Refrigerant Charge Prediction of Vapor Compression Air Conditioner Based on Start-Up Characteristics

Yechan Yun,Young Soo Chang

doi:10.3390/app11041780

Abstract

Refrigerant charge faults, which occur frequently, increase the energy loss and may fatally damage the system. Refrigerant leakage is difficult to detect and diagnose until the fault has reached a severe degree. Various techniques have been developed to predict the refrigerant charge amount based on steady-state operation; however, steady-state experiments used to develop prediction models for the refrigerant charge amount are expensive and time-consuming. In this study, a prediction model was established with dynamic experimental data to overcome these deficiencies. The dynamic models for the condensation temperature, degree of subcooling, compressor discharge temperature, and power consumption were developed with a regression support vector machine (r-SVM) model and start-up experimental data. The dynamic models for the condensation temperature and degree of subcooling can predict the distinct start-up characteristics depending on the refrigerant charge amount. Moreover, the estimated root mean square error (RMSE) of the condensation temperature and degree of subcooling of the test data are 0.53 and 0.84 °C, respectively. The refrigerant charge is one of the predictors that defines the dynamic characteristics. The refrigerant charge can be estimated by minimizing the RMSE of the predicted values of the dynamic models and experimental data. When the dynamic characteristics of the two predictor variables, “condensation temperature” and “degree of subcooling” are used together, the average prediction error of the test data is 2.54%. The proposed method, which uses the dynamic model during start-up operation, is an effective technique for predicting the refrigerant charge amount.

Highlights

IntroductionIn the United States in 2015, approximately 40% of the total consumed energy was used in commercial and residential buildings [1]
In response to global warming and climate change, governments worldwide have implemented related policies and technical efforts that include the use of renewable energies and the increase in the energy efficiency to reduce energy consumption and greenhouse gas (GHG) emissions.In the United States in 2015, approximately 40% of the total consumed energy was used in commercial and residential buildings [1]
The refrigerant charge amounts can be obtained by minimizing the root mean square error (RMSE) of the response values and test data for each response variable

Summary

Introduction

In the United States in 2015, approximately 40% of the total consumed energy was used in commercial and residential buildings [1]. 41% of the energy consumed in the building sector was used for heating and cooling, and between 15% and 30% was wasted owing to poor maintenance or inadequate control [2]. To operate and maintain air conditioning systems efficiently, the energy efficiency should be maximized through optimal control and with fault diagnosis technology. Faults in air conditioning systems can be classified into hard and soft faults. Hard faults that lead to system halt can be detected and diagnosed. Soft faults such as refrigerant leakage and heat exchanger fouling are difficult to detect and diagnose before the faults reaches a severe degree; this can lead to energy loss and a damaged system

Methods

Results

Conclusion