Proactive building air quality management
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Solution Overview
Problem
Conventional air quality management systems in buildings are reactive and inaccurate, failing to proactively improve indoor air quality, leading to inefficient use of air filters and inadequate protection against air pollution, which can contribute to health issues such as pulmonary and cardiovascular diseases.
Innovation Solution
A proactive air quality management system that uses low-cost, high-precision sensors, IoT communication, machine learning, and data storage to create a model of human behavior and causal links between actions and air quality effects, enabling proactive purification and minimizing exposure to pollutants by optimizing HVAC and air cleaning device operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional whole-house HVAC filters are used to remove particulates, then air quality is improved when HVAC fans are operational, but the system cannot proactively control HVAC fan speed based on air quality
Solution Approach 1:
The system continuously monitors air quality parameters (PM2.5, PM10, VOCs, CO2) using sensors and feeds this information back to the controller, which automatically adjusts HVAC fan speed and air purifier operation in real-time based on the measured air quality conditions, transforming the system from passive to proactive control
Solution Approach 2:
The system uses machine learning algorithms to automatically analyze sensor data, identify pollution sources, predict air quality trends, and determine optimal control actions without human intervention, enabling the HVAC system to self-regulate and proactively maintain air quality based on learned patterns of occupancy and environmental conditions
2Ease of operation
If room air purifiers operate in manual mode or respond to low-cost dust-measuring optical sensors, then air purification is provided, but the systems are inaccurate and reactive rather than predictive and proactive
Solution Approach 1:
The system replaces simple optical dust sensors with a multi-parameter sensor array that measures PM2.5, PM10, VOCs, CO2, temperature, and humidity simultaneously, providing comprehensive and accurate air quality assessment. Machine learning algorithms process this data to predict air quality degradation before it occurs, enabling proactive control rather than reactive response to already-deteriorated air quality
3Reliability
If air filters are used broadly without targeted control, then air quality is improved in general areas, but premature overuse of air filters and energetic inefficiency occur
Solution Approach 1:
The system divides the building into multiple zones with individual air quality monitoring and control, allowing targeted air purification only in areas where pollution is detected or predicted. The controller selectively activates specific air purifiers and adjusts HVAC distribution based on localized sensor readings and occupancy detection, avoiding unnecessary operation in unoccupied or already-clean areas
Solution Approach 2:
The system segments the building into controllable zones with independent air quality management, using occupancy sensors and air quality sensors to identify which zones require purification. The HVAC system and air purifiers are controlled on a zone-by-zone basis rather than operating the entire system uniformly, reducing energy consumption while maintaining air quality where needed
4Device complexity
If conventional air quality systems wait for significant air quality deterioration before acting, then system complexity is reduced, but responsiveness to air pollution is delayed
Solution Approach 1:
The machine learning system analyzes historical sensor data, occupancy patterns, weather conditions, and building usage to predict when and where air quality will deteriorate. The controller proactively activates air purifiers and adjusts HVAC settings before pollution levels rise to harmful thresholds, preventing air quality degradation rather than merely responding to it after the fact
Data Source
AI summary
An air quality management system comprises a plurality of air quality sensors to sense air quality within a building, a plurality of air cleaning devices, and a computer system in communication with the plurality of air quality sensors and the plurality of air cleaning devices. The plurality of air quality sensors is located at a particular location within the building. The computer system determines a correlational model of air quality for the building that indicates a correlational relationship between the sensed air quality, a spatial parameter, a temporal parameter, and operation of the air cleaning devices. The computer system controls the plurality of air cleaning devices to implement an air quality control policy based on one or more air quality management parameters.