Publications

Methane (CH4) leakage from natural gas (NG) pipelines poses an environmental, safety, and economic threat to the public. While previous leak detection and quantification studies focus on the aboveground infrastructure, the analysis of underground NG pipeline leak scenarios is scarce. Furthermore, no data from controlled release experiments have been published on the accuracy of methods used to (1) quantify emissions from an area source and (2) use these emissions to quantify the size of a subsurface leak. This proof-of-concept work uses CH4 mole fraction, as measured by a single gas sensor, as an input to a simple dispersion-based model (WindTrax) under ideal conditions (i.e., in a field) and compares the calculated emissions to the known controlled NG release rates. The aboveground and surface CH4 mole fractions were measured for 5 days at a field testbed using controlled underground release rates ranging from 0.08 to 0.52 kg hr–1 (3.83–24.94 ft3 hr–1). Results confirmed that the mean normalized CH4 mole fraction increases as the atmosphere transitions from the Pasquill–Gifford (PG) stability class A (extremely unstable) to G (extremely stable). The estimated surface CH4 emissions showed large temporal variability, and for the emission rates tested, at least 6 h of data are needed to have a representative estimate from subsurface pipeline leaks (±27% of the controlled release rate on average). The probability that the emission estimate is within ±50% of the controlled release rate (P±50%) is approximately 50% when 1 h of data is collected; the probability approaches 100% with 3–4 h of data. Findings demonstrate the importance of providing enough data over time for accurate estimation of belowground leak scenarios. By adopting the estimation method described in this study, operators can better estimate leakage rates and identify and repair the largest leaks, thereby optimizing annual greenhouse gas emissions reductions and improving public safety.

Continuous monitoring (CM) solutions can facilitate faster detection and repair of emissions compared to traditional survey methods. This study tested 13 CM solutions over 12 weeks using single-blind controlled testing. Controlled release rates ranged from 0.08 to 6.75 kg CH4 h−1 and lasted 18 min to 8 h. Six solutions demonstrated 90% method detection limits (DL90s) ranging from 0.5 [0.3, 0.6] kg CH4 h−1 to 6.7 [5.9, 8.0] kg CH4 h−1. Of the 6 solutions, 5 had low False Positive (FP) rates (7.8%–18.9%), and 4 had low False Negative (FN) rates (8.0%–34.1%). Similar to Ilonze et al., the results show that the tested solutions balance method sensitivity with low FP and FN rates. All scanning/imaging solutions achieved high localization precision and accuracy (≥40%) at the equipment unit level. Single quantification estimates exhibited high relative quantification errors, ranging from 33 [0.9, 66]%, 95% confidence interval (CI) to 1326 [1003, 1648]%, 95% CI for small emissions (between 0.1 and 1 kg CH4 h−1) and from 3 [−20, 26]%, 95% CI to 3578 [−2832, 9988]%, 95% CI for large emissions (>1 kg CH4 h−1). The mean detection time for all solutions ranged from 5 h to 5 days. Relative to previous studies, errors in quantification estimates decreased, as did FN and FP rates, with improved DL90s for 2 of the 4 retested solutions. However, the mean detection times increased for 2 solutions, remained constant for one solution, and decreased for 1 of the 4 retested solutions. These findings highlight that continuous, rigorous testing enhances solution performance, with notable improvements observed across multiple testing programs using the same test protocol.

Leaks in natural gas distribution networks are often initially detected by odor and then localized during operators' walking surveys. The size of the enhancement detected and the leak's proximity to infrastructure are used to determine leak severity of the leak and how quickly it is repaired. Methane (CH 4 ) enhancements on the surface above the leak change with atmospheric conditions, but currently the leak severity assessment is made without considering the environmental conditions and could result in misdiagnosis of the leak. This study has developed the “ESCAPE” model, a tool that can be used to estimate CH 4 enhancements on the surface above a leak using meteorological and leak data as input. Surface CH 4 concentrations calculated using the ESCAPE model agrees well with measurements made during controlled experiments (m = 0.95 and R 2 = 0.95). This study highlights the effect of micrometeorology on gas transport from the surface to the atmosphere, where the surface and the atmospheric conditions have the largest effect on the flux. Here, recommendations are made to improve industry best practices, including recording the meteorological conditions at the time of the leak detection, avoiding walking surveys on days where there are strong winds or strong solar irradiance and that known leak locations should be revisited and measured in different conditions. Mitigating CH 4 emissions from the gas distribution network is a cost-effective and economically realistic target. Distribution operators can be directed by the output of ESCAPE model to correctly identify and repair the largest distribution leaks and could reduce annual CH 4 emissions by 0.69 Tg, this may go some way to reducing energy sector emissions in accordance with the Paris Agreement. • Surface methane above pipeline leakage changes with atmospheric conditions. • Model developed to estimate surface methane using weather and leak data as input. • Model agrees with measurements made during controlled experiments, m = 0.95 & R2 = 0.95 • Shows need of using wind speed & irradiance to assess severity of distribution leaks. • Model helps identify/prioritize largest leaks for repair, reduces risk & GHG emission.

As smart grids introduce profound changes in the operation of the electric power industry, the need for efficient and robust resource allocation (RA) algorithms arises, especially due to the increasingly stochastic nature of availability of highly dispersed resources. A framework for solving the smart grid RA (SGRA) problem using a heuristic approach such as a genetic algorithm is presented in this paper. Similar challenges exist in resource allocation in the realm of computing. A comparison is drawn between SGRA and computing RA. Its application to a multi-agent-based distribution management system, used as an environment model, is also proposed. A path forward concludes the paper.

Unburned methane entrained in exhaust from natural gas-fired compressor engines ("combustion slip") can account for a substantial portion of station-level methane emissions. A novel in-stack, tracer gas method was coupled with Fourier transform infrared (FTIR) species measurements to quantify combustion slip from natural gas compressor engines at 67 gathering and boosting stations owned or managed by nine "study partner" operators in 11 U.S. states. The mean methane emission rate from 63 four-stroke, lean-burn (4SLB) compressor engines was 5.62 kg/h (95% CI = 5.15–6.17 kg/h) and ranged from 0.3 to 12.6 kg/h. The mean methane emission rate from 39 four-stroke, rich-burn (4SRB) compressor engines was 0.40 kg/h (95% CI = 0.37–0.42 kg/h) and ranged from 0.01 to 4.5 kg/h. Study results for 4SLB engines were lower than both the U.S. EPA compilation of air pollutant emission factors (AP-42) and Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) by 8 and 9%, respectively. Study results for 4SRB engines were 43% of the AP-42 emission factor and 8% of the GHGI emission factor, the latter of which does not distinguish between engine types. Total annual combustion slip from the U.S. natural gas gathering and boosting sector was modeled using measured emission rates and compressor unit counts from the U.S. EPA Greenhouse Gas Reporting Program. Modeled results [328 Gg/y (95% CI = 235–436 Gg/y) of unburned methane] would account for 24% (95% CI = 17–31%) of the 1391 Gg of methane emissions for "Gathering and Boosting Stations", or 6% of the net emissions for "Natural Gas Systems" (5598 Gg) as reported in the 2020 U.S. EPA GHGI. Gathering and boosting combustion slip emissions reported in the 2020 GHGI (374 Gg) fall within the uncertainty of this model.