Filter lifetime estimation
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Solution Overview
Problem
Current air purifier systems face challenges in accurately predicting the end-of-life (EOL) of pollutant removal structures, leading to premature or delayed replacements, which can increase costs and potentially compromise air quality, especially for vulnerable groups.
Innovation Solution
An air purifier monitoring system that uses a processor with performance characteristics and algorithms to estimate the degree of fouling of pollutant removal structures by considering ventilation conditions, utilizing CO2 levels to estimate actual ventilation and selecting appropriate algorithms for accurate fouling estimation, and adjusting sampling rates based on pollutant levels and ambient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed EOL values are used for pollutant removal structures, then replacement timing is simplified and standardized, but accuracy of EOL estimation deteriorates leading to premature or delayed replacements
Solution Approach 1:
The system transitions from static fixed EOL values to dynamic EOL estimation by continuously monitoring pollutant levels and calculating degree of fouling in real-time. The processor dynamically adjusts the estimated EOL based on actual operating conditions, pollutant concentrations, and environmental factors, allowing the replacement schedule to adapt to changing conditions rather than following a fixed timeline.
Solution Approach 2:
The system implements feedback mechanisms by continuously measuring pollutant levels with sensors, comparing actual performance against expected performance, and using this information to update the degree of fouling and estimated EOL. This closed-loop feedback allows the system to learn from actual operating conditions and improve EOL prediction accuracy over time, preventing both premature and delayed replacements.
2Reliability
If pollutant removal structures are replaced frequently to ensure air quality, then air purification reliability is improved, but operating costs increase due to unnecessary replacements
Solution Approach 1:
The system replaces the mechanical approach of scheduled physical replacement with an intelligent monitoring and estimation system. Instead of mechanically following a fixed replacement schedule or manually inspecting filters, the system uses sensors, processors, and algorithms to monitor pollutant levels, calculate degree of fouling, and estimate EOL, substituting physical replacement timing with intelligent decision-making.
Solution Approach 2:
The system enables self-service by automatically monitoring its own performance, calculating its own degree of fouling, and determining its own estimated EOL without requiring external intervention. The processor continuously assesses the condition of pollutant removal structures and provides self-diagnostic information about when replacement is actually needed, eliminating the need for conservative scheduled replacements.
3Loss of substance
If pollutant removal structures are delayed beyond EOL to reduce costs, then operating costs decrease, but air quality deteriorates posing health risks to vulnerable groups
Solution Approach 1:
The system takes preliminary action by continuously monitoring pollutant levels and calculating degree of fouling before the actual EOL is reached. By predicting the estimated EOL in advance based on current degradation trends and operating conditions, the system provides early warning signals that allow users to plan replacements proactively, preventing delayed replacements that would compromise air quality and health safety.
4Measurement precision
If multiple algorithms are used to estimate fouling under different ventilation conditions, then EOL estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system changes parameters by monitoring ventilation conditions (such as air exchange rates, CO2 levels, or airflow patterns) and selecting different algorithms based on the detected ventilation regime. Instead of using a single complex algorithm for all conditions, the system adjusts the computational approach by selecting from multiple algorithms optimized for specific ventilation scenarios, improving accuracy while managing complexity through conditional parameter-based selection.
Data Source
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AI summary
An air purifier monitoring system (10) is disclosed for monitoring an air purifier (50) including a pollutant removal structure (55) for removing a pollutant other than CO2 from an air-filled space housing the air purifier. The system comprises a processor (31) programmed with performance characteristics of the pollutant removal structure and with a plurality of algorithms for estimating a degree of fouling of the pollutant removal structure during an operating period of the air purifier, each of said algorithms being valid for a particular ventilation condition of the air-filled space, the processor being configured to receive a series of CO2 levels detected over a period of time from a CO2 sensor (23) in said air-filled space; estimate an actual ventilation condition of the air-filled space from the received series of CO2 levels; select an algorithm from the plurality of algorithms based on the estimated actual ventilation condition; receive a series of pollutant levels detected over the operating period from a pollutant sensor (21) in the air-filled space; and estimate the degree of fouling of the pollutant removal structure during the operating period using the selected algorithm, the received series of pollutant levels as a parameter of the selected algorithm and the performance characteristics of the pollutant removal structure. Also disclosed are an air purifier system comprising an air purifier (50) including a pollutant removal structure (55) and the air purifier monitoring system (10) and a method of monitoring a pollutant removal performance of a pollutant removal structure (55) of an air purifier (50) located in an air-filled space.