Methods for estimating refrigerant charge for HVACR systems
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Solution Overview
Problem
HVACR systems face inaccuracies in predicting refrigerant charge, leading to false 'Loss of Charge' alarms and increased maintenance costs due to the lack of precise refrigerant level estimation.
Innovation Solution
A method using regression analysis to determine predictive parameters from system parameters like suction superheat, mass flow, and expansion device opening, establishing a predictive model to directly estimate refrigerant charge, thereby improving prediction accuracy and reducing false alarms.
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 transforms the prediction approach by changing from indirect single-parameter methods (superheat/sub-cooling) to direct multi-parameter regression analysis. The system uses multiple system parameters including suction superheat, mass flow, expansion device opening, suction saturated temperature, and discharge saturated temperature to directly predict refrigerant charge levels, improving both reliability and precision simultaneously
Solution Approach 2:
The patent creates a composite predictive model that combines multiple system parameters into a unified refrigerant charge estimation. Rather than relying on a single parameter, the system integrates data from sensors measuring different aspects of system operation to form a composite indicator of refrigerant charge status
2Ease of operation
If indirect prediction methods are used, then monitoring can be implemented, but false 'Loss of Charge' alarms increase maintenance costs
Solution Approach 1:
The patent implements a feedback mechanism where the predictive model continuously monitors system parameters and compares predicted refrigerant charge levels against threshold values. The system provides feedback through maintenance cost savings and reduced false alarms, allowing operators to trust the monitoring system and take appropriate action only when genuinely needed
Solution Approach 2:
The patent replaces manual refrigerant charge checking and false alarm investigation with an automated electronic monitoring and prediction system. The regression-based predictive model automatically analyzes sensor data and generates refrigerant charge estimates, eliminating the need for manual intervention and reducing false alarm responses
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.