A Method for Precisely Controlling Energy Consumption in a Variable-Air-Volume Terminal Fresh Air System
2025-06-05
1. Precise Monitoring at the Perception Layer
Real-time capture of environmental and equipment conditions via multi-dimensional sensors:
• Environmental parameters
Air Quality: Built-in CO₂ sensor at the unit's end (automatic airflow boost above 1000 ppm)
Temperature and Humidity: Linked to Fresh Air Valve Opening (Error < ±0.5°C)
• Equipment Status
The current sensor continuously monitors the fan coil unit load, identifying inefficient operation caused by "overpowering a small task."
Wind valve opening feedback closed-loop calibration (accuracy up to ±2%)
II. Intelligent Decision-Making Algorithms
| Control Strategy | Principle | Energy-saving performance |
|---|---|---|
| Dynamic Enthalpy Optimization | Calculate the enthalpy difference between fresh and return air, and automatically select the minimum-energy mixing ratio of 24. | Refrigeration season energy consumption down by 18% |
| Variable Static Pressure Control | Dynamically adjust the fan speed based on the end-valve opening (maintaining 70%-90%)—setting it to 9. | Fan energy consumption reduced by 30% |
| Demand Forecasting Control | Combine historical data with weather forecasts to adjust airflow 72 hours in advance. | Avoid exceeding peak electricity costs |
3. End-effector Optimization
• Precise airflow adjustment
Minimum airflow setpoint dynamically adjusted (reduced to 35% of the design value during low load conditions)
Multi-terminal collaborative control to prevent air leakage losses caused by localized over-supply.
• Hardware Upgrade
Pressure-independent damper (linear control of opening degree vs. airflow)
Low-resistance duct design (airflow velocity ≤ 3 m/s, drag reduction of 45%)
4. Avoiding System-Level Energy Efficiency Traps
• Design Phase
Avoid oversizing the terminal (as excessive actual load leads to frequent switching).
Air duct system airtightness test (leakage rate < 5%)
• Operation Phase
Regularly calibrate the sensor (CO₂ sensor drift correction)
The minimum static pressure value is dynamically reset according to the season (0.8 kPa in summer → 0.5 kPa in winter).
5. Application of Innovative Technologies
AI-based Fault Diagnosis: Learning from Abnormal Current/Wind Pressure Curves to Provide Early Warnings of Bearing Wear
Blockchain-based traceability: Carbon emission data is recorded on the blockchain in real time, meeting export certification requirements.
Digital Twin Optimization: Build a virtual system model to simulate and fine-tune parameters, reducing trial-and-error costs.
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