Systems and methods for correlating indoor air quality data and trends to pathogen remediation
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Solution Overview
Problem
Current HVACR systems face challenges in cost-effectively monitoring and validating the reduction of biological pollutant loads, as continuous digital solutions for directly monitoring pathogens are expensive and prohibitive for general-purpose buildings, necessitating an alternative method to estimate and remediate indoor air quality.
Innovation Solution
An indoor air quality control system that includes an IAQ monitor, a controller, and an IAQ management server, which collects air quality data, generates biological pollutant estimates using algorithms, and recommends remediation actions, utilizing existing sensors for inorganic and volatile organic compounds to estimate and manage biological pollutant loads without dedicated pathogen detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuously sensing digital solutions for directly monitoring biological pollutant load are used, then measurement precision of biological pollutants is improved, but device complexity and cost increase to levels that are cost-prohibitive for general-purpose buildings
Solution Approach 1:
The patent introduces intermediary parameters (inorganic pollutants, VOCs, particulate matter) that can be measured by existing cost-effective sensors. These intermediaries serve as proxies for biological pollutant load, allowing indirect monitoring without requiring expensive direct pathogen detection equipment. The system correlates changes in these intermediary parameters with biological pollutant presence and trends.
Solution Approach 2:
The system creates a computational model that copies or simulates biological pollutant load estimation based on patterns observed from inorganic and organic air quality data. Instead of directly measuring biological pollutants, the system uses algorithms to generate estimates that mirror what direct measurement would provide, based on correlated environmental parameters.
2Device complexity
If existing sensors for inorganic and volatile organic compounds are used to estimate biological pollutant loads, then device complexity is reduced, but measurement precision of biological pollutants deteriorates
Solution Approach 1:
The system implements continuous feedback loops where air quality sensor data is constantly monitored, analyzed, and fed back into the estimation algorithm. This allows the system to refine its biological pollutant load estimates over time by detecting patterns and trends in the inorganic and organic compound data, improving precision through iterative optimization rather than relying on single-point measurements.
Solution Approach 2:
The system transforms the measurement approach by changing from direct biological pollutant measurement to indirect estimation through multiple inorganic and organic parameters. By monitoring changes in CO2, VOCs, particulate matter, temperature, and humidity simultaneously, the system creates a multi-parameter fingerprint that correlates with biological pollutant presence, compensating for the lack of direct biological sensing capability.
Data Source
AI summary
An indoor air quality (IAQ) control system for a heating, ventilation, air conditioning, and Refrigeration (HVACR) system including an IAQ monitor that collects air quality data from an air quality sensor of an air quality-controlled space; a controller that manages a remediation device of the air quality-controlled space; and an IAQ management server that generates a biological pollutant estimate based on the air quality data using an algorithm that correlates the air quality data to the biological pollutant estimate, and generates a remediation recommendation. Additionally, a control method includes collecting air quality data from an air quality sensor of an air quality-controlled space using an IAQ monitor; managing a remediation device of the air quality-controlled space using a controller; generating a biological pollutant estimate based on the air quality data using an algorithm that correlates the air quality data to the biological pollutant estimate; and generating a remediation recommendation.