Smart ventilation for air quality control
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Solution Overview
Problem
Traditional ventilation methods for enclosed spaces are ineffective when outdoor air quality is lower than indoor air quality, leading to potential health risks and inefficient energy use, as they often rely on human intervention and mechanical systems that can import pollutants and consume significant energy.
Innovation Solution
A processor-based system that collects and analyzes real-time and forecasted air quality data from external and internal sources, using optimization criteria to determine an air exchange plan that optimizes ventilation timing and method to minimize energy waste and ensure safe air quality, leveraging IoT sensors, weather data, and AI for efficient air exchange between enclosed spaces and the outdoors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional mechanical ventilation systems are used to exchange air in enclosed spaces, then air exchange can occur, but energy consumption increases significantly
Solution Approach 1:
The system performs preliminary analysis of outdoor air quality data and indoor air quality conditions before initiating ventilation. By forecasting air quality trends and comparing them in advance, the system determines optimal ventilation timing, avoiding unnecessary mechanical ventilation when outdoor air quality is poor, thus reducing energy consumption while maintaining effective air exchange when needed
Solution Approach 2:
The system continuously monitors both outdoor air quality forecasts and indoor air quality sensor data, using this feedback to dynamically adjust ventilation decisions. The optimization algorithm processes real-time indoor conditions against predicted outdoor conditions, creating a closed-loop control system that minimizes energy consumption while ensuring air quality requirements are met
2Productivity
If traditional ventilation methods are used when outdoor air quality is lower than indoor air quality, then air exchange occurs, but pollutants are imported into the enclosed space
Solution Approach 1:
The system performs preliminary assessment of outdoor air quality forecasts before executing ventilation. By analyzing predicted air quality indices and comparing them with current indoor conditions in advance, the system proactively prevents pollutant import by avoiding ventilation operations when outdoor air quality is deteriorated, while ensuring effective air exchange when outdoor air is cleaner than indoor air
Solution Approach 2:
The system applies preliminary anti-action by preventing ventilation operations when forecasted outdoor air quality indicates high pollutant levels. The optimization algorithm identifies conditions where ventilation would be harmful and blocks these operations in advance, counteracting the potential harm of pollutant import before it can occur
3Device complexity
If manual intervention is used for ventilation control, then simple systems are used, but air quality optimization is insufficient
Solution Approach 1:
The system implements self-service by automatically monitoring outdoor air quality forecasts, analyzing indoor air quality sensor data, and executing ventilation control decisions without manual intervention. The optimization algorithm autonomously processes data from multiple sources and controls ventilation systems, eliminating the need for human decision-making while ensuring reliable air quality optimization through consistent application of optimization criteria
4Reliability
If mechanical ventilation systems operate continuously, then air quality can be maintained, but energy waste increases
Solution Approach 1:
The system implements periodic action by scheduling ventilation operations based on forecasted outdoor air quality patterns and actual indoor air quality conditions. Rather than continuous operation, ventilation is activated only during periods when outdoor air quality is favorable and indoor conditions require exchange, creating periodic on-demand ventilation cycles that maintain air quality while minimizing energy waste from unnecessary continuous operation
Solution Approach 2:
The system changes operational parameters dynamically by adjusting ventilation timing, duration, and intensity based on real-time indoor air quality measurements and forecasted outdoor conditions. The optimization algorithm modifies ventilation parameters to match actual needs, reducing energy consumption by operating at lower intensities or shorter durations when full mechanical ventilation is not required, while maintaining air quality standards
Data Source
AI summary
A processor may control air quality in an enclosed space. A processor may receive an external air condition index dataset associated with a geographical location. A processor may receive an internal air condition index dataset from one or more data collection devices in the enclosed space. A processor may apply an optimization criteria to the external air condition index dataset and the internal air condition index dataset. A processor may, responsive to applying the optimization criteria, determine an air exchange plan. The processor may perform the air exchange plan.