Calibration of a low-cost methane sensor using machine learning
Calibration of a low-cost methane sensor using machine learning
In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0–200 ppm methane, 5–30 °C and 40–80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.
calibration, machine learning, methane, sensor
Mitchell, Hazel Louise
06b74ff6-e3ef-469f-8a69-b31ed409c09b
Cox, Simon J.
0e62aaed-24ad-4a74-b996-f606e40e5c55
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
6 February 2024
Mitchell, Hazel Louise
06b74ff6-e3ef-469f-8a69-b31ed409c09b
Cox, Simon J.
0e62aaed-24ad-4a74-b996-f606e40e5c55
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Mitchell, Hazel Louise, Cox, Simon J. and Lewis, Hugh G.
(2024)
Calibration of a low-cost methane sensor using machine learning.
Sensors, 24 (4), [1066].
(doi:10.3390/s24041066).
Abstract
In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0–200 ppm methane, 5–30 °C and 40–80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.
Text
sensors-24-01066-v2
- Version of Record
More information
Accepted/In Press date: 1 February 2024
Published date: 6 February 2024
Additional Information:
Publisher Copyright:
© 2024 by the authors.
Keywords:
calibration, machine learning, methane, sensor
Identifiers
Local EPrints ID: 490058
URI: http://eprints.soton.ac.uk/id/eprint/490058
ISSN: 1424-8220
PURE UUID: 70336f42-fae2-48bc-8ef0-695b54659221
Catalogue record
Date deposited: 14 May 2024 16:37
Last modified: 05 Jun 2024 01:35
Export record
Altmetrics
Contributors
Author:
Hazel Louise Mitchell
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
