Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring
Publication: Journal of Environmental Engineering
Volume 146, Issue 7
Abstract
Advances in sensor technology have increased our ability to monitor a wide range of environments. However, even as the cost of sensors decline, only a limited number of sensors can be installed at any given site. The physical placement of sensors, along with the sensor technology and operating conditions, can have a large impact on our ability to adequately monitor environmental change. This paper introduces a new open-source Python package, called Chama, that determines optimal sensor placement and technology to improve a sensor network’s detection capabilities. The methods are demonstrated using site-specific methane emission scenarios that capture uncertainty in wind conditions and emission characteristics. Mixed-integer linear programming formulations are used to determine sensor locations and detection thresholds that maximize detection of the emission scenarios. The optimized sensor networks consistently increase the ability to detect leaks, as compared to sensors placed near each potential emission source or along the perimeter of the site.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study are available from the corresponding author by request. Chama can be installed through the Sandia National Laboratories GitHub organization at https://github.com/sandialabs/chama. Software documentation is available at https://chama.readthedocs.io.
Acknowledgments
This work was funded through Sandia National Laboratories’ Laboratory Directed Research and Development (LDRD) program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc, for the USDOE’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the USDOE or the United States Government.
References
Ahmadzadeh, A., J. Keller, G. Pappas, A. Jadbabaie, and V. Kumar. 2008. “An optimization-based approach to time-critical cooperative surveillance and coverage with UAVs.” In Vol. 39 of Experimental robotics, 491–500. Berlin: Springer.
Benavides-Serrano, A. J., M. S. Mannan, and C. D. Laird. 2016. “Optimal placement of gas detectors: A P-median formulation considering dynamic nonuniform unavailabilities.” AlChE J. 62 (8): 2728–2739. https://doi.org/10.1002/aic.15259.
Berry, J., W. E. Hart, C. A. Phillips, J. G. Uber, and J.-P. Watson. 2006. “Sensor placement in municipal water networks with temporal integer programming models.” J. Water Resour. Plann. Manage. 132 (4): 218–224. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(218).
Brereton, C. A., I. M. Joynes, L. J. Campbell, and M. R. Johnson. 2018. “Fugitive emission source characterization using a gradient-based optimization scheme and scalar transport adjoint.” Atmos. Environ. 181 (May): 106–116. https://doi.org/10.1016/j.atmosenv.2018.02.014.
ERG (Eastern Research Group). 2011. City of Fort Worth natural gas air quality study, Final report. Fort Worth, TX: ERG.
Gurobi. 2019. “Gurobi optimization.” Accessed January 3, 2019. http://www.gurobi.com.
Hart, W. E., C. Laird, J.-P. Watson, D. L. Woodruff, G. Hackebeil, B. Nicholson, and J. Siirola. 2017. Vol. 67 of Pyomo—Optimization modeling in Python. 2nd ed. New York: Springer.
IBM. 2019. “CPLEX optimizer.” Accessed January 3, 2019. https://www.ibm.com/analytics/cplex-optimizer.
Karatas, M., N. Razi, and H. Tozan. 2016. “A comparison of P-median and maximal coverage location models with Q-coverage requirement.” Procedia Eng. 149: 169–176. https://doi.org/10.1016/j.proeng.2016.06.652.
Klise, K. A., B. Nicholson, and C. D. Laird. 2017. Sensor placement optimization using Chama. Albuquerque, NM: Sandia National Laboratories.
Klise, K. A., B. Nicholson, C. D. Laird, T. Flanagan, A. Ravikumar, S. Sreedhara, and A. Brandt. 2018. Developing fugitive emissions sensor networks. Albuquerque, NM: Sandia National Laboratories.
Krol, M., S. Houweling, B. Bregman, M. Broek, A. Segers, P. V. Velthoven, W. Peters, F. Dentener, and P. Bergamaschi. 2005. “The two-way nested global chemistry-transport zoom model TM5: Algorithm and applications.” Atmos. Chem. Phys. 5 (2): 417–432. https://doi.org/10.5194/acp-5-417-2005.
MacDonell, M., M. Raymond, D. Wyker, M. Finster, C. Young-Soo, T. Raymond, B. Temple, and M. Scofield. 2013. Mobile sensors and applications for air pollutants. Washington, DC: USEPA.
Makhorin, A. 2019. “GLPK (GNU Linear Programming Kit).” Accessed January 3, 2019. https://www.gnu.org/software/glpk.
McKinney, W. 2012. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media.
NOAA. 2018. “NOAA Automated surface observing system (ASOS).” Accessed September 10, 2018. https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/automated-surface-observing-system-asos.
NOAA. 2019. “What averaging procedures are performed on the wind measurements?” Accessed November 3, 2019. https://www.ndbc.noaa.gov/wndav.shtml.
Powers, G., X. Y. Huang, B. Klemp, C. Skamarock, J. Dudhia, O. Gill, G. Duda, D. Barker, and W. Wang. 2008. A description of the advanced research WRF, Version 3. Boulder, CO: National Center for Atmospheric Research.
Ravikumar, A. P., J. Wang, and A. R. Brandt. 2017. “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51 (1): 718–724. https://doi.org/10.1021/acs.est.6b03906.
Rossman, L. A. 2000. Epanet 2 users manual. Cincinnati: US EPA.
Scire, J. S., D. G. Strimaitis, and R. J. Yamartino. 2000. A user’s guide for the CALPUFF dispersion model, Version 5. Concord, MA: Earth Tech.
Stein, A. F., R. R. Draxler, G. D. Rolph, B. J. B. Stunder, M. D. Cohen, and F. Ngan. 2015. “NOAA’s HYSPLIT atmospheric transport and dispersion modeling system.” Bull. Am. Meteorol. Soc. 96 (12): 2059–2077. https://doi.org/10.1175/BAMS-D-14-00110.1.
USEPA. 2016. User’s guide for the AMS/EPA regulatory model (AERMOD). Research Triangle, NC: USEPA.
Williams, M. D., M. J. Brown, B. Singh, and D. Boswell. 2004. QUIC-PLUME theory guide. Los Alamos, NM: Los Alamos National Laboratory.
Yi, T. H., and H. N. Li. 2012. “Methodology developments in sensor placement for health monitoring of civil infrastructures.” Int. J. Distrib. Sens. Netw. 8 (8): 612726. https://doi.org/10.1155/2012/612726.
Younis, M., and K. Akkaya. 2008. “Strategies and techniques for node placement in wireless sensor networks: A survey.” Ad Hoc Networks 6 (4): 621–655. https://doi.org/10.1016/j.adhoc.2007.05.003.
Zhen, T., K. A. Klise, S. Cunningham, E. Marszal, and C. D. Laird. 2019. “A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities.” Process Safe. Environ. Protect. 132 (4): 47–58. https://doi.org/10.1016/j.psep.2019.08.030.
Zhou, X., V. Amaral, and J. D. Albertson. 2017. “Source characterization of airborne emissions using a sensor network: Examining the impact of sensor quality, quantity, and wind climatology.” In Proc., IEEE Int. Conf. on Big Data (BIGDATA), 4621–4629. New York: IEEE.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 146 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Aug 18, 2019
Accepted: Jan 22, 2020
Published online: Apr 24, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 24, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.