good work
good work
Thank you!
Nicely done Ashleigh
Great presentation and very actual.
Very informative
Nice video Ashleigh, basis a study I have just done on shale gas and its impacts within our industry seems you are going to be busy with some useful research here.
Very insightful and visually engaging.
Good work, Ashleigh!
GREAT job Ashleigh!!
Good job!!!!!!
Excellent video. I certainly hope this reseach gets the support in needs in adressing the issue of “fugitive” natual gas emissions and the harm they cause on the environment.
Great work!
Great job animating the mobile sampling process and conveying the research needs!
- Eben and Gayle
Way to Go Ashleigh!!! Excellent research topic! Your video provided great substance for the allotted timeframe and your presentation captivated the essence of your topic with great eloquence.
Let’s protect “Mother Earth” and our environment. I pray that grants and funding continue for this critical research.
Thank you everyone!
Excellent paper, Ashleigh. Good luck!
Wow – great presentation – I was really into this field, both from the EPA viewppoint and threshold levels to the OSHA/NIOSH occupational exposures for those in the chemical, oil, and gas industry. Things are coming a long way – thanks for sharing this informative approach and menthodology.
Very interesting- I had my 13 year old son watch with me and he liked it too!
Very nice research. Great work, Ashleigh.
Excellent presentation Ashleigh, good work for the research.
Good work in your video and nice research, Ashleigh.
Great work! Very impressive! Your findings will surely be very helpful in addressing environmental issues/challenges.
The possibilities of the proposed enhancements to the EPA/GMAP on methane emissions by optimizing mobile sampling mapping and other baseline factors that may result in a cost neutral model to improve the environment is excellent. I very much enjoyed the professional grade video overview which will be shared with my peers and family
Good work!
Ashleigh, good work in your video, your research
is very helpful.
Excellent presentation, Good work, Ashleigh.
Extremely informative presentation Ashleigh. Well done!
Very interesting Ashleigh.
An excellent presentation.
Best Wishes
Excellent presentation Ashleigh. I wish you the very best!
Very interesting research. Great work, Ashleigh.
Ashleigh,
Nice presentation! A very interesting application of path planning/optimization.
Prof. Jeff Krolik
Great presentation.
A fascinating subject and project. You could strengthen the presentation by emphasizing the multiple disciplines that you apparently need to tap for this work. Seems like key issues will be how well the model of emissions transport represents the relevant processes, and how well your available measurements will be able to validate or calibrate the model.
Nice!!!!
Great presentation, Ashleigh!
Excellent. We need useful scientific research such as this.
Further posting is closed as the event has ended.
Mary Albert
Good job. It seems that the weather would have a major impact on the near-surface gas distribution that you would be sampling. How do you account for outcomes that vary due to changes in the weather?
Ashleigh Swingler
Thank you for the comment and question. The REM vehicle is able to record the current weather conditions at the time of a particular concentration measurement using the sonic anemometer and compact meteorological station. This data will be applied in the inverse-dispersion algorithm. Then, the Bayesian inference approach will take into account the various weather conditions at the time of concentration measurement when determining the probability of a particular hypothesis of source characteristics.
Hainsworth Shin
good job. Will it be necessary to develop the optimal paths of the REM vehicles for each specific production site. I assume that this would be the case since not all sites are laid out the same. Or will it eventually be possible to develop an automated process to design vehicle paths on common features of these production sites, if there are any? This seems like a complex undertaking.
Ashleigh Swingler
Excellent question. A long range goal of this project is to develop a software module that would automate the path planning process. Our current vision of this software is that it would extract road position data from satellite images of the production sites. Then, using a database of well locations, environmental forecasts, and real-time data obtained from the REM vehicle, the software would instantaneously determine the optimal path for the sensing system.
Christopher Buneo
Very good! It seems like the inverse-dispersion method is critical to your approach but I didn’t get a good sense of how it works. Can you elaborate on how the source of the emissions is determined from the concentration measurements?
Ashleigh Swingler
Correct, the inverse-dispersion technique is an essential piece to this project. It will utilize Bayesian inference and plume dispersion models to determine the well source strengths from the concentration data obtained by the REM vehicle along public roadways. A model for the dispersion of methane will be formulated, then, Bayesian probability theory will be used to derive the posterior probability density function (PDF) of the source parameters. The posterior PDF defines the probability of a particular hypothesis, i.e. distribution of source strengths, given the sensor data and background information.
Karen McDonald
A very neat project. Since all of the source well locations are known doesn’t the problem just revert to the standard “shortest path for the traveling salesman” problem from operations research or is there something more here that relies on following methane gradients (sort of like chemotaxis of microbes)? Also assuming that data from different sources is collected under different whether conditions (wind speed/direction) how do you correct for that to identify the site with the highest fugitive emissions?
Ashleigh Swingler
These are two interesting questions. First, the goal of this project is to determine an optimal sampling strategy for the REM vehicle that both maximizes the information profit of the sensors and minimizes the operational costs of the vehicle. This problem is typically referred to as sensor path planning, in which the primary purpose of the mobile agent is to fulfill a sensing objective. As I understand, the traveling salesman problem is concerned with determining the shortest possible route by which a salesman can visit each city in a map exactly one time. The latter problem is more akin to robot path planning, which addresses purely navigational objectives and typically aims to optimize a deterministic additive function, such as Euclidean distance, while sensor path planning aims to optimize a stochastic sensing objective that is not necessarily additive. In this project, the algorithm developed will access the expected measurement of a particular path by utilizing prior sensor information, and sensor and environmental models.
Your second question was actually asked by another judge as well. Here was my response:
The REM vehicle is able to record the current weather conditions at the time of a particular concentration measurement using the sonic anemometer and compact meteorological station. This data will be applied in the inverse-dispersion algorithm. Then, the Bayesian inference approach will take into account the various weather conditions at the time of concentration measurement when determining the probability of a particular hypothesis of source characteristics.
Peter Pfromm
It seems to me that putting an “error bar” on the mass balance that you are trying to do here is perhaps most important, considering the humbling complexity (large scale and highly variable convection, diffusion, absorption, unknown time dependence of emissions….) of the problem and the very modest tools and data available. What are your thoughts on assigning a “confidence interval” or some such?
Ashleigh Swingler
Humbling indeed. But yes, providing a confidence interval will absolutely be an important component of this project. In this problem, uncertainty arises in the model parameters, assumed distribution, and the sensor models; therefore, it will be important to determine our confidence in a particular hypothesis of source strengths. The assessment of uncertainty will be addressed when using Bayesian inference for the determination of source strengths. This approach will result in a posterior probability density function over the source parameters, exhibiting the confidence in the hypothesis of a particular set of source strengths.