Apparatuses and methods for air quality maintenance by ventilation based on pollutant forecast
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Solution Overview
Problem
Indoor air quality in closed environments like buildings is challenging to maintain due to factors such as space size, occupant density, and ventilation, leading to potential pollutant concentration exceeding safety thresholds without effective monitoring and remediation systems.
Innovation Solution
A system comprising an air quality server that receives pollutant concentration data, generates quality tendency projections, and performs air degradation or ventilation analysis to forecast critical times and safe times, triggering warnings and ventilation adjustments as needed to maintain safe pollutant levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ventilation is increased to reduce pollutant concentration, then air quality improves, but energy consumption increases
Solution Approach 1:
The system performs preliminary forecasting of pollutant concentrations using machine learning models to predict future air quality conditions. By anticipating pollution events before they occur, the system can proactively adjust ventilation settings to prevent pollutant accumulation, thereby maintaining air quality without requiring excessive ventilation and energy consumption during actual pollution events
Solution Approach 2:
The system continuously monitors actual pollutant concentrations and compares them against predicted values, using this feedback to refine ventilation control decisions. The feedback loop enables dynamic adjustment of ventilation rates based on real-time air quality conditions, optimizing the balance between maintaining acceptable air quality and minimizing energy consumption
2Reliability
If continuous monitoring and forecasting systems are implemented, then air quality management improves, but system complexity increases
Solution Approach 1:
The system employs multi-functional components where the machine learning models serve multiple purposes: they forecast pollutant concentrations, identify pollution events, trigger alerts, and inform ventilation control decisions. This multi-functionality reduces the need for separate dedicated systems for each task, thereby managing complexity while maintaining comprehensive air quality management capabilities
Solution Approach 2:
The machine learning models are trained and refined automatically using historical air quality data and sensor inputs, reducing the need for manual system configuration and ongoing expert intervention. The system self-optimizes its forecasting accuracy and pollution detection capabilities over time, simplifying operational complexity while improving reliability
Data Source
AI summary
Methods, apparatuses, and computer program products are disclosed for performing air quality maintenance. An example method include receiving an air quality dataset and generating a quality tendency projection based upon the air quality dataset that includes a quality rate of change. In an instance in which the quality tendency projection exceeds a prediction threshold and the quality rate of change exceeds a pollutant rate increase threshold, the method performs performing air degradation analysis. In an instance in which the quality tendency projection exceeds a warning threshold and the quality rate of change exceeds a pollutant rate decrease threshold, the method performs ventilation analysis.