Optimal Design and Synthesis of Shale Gas Processing and NGL Recovery Processes

Jian Gong, Fengqi You

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

The booming of the shale gas industry is predicted to impact North American's energy landscape. This paper addresses the robust design and synthesis of shale gas processing and natural gas liquids (NGLs) recovery processes under uncertain feedstock compositions. The problem is addressed via three steps. First, we construct an uncertainty set for raw shale gas compositions. Second, we develop a superstructure for shale gas processing and NGLs recovery processes that includes monoethanolamine and diethanol amine absorption processes in an acid gas removal section, triethylene glycol absorption and condensation processes in a dehydration section, standalone and integrated designs of a NGLs recovery process, and a nitrogen rejection process. The proposed superstructure explicitly shows process configurations for the feedstocks in the uncertainty set. In the last step, we propose a two-stage adaptive robust mixed-integer linear programming problem based on the superstructure and a tailored solution method for the proposed model. The obtained robust optimal design is immunized against uncertainty in feedstock compositions.

Original languageEnglish (US)
Title of host publication26 European Symposium on Computer Aided Process Engineering, 2016
PublisherElsevier B.V.
Pages535-540
Number of pages6
Volume38
ISBN (Print)9780444634283
DOIs
StatePublished - Jan 1 2016

Publication series

NameComputer Aided Chemical Engineering
Volume38
ISSN (Print)1570-7946

Keywords

  • adaptive robust optimization
  • NGLs recovery
  • shale gas processing
  • superstructure

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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