Methods for estimating refrigerant charge for HVACR systems
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Solution Overview
Problem
Existing HVACR systems face challenges in accurately predicting refrigerant charge, leading to potential false alarms of 'Loss of Charge' even when the system is near 100% charged, which affects accuracy and reliability.
Innovation Solution
A method using regression analysis to determine predictive parameters based on system parameters such as suction superheat, mass flow, expansion device opening, and discharge temperature, establishing a predictive model to estimate refrigerant charge directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If indirect prediction methods using superheat or sub-cooling are used, then the system can monitor refrigerant charge, but false alarms occur and prediction accuracy deteriorates
Solution Approach 1:
The patent changes the parameters used for prediction from indirect thermal parameters (superheat, sub-cooling) to direct operational parameters (mass flow, expansion device opening, saturated temperatures). This parameter transformation enables direct calculation of refrigerant charge status, eliminating false alarms while maintaining monitoring reliability.
Solution Approach 2:
The patent replaces indirect thermal field measurement with direct mass flow and operational parameter measurement. By using mass flow meters and expansion device position sensors instead of relying solely on temperature differential measurements, the system achieves more accurate and reliable refrigerant charge prediction.
2Ease of operation
If indirect prediction methods are used, then monitoring can be implemented, but false alarms of 'Loss of Charge' occur even when charged to 100%
Solution Approach 1:
The system uses its own operational parameters (mass flow, expansion device opening, saturated temperatures) that are already being measured for control purposes to directly calculate refrigerant charge status. This self-service approach eliminates the need for separate monitoring infrastructure while improving reliability by using accurate, directly-measured parameters.
3Measurement precision
If regression analysis with multiple parameters is conducted, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and isolates the three most critical parameters (mass flow, expansion device opening, saturated temperatures) from the full set of available system parameters. By focusing regression analysis on these key parameters with the highest correlation to refrigerant charge, the model achieves high accuracy while maintaining computational efficiency and manageable complexity.
Data Source
AI summary
A method for estimating refrigerant charge for an HVACR system is provided. The method includes obtaining one or more system parameters during operation. The one or more system parameters include at least one of compressor suction superheat, system mass flow, expansion device mass flow or opening degree, compressor suction saturated temperature, and compressor discharge saturated temperature. The method also includes conducting a regression analysis on the one or more system parameters to determine one or more predictive parameters for estimating the refrigerant charge. The method further includes determining a predictive model based on regression analysis. The predictive model establishes a relationship between the refrigerant charge and the one or more predictive parameters. Also the method includes estimating the refrigerant charge based on the predictive model.