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.
Retrieval of water vapor profile in the mesosphere from satellite ozone and hydroxyl measurements by the basic dynamic model of mesospheric photochemical system
