Profit optimization for bio-gas upgrading PSA process based on controlling step-time

Seungnam Kim, Jinkyung Kim, Il Moon

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

High purity methane gas from bio-gas has been magnified as an alternative source of energy recently. Gas above 95% purity can be used as fuel for transportation, however only gas above 97% purity can be distributed via pipe lines for domestic usage. Price of 97% purity gas for domestic gas is much higher than 95% purity gas that is used only for transportation, however recovery of methane gas in the process is lower. Purity and recovery of the product is determined by operating procedures. Thus, several researchers are setting their goal to improve profit by controlling operating procedures. Among upgrading processes, the Pressure Swing Adsorption (PSA) is suitable for separation of high purity methane gas in terms of economics. In this study, as step-time in the PSA has huge effect in the entire process, the optimal step-time for maximizing profit is evaluated in the bio-gas upgrading process using PSA. Especially the relation between profit and steptime is taken into consideration. Before optimization, a 4-step PSA model has been built on physics and chemistry theoretical basis. The developed model is also simulated using dynamic simulation software. After that, optimal step-time for maximizing profit is calculated by optimization method.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages397-402
Number of pages6
DOIs
Publication statusPublished - 2013

Publication series

NameComputer Aided Chemical Engineering
Volume32
ISSN (Print)1570-7946

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

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    Kim, S., Kim, J., & Moon, I. (2013). Profit optimization for bio-gas upgrading PSA process based on controlling step-time. In Computer Aided Chemical Engineering (pp. 397-402). (Computer Aided Chemical Engineering; Vol. 32). Elsevier B.V.. https://doi.org/10.1016/B978-0-444-63234-0.50067-1