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Improving the Gordon-Ng Model and Analyzing Thermodynamic Parameters to Evaluate Performance in a Water-Cooled Centrifugal Chiller
Journal article   Open access   Peer reviewed

Improving the Gordon-Ng Model and Analyzing Thermodynamic Parameters to Evaluate Performance in a Water-Cooled Centrifugal Chiller

Blanca Foliaco, Antonio Bula and Peter Coombes
Energies (Basel), Vol.13(9), p.2135
2020
DOI: https://doi.org/10.3390/en13092135
url
https://doi.org/10.3390/en13092135View
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UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#7 Affordable and Clean Energy
#11 Sustainable Cities and Communities
#13 Climate Action

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Abstract

The Gordon-Ng models are tools that have been used to estimate and evaluate the performance of various types of chillers for several years. A 550 TR centrifugal chiller plant facility was available to collect data from July and September 2018. The authors propose rearranging variables of the traditional (GNU) model based on average electric consumption and through a thermodynamic analysis comparable to the original model. Furthermore, assumptions are validated. Then, by estimation of the parameters of the new model using least square fitting with field training data and comparing to the GNU model and Braun model (based on consumption), it was shown that the proposed model provides a better prediction in order to evaluate consumption of a centrifugal chiller in regular operation, by improving the coefficient of variation (CV), CV = 3.24% and R2 = 92.52% for a filtered sub-data. Through an algorithm built from steady-state cycle analysis, physical parameters (Sgen, Qleak,eq, R) were estimated to compare with the same parameters obtained by regression to check the influence of the interception term in the model. It was found that without an interception term, the estimated parameters achieve relative errors (ER) below 20%. Additional comparison between external and internal power prediction is shown, with CV = 3.57 % and mean relative error (MRE) of 2.7%, achieving better accuracy than GNU and Braun model.

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