Optimization of driver selection for minimizing cost on LNG plant considering risk factor

Inkyu Lee, Kyungjae Tak, Wonsub Lim, Il Moon, Kwang Ho Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The natural gas liquefaction process is an cryogenic system so it is very energy and cost intensive. There are several ways to reduce energy and cost consumption. One is getting optimal design and the other is getting optimal operating condition. After determined the process design, then optimize operating conditions such as temperatures, pressures and compositions. In this paper, to get optimal design in point of cost, optimization of driver selection is performed considering risk factor on C3-MR (propane pre-cooling mixed refregerant) process. Driver selection includes capital and operating cost. Capital cost could just get from venders but operating cost is very changeable. To solve driver selection more accurate, our team consider not only cost of fuels and electrics but also risk factor as the cost term. There are three scenarios to compare effects of risk factor. Risk factor depends on number of compressors which is connected to same driver. Each case is selected from mixed integer non-linear programming (MINLP) optimization results. The result is significantly different considering risk factor or not.

Original languageEnglish
Title of host publicationICCAS 2012 - 2012 12th International Conference on Control, Automation and Systems
Pages2222-2225
Number of pages4
Publication statusPublished - 2012
Event2012 12th International Conference on Control, Automation and Systems, ICCAS 2012 - Jeju, Korea, Republic of
Duration: 2012 Oct 172012 Oct 21

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Other

Other2012 12th International Conference on Control, Automation and Systems, ICCAS 2012
Country/TerritoryKorea, Republic of
CityJeju
Period12/10/1712/10/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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