(Invited) Applications of an AI-Powered, Iot-Based Electrochemical Sensor Platform
Authors: Agi Kamil , Smith James , Halley Sleight , Ramaiyan Kannan , Ian Robert , Garzon Fernando H. , Tsui Lok-kun 
Published: August 2024

Emissions are a significant contributor to climate change [1-2]. Low-cost sensor platforms are needed to understand the levels of emissions and the types of emissions [3]. In addition, sensors are needed to help determine the impact levels of what soon-to-be prominent gases, like hydrogen, will have on the environment [4]. Mixed potential electrochemical sensors are a promising technology for the foundation of the sensor platform [5].

Our group has developed an AI-powered, IoT-driven sensor platform capable of measuring and differentiating sources of methane, hydrogen, carbon monoxide, ammonia and oxides of nitrogen [6]. The sensor platform has been field tested at Colorado State University’s Methane Emission Technology Evaluation Center (METEC). Embedded in the system are machine learning methods that are applied to a training dataset collected in the laboratory to quantify the concentration and identify sources of emissions [6]. These results serve as a first demonstration that a low-cost mixed potential electrochemical sensor system can successfully detect underground pipeline emissions and quantify CH4 concentrations that are in agreement with previously published results [7] collected using more complex and costly methods.

This talk will focus on the applications of the sensor platform including leak detection, emissions monitoring, tailpipe emissions for transportation, flare management optimization and even methane destruction - all of which support the ability to measure and confirm net-zero strategies.

This work was supported by US Department of Energy Award DE-FE0031864.

References:

[1] US EPA, “Estimates of Methane Emissions by Segment in the United States,” Aug. 27, 2018. https://www.epa.gov/natural-gas-star-program/estimates-methane-emissions-segment-united-states (accessed Dec. 08, 2022).

[2] A. J. Marchese et al., “Methane Emissions from United States Natural Gas Gathering and Processing,” Environ. Sci. Technol., vol. 49, no. 17, pp. 10718–10727, Sep. 2015, doi: 10.1021/acs.est.5b02275.

[3] T. Aldhafeeri, M.-K. Tran, R. Vrolyk, M. Pope, and M. Fowler, “A Review of Methane Gas Detection Sensors: Recent Developments and Future Perspectives,” Inventions, vol. 5, no. 3, Art. no. 3, Sep. 2020, doi: 10.3390/inventions5030028.

[4]. https://www.edf.org/blog/2022/03/07/hydrogen-climate-solution-leaks-must-be-tackled

[5] F. H. Garzon, R. Mukundan, and E. L. Brosha, “Solid-state mixed potential gas sensors: theory, experiments and challenges,” Solid State Ion., vol. 136–137, pp. 633–638, Nov. 2000, doi: 10.1016/S0167-2738(00)00348-9.

[6] S. Halley, L. Tsui, and F. Garzon, “Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring,” J. Electrochem. Soc., vol. 168, no. 9, p. 097506, Sep. 2021, doi: 10.1149/1945-7111/ac2465.

[7] B. A. Ulrich, M. Mitton, E. Lachenmeyer, A. Hecobian, D. Zimmerle, and K. M. Smits, “Natural Gas Emissions from Underground Pipelines and Implications for Leak Detection,” Environ. Sci. Technol. Lett., vol. 6, no. 7, pp. 401–406, Jul. 2019, doi: 10.1021/acs.estlett.9b00291.