Multipoint Air Sampling With Blended Signals for HVAC Ventilation Control
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Solution Overview
Problem
Existing multipoint air sampling systems struggle to provide accurate and cost-effective blended air quality parameter measurements, particularly for controlling building HVAC operations and maintaining healthy indoor air quality, due to limitations in combining individual air quality parameter data and issues with sensing temperature and other properties like relative humidity and enthalpy.
Innovation Solution
A system that uses a multipoint air sampling system and local discrete sensors to create blended air quality parameter measurements through signal processing, incorporating carbon dioxide levels and other air quality parameters to generate dilution ventilation and outside airflow command signals, ensuring accurate and efficient control of airflows and ventilation rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a multipoint air sampling system uses shared sensors to monitor multiple locations, then system cost is reduced, but measurement precision of blended air quality parameters deteriorates
Solution Approach 1:
The patent combines multiple individual air quality parameter measurements from different locations into a single blended measurement that represents the overall air quality of the monitored space. This merging approach allows the system to use cost-effective shared sensors while still providing accurate representative measurements of the entire environment, resolving the contradiction between system cost and measurement precision.
Solution Approach 2:
The shared sensors are designed to perform multiple functions by monitoring various air quality parameters (temperature, humidity, CO2, VOCs) across multiple locations. This multi-functionality reduces the need for separate sensors at each location, lowering system cost while maintaining measurement precision through the blended parameter approach.
2Adaptability or versatility
If the system monitors multiple air quality parameters simultaneously, then air quality monitoring comprehensiveness is improved, but device complexity increases
Solution Approach 1:
The system employs shared multi-functional sensors that can measure multiple air quality parameters (temperature, relative humidity, CO2, VOCs) simultaneously. This universality allows comprehensive air quality monitoring without requiring separate dedicated sensors for each parameter, thereby reducing device complexity while maintaining monitoring comprehensiveness.
Solution Approach 2:
The patent merges the functionality of multiple single-parameter sensors into unified multi-parameter sensing units. This consolidation reduces the overall number of components and simplifies system architecture while enabling simultaneous monitoring of multiple air quality parameters, resolving the contradiction between comprehensiveness and complexity.
3Measurement precision
If the system uses local discrete sensors at each location, then measurement precision is improved, but system cost increases
Solution Approach 1:
The system combines measurements from multiple locations into blended air quality parameters that represent the overall environment. This approach maintains measurement precision by capturing spatial variations through sampling while using cost-effective shared sensors, avoiding the need for expensive local discrete sensors at every location.
Solution Approach 2:
Instead of deploying physical sensors at every location, the system uses air sampling to create virtual copies of the air quality conditions at each location. The sampled air is transported to shared sensors that create representative measurements, providing accurate local information without the cost of physical sensor deployment at each point.
4Measurement precision
If the system processes signals from multiple sensors to create blended parameters, then air quality monitoring accuracy is improved, but processing complexity increases
Solution Approach 1:
The signal processing system merges data from multiple sensors and locations through weighted averaging and blending algorithms. This approach improves accuracy by accounting for spatial variations and sensor uncertainties while using computationally efficient methods that avoid excessive processing complexity. The blending process integrates multiple measurements into unified air quality parameters representing the overall environment.
Data Source
AI summary
A system for monitoring air quality conditions, comprising, a multi-point air monitoring system comprising, a plurality of sensors for collecting air quality data from a plurality of at least partially enclosed areas; one or more data processing units for processing one or more air quality parameters based on the collected air quality data; and one or more communication devices for communicating the data from the sensor to the processing unit; and a signal processing controller that generates one or more blended air quality parameter signals via the multi-point air monitoring system based at least in part on one or more of the processed air quality parameters representative of data from a plurality of the sensors.