Reliable drone-based remote infrared optical quantification of methane emissions

To slow global warming as quickly as possible, it is imperative to minimize not only CO2 emissions but, in particular, methane emissions. In the energy and industrial sectors, this can be achieved through leak detection and repair (LDAR) programs. Efficient quantification of leak volumes enables planning, prioritization, and the rapid, effective implementation of repairs, as well as objectively measured emissions reporting. Similarly, future emissions can be prevented by quantitatively measuring current leaks and, based on these measurements, selecting robust and “low-emission” designs and materials—such as seals.
To achieve this, this project will build upon the quadcopter drone measurement system developed as a prototype in preliminary work and will conduct a detailed investigation and further development of the sensors and data fusion algorithms used for quantification. The intended applications for the drone measurement system include biogas plants, landfills, and industrial and utility gas networks. An infrared remote-sensing gas sensor (Tunable Diode Laser Absorption Spectroscopy, TDLAS) will be used. This sensor enables integral methane concentration measurements without interference from the downwash of the drone’s rotors and allows for measurements in locations that are not directly accessible or flyable. The TDLAS sensor is mounted on a gimbal, which enables precise alignment, stabilization, measurement in any direction, and scanning movements.
As part of the method development, the measurement accuracy of the methane-sensitive TDLAS sensor is to be significantly improved through task-specific calibration. Since wind speed—and thus gas velocity—has a major influence on quantification and can vary greatly from place to place, wind fields will be measured in field trials using an anemometer array. Furthermore, the developed measurement model will be improved by reducing oversimplifying assumptions, and advanced quantification methods will be developed using regression models and machine learning approaches. The reliability of the quantification results will be described by a measure of uncertainty that also takes into account the measurement conditions, such as wind speed, the degree of land development, and the number and distribution of integral methane concentration measurements.
In the partner project for field testing, the airborne measurement system is to be improved and tested in terms of its practical suitability in both hardware and software. This includes, among other things, expanding the existing smart support systems (e.g., autopilot) as well as enhancing the human-machine interface to display detected leaks in a virtual 3D model of the facility. To evaluate the practical suitability of the improved aerial measurement system, detailed field tests will be conducted at various facilities. In addition to biogas plants, other facilities and pipelines—such as those on bridges over bodies of water—will also be inspected.

Christian Grundman, M.Sc.

March 2025 – February 2027

Hermann Sewerin, LLC

1. Grundman, Christian; Jerschke, Aaron; Schmoll, Robert; Kroll, Andreas: Calibration Stand Development for Open-Path Calibration of TDLAS Sensors at Low Concentrations, Field Laser Application in Industry and Research (FLAIR) 2026, Aix-les-Bains, 2026, accepted