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A new methodology to determine the pre-setting of the control valve in a heating installation. A general model

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  • Muniak, Damian Piotr

Abstract

This paper presents and discusses a new methodology to determine the pre-setting of the radiator and balancing control valve, as one of the basic parameters in the process of the heating installation hydraulic balancing. Example calculations are also made for selected control valves using the proposed and alternative methodologies, including the methodology generally accepted and used in practice. A comparison of the results is presented. It is shown that the proposed methodology gives results consistent with experimental data. It is also more accurate and versatile than the other methodologies discussed in the paper.

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  • Muniak, Damian Piotr, 2014. "A new methodology to determine the pre-setting of the control valve in a heating installation. A general model," Applied Energy, Elsevier, vol. 135(C), pages 35-42.
    • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:35-42
    DOI: 10.1016/j.apenergy.2014.08.064
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Weker, P. & Mineur, J. M., 1980. "A performance index for thermostatic radiator valves," Applied Energy, Elsevier, vol. 6(3), pages 203-215, May.
    3. Gustafsson, Jonas & Delsing, Jerker & van Deventer, Jan, 2010. "Improved district heating substation efficiency with a new control strategy," Applied Energy, Elsevier, vol. 87(6), pages 1996-2004, June.
    4. Gustafsson, Jonas & Delsing, Jerker & van Deventer, Jan, 2011. "Experimental evaluation of radiator control based on primary supply temperature for district heating substations," Applied Energy, Elsevier, vol. 88(12), pages 4945-4951.
    5. Liu, Lanbin & Fu, Lin & Jiang, Yi & Guo, Shan, 2011. "Major issues and solutions in the heat-metering reform in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 673-680, January.
    6. Yan, Aibin & Zhao, Jun & An, Qingsong & Zhao, Yulong & Li, Hailong & Huang, Yrjö Jun, 2013. "Hydraulic performance of a new district heating systems with distributed variable speed pumps," Applied Energy, Elsevier, vol. 112(C), pages 876-885.
    7. Fernández-Seara, José & Uhía, Francisco J. & Pardiñas, Ángel Á. & Bastos, Santiago, 2013. "Experimental analysis of an on demand external domestic hot water production system using four control strategies," Applied Energy, Elsevier, vol. 103(C), pages 85-96.
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