ANN Modeling to Analyze the R404A Replacement with the Low GWP Alternative R449A in an Indirect Supermarket Refrigeration System

Morteza Ghanbarpour,Adrián Mota-Babiloni,Jörgen Rogstam,Rahmatollah Khodabandeh,Pavel Makhnatch,Bassam E. Badran

doi:10.3390/app112311333

Abstract

Artificial neural networks (ANNs) have been considered for assessing the potential of low GWP refrigerants in experimental setups. In this study, the capability of using R449A as a lower GWP replacement of R404A in different temperature levels of a supermarket refrigeration system is investigated through an ANN model trained using field measurements as input. The supermarket refrigeration was composed of two indirect expansion circuits operated at low and medium temperatures and external subcooling. The results predicted that R449A provides, on average, a higher 10% and 5% COP than R404A at low and medium temperatures, respectively. Moreover, the cooling capacity was almost similar with both refrigerants in both circuits. This study also revealed that the ANN model could be employed to accurately predict the energy performance of a commercial refrigeration system and provide a reasonable judgment about the capability of the alternative refrigerant to be retrofitted in the system. This is very important, especially when the measurement data comes from field measurements, in which values are obtained under variable operating conditions. Finally, the ANN results were used to compare the carbon footprint for both refrigerants. It was confirmed that this refrigerant replacement could reduce the emissions of supermarket refrigeration systems.

Highlights

Greenhouse gas (GHG) emissions into the atmosphere must be reduced to mitigate global warming
R404A and R507A have been refrigerants extended in supermarket refrigeration systems [4], but both refrigerants have very high global warming potential (GWP) values, 3943 and 3985, respectively
With a lower GWP value than R404A and still non-flammable and zero-ODP fluid, this refrigerant can perform as a drop-in/light retrofit replacement in supermarket refrigeration systems [8]

Summary

Introduction

Greenhouse gas (GHG) emissions into the atmosphere must be reduced to mitigate global warming. The ANN method was employed by Hosoz and Ertunc [13] to predict the energy parameters in a cascade vapour compression refrigeration system using R134a Their results revealed that the ANN had great potential to model the performance of the system reliably. Tong et al [14] developed an ANN model to study the performance of a refrigeration system Using this technique, they proposed a refrigeration system control method for saving energy when working in part-load conditions. Li et al [16] developed an ANN model to design a control strategy for indoor air temperature and humidity in a direct expansion air conditioning system They employed 169 sets of experimental data and modelled the performance of the system. These results are used to analyze the carbon footprint reduction caused by refrigerant replacement

Characteristics of R404A and R449A

Artificial Neural Networks