Method of optimizing filter life cycle between replacements and system for monitoring a ventilation system
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Solution Overview
Problem
Current methods for optimizing filter life cycles in ventilation systems fail to consider the trade-off between extending filter life to reduce replacement costs and minimizing energy consumption, particularly in demand-controlled ventilation systems, where fan operation varies with activity levels.
Innovation Solution
A method that determines the optimal filter life cycle by analyzing filter hardware and use values, pressure drop measurements, and airflow estimation to minimize total resource consumption, eliminating the need for a separate airflow meter and allowing for planning of filter changes to reduce costs and carbon dioxide impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If filter replacement interval is extended to reduce replacement costs, then filter hardware cost decreases, but energy consumption increases due to higher pressure drop
Solution Approach 1:
The patent changes the parameter of filter replacement timing from fixed intervals to dynamic optimization based on total resource consumption. By calculating the derivative of total cost function with respect to time, the system identifies the optimal replacement moment that balances filter cost and energy consumption, rather than using static replacement schedules.
Solution Approach 2:
The system implements feedback by continuously monitoring pressure drop across the filter and using this information to update the optimal replacement time calculation. The measured pressure drop data feeds into the cost function optimization, allowing the system to adapt replacement timing based on actual filter performance and operating conditions.
2Use of energy by moving object
If demand-controlled ventilation is used to save energy, then fan operation varies with activity levels, but filter life cycle optimization becomes more complex due to variable airflow
Solution Approach 1:
The patent accounts for variable airflow conditions by making the pressure drop function time-dependent and incorporating it into the total resource consumption calculation. The optimization algorithm adjusts for changing airflow rates caused by demand-controlled ventilation, allowing accurate determination of optimal replacement timing despite variable operating conditions.
Solution Approach 2:
The system transitions from static to dynamic optimization by continuously updating the optimal replacement time based on current pressure drop measurements and future pressure drop predictions. The calculation adapts to changing ventilation patterns, making the filter replacement strategy dynamic rather than fixed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more accurate estimation of airflow and optimal filter replacement timing, reducing resource consumption and energy costs while minimizing environmental impact by considering both filter production and usage costs.
Implementation Method 1
the system normally comprises one or more filters, arranged to filter incoming and/or outgoing air
Implementation Method 2
receiving a plurality of measured data points, each representing a measured pressure drop over the filter at a respective time
Data Source
AI summary
The present disclosure provides a method of determining an optimal filter life cycle between replacements of a filter in a ventilation system. The method comprising performing the following steps in a processing device: receiving at least one filter hardware value, receiving at least one filter use value, receiving a plurality of measured data points, and determining the optimal filter life cycle by minimizing a total filter resource consumption composed of a first factor, a second factor, providing a plurality of predicted data points, grouping successive measured data points into windows, and assigning the identified maximum pressure drop as a maximum pressure drop for all measured data points of that window.