol. 35(12), pages 2894-2901.
    6. Hernández, J.A. & Bassam, A. & Siqueiros, J. & Juárez-Romero, D., 2009. "Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse," Renewable Energy, Elsevier, vol. 34(4), pages 1084-1091.
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    1. Zhenchang Liu & Aiguo Wu & Haitang Wen, 2024. "Research on Energy-Saving Control Strategies for Single-Effect Absorption Refrigeration Systems," Energies, MDPI, vol. 17(18), pages 1-25, September.
    2. Lubis, Arnas & Jeong, Jongsoo & Giannetti, Niccolo & Yamaguchi, Seiichi & Saito, Kiyoshi & Yabase, Hajime & Alhamid, Muhammad I. & Nasruddin,, 2018. "Operation performance enhancement of single-double-effect absorption chiller," Applied Energy, Elsevier, vol. 219(C), pages 299-311.
    3. Cabrera, F.J. & Fernández-García, A. & Silva, R.M.P. & Pérez-García, M., 2013. "Use of parabolic trough solar collectors for solar refrigeration and air-conditioning applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 103-118.
    4. 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.
    5. 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.
    6. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.

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