Natural Gas to Liquid Transportation Fuels under Uncertainty Using Robust Optimization

Title

Natural Gas to Liquid Transportation Fuels under Uncertainty Using Robust Optimization

Subject

Optimization
Natural gas transportation
Liquefied natural gas
Profitability
Uncertainty analysis
Refining
Costs
Investments
Iterative methods

Description

The current pricing climate for natural gas and liquid transportation fuels adds a significant amount of uncertainty when designing new natural gas to liquid transportation fuel (GTL) refineries. Robust optimization is a useful tool for optimization under uncertainty and can be specifically applied to the problem of GTL process synthesis under feedstock price, product price, and investment cost uncertainty. Using historical data to define uncertain price parameters according to an assumed uniform distribution, a process synthesis superstructure with an uncertain objective function was created to maximize the profit of a GTL refinery. Recently developed, tight probabilistic bounds were used a priori and a posteriori in an iterative method to provide solutions with known probabilities of constraint violation for three different uncertainty sets. The relative impact of price and investment cost uncertainties are discussed, as is the impact of uncertainty on the overall guaranteed profit of a refinery, the refinery topology, and product distributions. The profitability results with probabilistic guarantees provide useful information for potential GTL refinery investors in the current uncertain energy markets. 2018 American Chemical Society.
11112-11129
32
57

Publisher

Industrial and Engineering Chemistry Research

Date

2018

Contributor

Matthews, Logan R.
Guzman, Yannis A.
Onel, Onur
Niziolek, Alexander M.
Floudas, Christodoulos A.

Type

journalArticle

Identifier

8885885
10.1021/acs.iecr.8b01638

Collection

Citation

“Natural Gas to Liquid Transportation Fuels under Uncertainty Using Robust Optimization,” Lamar University Midstream Center Research, accessed May 14, 2024, https://lumc.omeka.net/items/show/26697.

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