Outdoor Air Damper Control Using Extremum Seeking States
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Solution Overview
Problem
Existing HVAC systems face inefficiencies in regulating outdoor air intake due to inaccurate humidity sensing and the need for reference values, leading to suboptimal performance, especially under dynamic conditions.
Innovation Solution
A system and method that utilize a finite state machine controller to monitor and compare heating, damper, and cooling signals with outdoor air temperature and humidity, transitioning between states to optimize outdoor air intake and minimize mechanical cooling load, incorporating extremum seeking control to dynamically adjust damper positions for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional humidity sensing and reference value-based control are used, then the system can regulate outdoor air intake, but the accuracy and reliability of control deteriorates due to sensor inaccuracies and dynamic condition variations
Solution Approach 1:
The system uses extremum seeking control to autonomously identify and track the optimal outdoor air fraction without requiring manual calibration or reference values. The controller automatically adjusts dampers to maximize the cooling effect of outdoor air, eliminating the need for accurate humidity sensing and predefined reference values while maintaining reliable control under dynamic conditions.
Solution Approach 2:
The control system dynamically changes the outdoor air fraction parameter based on real-time thermal conditions rather than relying on fixed reference values or inaccurate sensor readings. The extremum seeking algorithm continuously optimizes this parameter to achieve maximum cooling effectiveness, adapting to changing environmental conditions without requiring precise measurement inputs.
2Use of energy by moving object
If maximum outdoor air intake is used to reduce mechanical cooling load, then energy consumption decreases, but the mechanical cooling capacity becomes insufficient when outdoor conditions are unfavorable
Solution Approach 1:
The system dynamically adjusts the outdoor air fraction based on real-time thermal conditions rather than operating at a fixed maximum or minimum setting. The extremum seeking control continuously optimizes the balance between free cooling utilization and mechanical cooling availability, ensuring that the system can reliably meet cooling demands while minimizing energy consumption under varying outdoor conditions.
Solution Approach 2:
The control system uses feedback from thermal sensors to continuously monitor the actual cooling effect achieved and adjusts the outdoor air fraction accordingly. This closed-loop control ensures that the system maintains reliable cooling capacity by reducing outdoor air intake when conditions are unfavorable and increasing it when conditions are optimal, rather than relying on open-loop maximum intake strategies.
3Productivity
If extremum seeking control is implemented to dynamically optimize damper positions, then system performance improves, but the control algorithm complexity increases
Solution Approach 1:
The system replaces complex mechanical calibration and reference value setup procedures with a software-based extremum seeking control algorithm. This substitution eliminates the need for manual system configuration and complex mechanical adjustment mechanisms, achieving high performance through adaptive software control that automatically optimizes damper positions based on real-time thermal feedback.
Data Source
AI summary
A computerized method for operating a process includes using at least one sensor to determine when to transition a state machine between a first operating state wherein the process is operated using extremum seeking control and a second operating state wherein the process is operated without extremum seeking control. The method further includes, in response to the determination, automatically transitioning from the first operating state to the second operating state.