Evaluation of fixed and adaptive concentration thresholds for particle filter systems

Area of Science:

• Environmental Health
• Indoor Air Quality
• Energy Efficiency

Background:

• Particle filtration systems reduce indoor particulate matter (PM) but often have high energy demands.
• Automated operation can optimize filtration efficiency and energy consumption.

Purpose of the Study:

• To evaluate fixed and adaptive concentration thresholds for automating air filtration systems.
• To assess the impact of these thresholds on energy usage and PM reduction.

Main Methods:

• Simulated indoor environments based on real-world PM measurements from two particle counters (Dylos DC1700, Alphasense OPC-N2).
• Analysis of air cleaner runtime and PM concentration reduction under different fixed and adaptive threshold strategies.
• Utilized k-means clustering for adaptive threshold development.

Main Results:

• A fixed threshold of 4.0 μg·m⁻³ reduced runtime to 6.9%-21.0% while achieving 67%-71% PM reduction.
• Raising fixed thresholds further decreased runtime with minimal impact on exposure reduction.
• Adaptive thresholds provided significant exposure reduction and prevented high runtimes, largely independent of filtration power or sensor type.

Conclusions:

• Automated particle filtration using optimized thresholds offers a sustainable method for improving indoor air quality and occupant health.
• Adaptive and optimized fixed thresholds can substantially reduce energy consumption of air filtration systems.
• Smart automation is key to making particle filtration a more viable and energy-efficient solution for indoor environments.