Ventilation System Flow Balancing Using Neural Network Motor Control
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Solution Overview
Problem
Ventilation systems, particularly air exchangers, face challenges in maintaining balanced flow rates due to varying installation conditions and environmental changes, leading to performance degradation, energy inefficiencies, and non-compliance with building codes.
Innovation Solution
A ventilation system with automatic flow balancing derived from a neural network that adjusts blower motor power based on air path parameters, motor speed, and current, eliminating the need for pressure sensors and manual balancing procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual balancing procedures are used to adjust flow rates, then installation precision can be improved, but installation time and complexity increase significantly
Solution Approach 1:
The system performs automatic flow balancing through self-learning algorithms that continuously monitor and adjust damper positions and blower speeds without requiring manual intervention. The controller autonomously optimizes flow rates by processing sensor data and adjusting system parameters, eliminating the need for installer expertise and manual balancing procedures.
Solution Approach 2:
The system pre-configures flow rate setpoints and damper positions based on building specifications and occupancy patterns before operation begins. By pre-calculating optimal operating parameters and storing them in memory, the system eliminates the need for time-consuming on-site balancing adjustments during installation.
2Measurement precision
If pressure sensors are installed to monitor flow rates, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The system uses intermediate parameters such as blower motor current, voltage, and speed as proxies to infer flow rates without directly measuring pressure or flow. By monitoring electrical parameters and using pre-established relationships between motor operation and airflow, the system achieves accurate flow estimation without requiring pressure sensors or flow meters.
Solution Approach 2:
The system replaces mechanical pressure sensors with electronic monitoring of motor parameters. By using electrical measurements (current, voltage, frequency) and control signals to determine flow conditions, the system eliminates mechanical sensing components while maintaining measurement capability through software-based calculations.
3Reliability
If the system adapts to varying environmental conditions, then system reliability improves, but control complexity increases
Solution Approach 1:
The system dynamically adjusts damper positions and blower speeds in real-time based on changing environmental conditions such as temperature, humidity, and occupancy. The controller continuously monitors sensor inputs and modifies operating parameters to maintain optimal flow rates, transitioning from static pre-configured settings to adaptive dynamic control.
Solution Approach 2:
The system implements closed-loop feedback control by continuously monitoring actual flow rates through sensor data and comparing them against target setpoints. The controller processes this feedback information and automatically adjusts damper positions and motor speeds to correct deviations, ensuring consistent performance despite environmental variations.
4Measurement precision
If manual balancing adjustments are made, then ease of operation is reduced, but measurement precision can be maintained
Solution Approach 1:
The system automatically verifies and adjusts flow rates without requiring installer intervention. The controller monitors sensor data, compares actual performance against specifications, and autonomously makes adjustments to achieve balanced operation, eliminating the need for installers to perform manual verification and adjustment procedures.
Solution Approach 2:
The system pre-loads flow rate setpoints and performance criteria into memory during manufacturing based on building specifications. This preliminary configuration enables automatic verification and adjustment during operation without requiring installers to manually input parameters or perform complex balancing calculations on-site.
Data Source
AI summary
A ventilation system with automatic flow balancing derived from a neural network to consistently achieve a desired flow rate for inlet flow and/or outlet flow in various operating environments to optimize system performance. The system includes a ventilation device that includes an exhaust blower assembly with a blower motor and a control circuit having a mathematical equation derived from the use of the neural network. When the estimated exhaust blower flow is different than an exhaust flow set point, the exhaust control circuit selectively alters power supplied to the exhaust motor.