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.
Highlights
Nowadays, there is a great effort dedicated to improving heating, ventilation and air conditioning (HVAC) equipment efficiency, especially in chillers, which are equipment used extensively in buildings and industrial air conditioning installations
This paper aims to perform a rearranged Fundamental Gordon–Ng model, based on power consumption instead of coefficient of performance (COP)
According to a simple thermodynamic analysis, a steady-state model was built based on easy measured variables and three physical parameters which can be obtained by regression
Summary
There is a great effort dedicated to improving heating, ventilation and air conditioning (HVAC) equipment efficiency, especially in chillers, which are equipment used extensively in buildings and industrial air conditioning installations. It is crucial to evaluate their performance continually for optimization processes and failure prediction This situation creates a necessity to generate more accurate models that allow simulation to predict their behavior. Engineering models are classified into three categories: black-box, grey-box, and physical or mechanistic models [1,2,3,4,5]. These can be steady-state or dynamic models. The former has the disadvantage that only applies to the data range used to fit model parameters. That makes the analysis of a process very hard when it is not possible to obtain manufacturer data
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