HVAC Capacity Constraint Management via Gain Factors
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Solution Overview
Problem
HVAC systems face challenges in accurately determining and utilizing actual capacity limits of devices under varying operating conditions, leading to suboptimal performance and energy inefficiency due to design capacity limits being inaccurate under different operating conditions.
Innovation Solution
An adaptive capacity constraint management system that uses measured thermodynamic properties to calculate a gain factor, updating the design capacity limits to determine actual capacity limits, and optimizing the selection of HVAC devices to satisfy thermal energy loads efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If design capacity limits are used to determine HVAC device capacity, then the system can operate with simple manufacturer specifications, but the capacity limits become inaccurate under actual operating conditions that differ from design conditions
Solution Approach 1:
The patent implements dynamic adjustment of capacity limits based on real-time operating conditions. The system continuously monitors actual operating parameters (temperature, pressure, load conditions) and adjusts the capacity limit dynamically using gain factors, transitioning from static design capacity limits to adaptive actual capacity limits that reflect current system performance
Solution Approach 2:
The system changes the capacity limit parameter from a fixed design value to a variable actual value by applying gain factors derived from thermodynamic property ratios. The capacity limit is updated as: actual capacity limit = design capacity limit × gain factor, where the gain factor accounts for deviations in operating conditions from design conditions
2Ease of operation
If design capacity limits are used for device selection, then the selection process is straightforward, but the utilization of HVAC devices becomes suboptimal
Solution Approach 1:
The system incorporates feedback loops that continuously monitor operating conditions and update capacity limits accordingly. The optimization routine uses updated actual capacity limits to make informed device selection decisions, creating a closed-loop system that improves productivity by selecting the most efficient device combinations based on real-time performance data
3Reliability
If multiple HVAC devices are activated to satisfy loads exceeding design capacity limits, then the thermal energy load can be satisfied, but power consumption increases
Solution Approach 1:
The system performs preliminary calculation of actual capacity limits before device selection and activation. By determining the true capacity of each device under current operating conditions in advance, the optimization routine can make accurate decisions about device activation, avoiding unnecessary activation of additional devices and thereby reducing power consumption while ensuring load satisfaction
Data Source
AI summary
An adaptive capacity constraint management system receives a measured value affected by HVAC equipment at actual operating conditions and uses the measured value to determine an operating value for a variable that affects a capacity of the HVAC equipment at the actual operating condition. The system uses the operating value to calculate a gain factor for the variable relative to design conditions and uses the calculated gain factor to determine a capacity gain for the HVAC equipment relative to the design conditions. The system applies the capacity gain to a design capacity limit for the HVAC equipment to determine a new capacity limit for the HVAC equipment at the actual operating conditions. The system may use the new capacity limit as a constraint in an optimization routine that that selects one or more devices of the HVAC equipment to satisfy a load setpoint.