Variable Air Volume Modeling for HVAC Fan and Zone Optimization
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Solution Overview
Problem
Existing HVAC systems face inefficiencies due to poor design, wear, and complex interactions between air-handling units, which traditional rule-based control methods fail to optimize, leading to suboptimal operation and lack of identification of operational issues.
Innovation Solution
A remote server uses a heuristic model to optimize air handling in HVAC systems by analyzing measurements from sensors, including fan speed, pressure, and power input, to determine optimal settings and predict future needs, identify problems, and suggest improvements, employing both physics-based and machine-learning approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rule-based control is used for air-handling units, then the system is simple to operate, but the system cannot identify improper operation or optimize complex interactions between units
Solution Approach 1:
A cloud-based server acts as an intermediary between air-handling units and local controllers. The server receives operational data from multiple units, performs complex analytics and optimization calculations, then transmits optimized control settings back to the units. This allows sophisticated optimization without increasing local system complexity.
Solution Approach 2:
The patent replaces traditional mechanical rule-based control systems with a data-driven analytics platform using machine learning and statistical models. This substitution enables the system to identify improper operations and optimize complex interactions that rule-based systems cannot detect.
2Area of stationary object
If multiple interconnected air-handling units are deployed to handle complex air distribution, then the system can serve larger areas, but the interactions between units become difficult to control and optimize
Solution Approach 1:
The system segments the complex multi-unit control problem into individual unit analyses while accounting for interactions. Each air-handling unit's performance is evaluated separately using analytics, allowing the system to manage complexity by breaking down the overall system into manageable components that can be optimized individually and collectively.
Solution Approach 2:
The cloud-based analytics platform serves multiple air-handling units simultaneously, providing a universal control solution that handles complex interactions across the entire system. This multi-functional approach allows the same analytical engine to optimize numerous interconnected units without requiring separate control systems for each.
3Device complexity
If traditional control methods are used, then the system operates with simple control logic, but energy efficiency and operational performance are suboptimal
Solution Approach 1:
The system implements continuous feedback loops where operational data from sensors on air-handling units is constantly monitored, analyzed, and used to adjust control settings. This feedback mechanism enables real-time optimization of energy efficiency while maintaining simple local control logic through cloud-based processing.
Solution Approach 2:
The analytics platform dynamically changes operational parameters such as fan speeds, damper positions, and setpoint temperatures based on real-time analysis of system performance and external conditions. These parameter adjustments optimize energy efficiency without requiring complex local control logic, as calculations are performed remotely.
Data Source
AI summary
Methods for modeling heating, ventilation, and air conditioning (HVAC) are described. A heuristic model of air handling in a HVAC system is optimized by a server based on measurements from outdoor sensors, from building space sensors, plant sensors, and sensors for the air handling. Settings of the air handling in the HVAC system are determined by the server from the model as optimized and transmitted to the HVAC system. The heuristic model may be optimized by indicating a mismatch of a fan with a space based on a diversity from a fan flow set point and a fan designed maximum flow; identifying a rogue zone or a critical zone of air handling units based on a number of actuator re-positioning, box pressures, and box flow; and/or determining a coupling of a coupled zone operation with temperature, flow, or temperature and flow.