Ammon Eaton

About

ammon@byu.edu
I became interested in a degree in Chemical Engineering after managing a small independent oil production company in Bakersfield, CA. I graduated from BYU with a B.S. in chemical engineering and a minor in geology in 2013, and I am currently pursuing a PhD.

I enjoy soccer, gardening, raising chickens, snow skiing, boating, and horseback riding. I grew up in Heber City, UT and I am married and have three children.
I am working with Dr. John Hedengren and Dr. Morris Argyle to address flow assurance challenges in crude oil pipelines. Petroleum currently fulfills 32% of total national energy demand (more than any other source of energy), and will likely continue to do so for at least the next 30 years (see www.EIA.gov). This sustained high demand requires efficient, safe, reliable and economical transportation methods for crude oil throughout the country and from offshore oilfields. Historically, pipelines have transported oil in routes that are separate from public traffic, uses less energy per barrel of oil transported than other methods (trucks or railways), and have a smaller carbon footprint than other transportation methods. However, these benefits come with many maintenance and operation challenges. One of the most critical challenges is the monitoring and control system. This system measures necessary properties such as pressure and temperature, converts the data into relevant information, and notifies pipeline controllers of the current conditions. If the system reports an anomaly, the personnel decide what the appropriate action should be. One of the major problems with a monitoring and control system is the frequency of false alarms. For example, hydrocarbon sensors on the exterior of the pipeline often report leaks. Yet, visual inspection of the pipeline segment reveals there is no leak, and that the elevated reading came from a natural hydrocarbon source not related to the pipeline. The frequency of these types of false alarms can be reduced by incorporating model predictive control into the pipeline monitoring and control system. Model predictive control uses a mathematical model to predict, for example, the amount of wax deposits that should be inside a pipeline over a given time period. This prediction is then compared to the physical measurements of the monitoring system for agreement. If the difference between the model and the measurements exceed a given threshold, the system notifies pipeline controllers, and possibly takes action such as automatically closing a valve. While few pipeline monitoring and control systems use model predictive control, it may actually help to decrease to number of false alarms in the pipeline industry. Using already established models, I plan to develop model predictive controls that can be used in the pipeline industry.

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Ammon Eaton