A comparison of empirically based steady-state models for vapor-compression liquid chillers

This paper presents a comprehensive comparison of empirically based models for steady-state modeling of vapor-compression liquid chillers. Next to the considered models already proposed in the open literature, i.e. regression, thermodynamic, and a radial basis function neural network model, a multilayer perceptron neural network model is introduced. The models predict the coefficient of performance by only using input variables that are readily known to the operating engineer. They are applied to two different chillers operating at the University of Auckland, New Zealand. The comparison demonstrates that neural networks show higher generalization abilities and at least equal forecast results compared to the regression models. Procedures are presented that make models without any physical meaning in the parameters possible to be used in fault detection and diagnosis. It is inferred that black-box models, in particular the radial basis function neural network model, may be preferred for predicting a chiller's performance in these purposes.