Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 99 | Predictive Indoor Ventilation Control Using Air Quality Estimation |
Arka Kolay Gulnaaz Sayyad Noah Rockoff |
Hossein Ataee | design_document1.pdf final_paper1.pdf proposal1.pdf video |
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| Team Members: Gulnaaz Sayyad (gsayy2), Noah Rockoff (noahlr2), Arkaprabha Kolay (akolay2) Problem: Indoor air quality is often poorly managed in homes, classrooms, and office spaces because harmful conditions such as elevated CO2, PM2.5, and humidity are not immediately noticeable to occupants. Poor ventilation can lead to fatigue, reduced concentration, and health issues. Most existing ventilation systems operate on fixed schedules or require manual control, which means they do not respond dynamically to changing air quality conditions. This results in either insufficient ventilation that harms occupant health or excessive ventilation that wastes energy. Solution: This project proposes an indoor air quality monitoring and ventilation control system that continuously measures CO2, PM2.5, temperature, and humidity. Based on real-time sensor data, control algorithms automatically activate ventilation mechanisms such as fans using predictive, model-based control algorithms to proactively regulate ventilation before air quality thresholds are exceeded. The system will incorporate a simplified physical model of indoor CO2 dynamics to estimate future air quality trends and inform ventilation decisions. The system also includes a software dashboard that displays current conditions and stores air quality data. These will allow users to track trends over time while maintaining a healthier indoor environment. Solution Components: Air Quality Sensor Sensors to continuously monitor indoor environmental quality CO₂, temperature, and humidity sensor (Sensirion SCD40, I²C) PM1006K Low Cost PM2.5 Sensor Microcontroller Processes sensor data Executes predictive ventilation control algorithms Logs air quality data for analysis Ventilation Subsystem Fan controlled using PWM MOSFET driver circuit implemented on custom PCB Will run based on the data collected from the sensors Software dashboard Displays live air quality data Potentially send alerts Used for system validation and performance evaluation Buy SCD40 CO2, Temperature and Humidity Sensor Breakout I2C at Best Price | 7semi Criterion for Success: To validate system performance, controlled experiments will be conducted to create repeatable indoor air quality disturbances. For example, candles or small flames will be used near the CO₂ sensor to artificially increase CO₂ concentration, allowing verification of sensor response and system behavior. These disturbances will be used to evaluate both a baseline threshold-based controller and the proposed predictive control strategy. Ventilation activation and system response will be observed and logged to compare control approaches under identical conditions. The project will be considered successful if the following measurable performance criteria are met: The system predicts CO₂ threshold crossings within ±X minutes using the internal air quality model. Indoor CO₂ concentration is maintained below a specified ppm value for at least a majority of occupied operation time. Compared to a baseline threshold-based controller, the predictive control strategy reduces ventilation fan runtime or estimated energy usage by at least a baseline percentage. The system operates continuously without unintended resets or sensor failures during fan actuation and environmental changes. Controlled experiments (e.g., candle-based CO₂ disturbances) demonstrate repeatable and observable differences between predictive and threshold-based control behavior. |
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