4.6 Article

Novel Natural Gas to Liquids Processes: Process Synthesis and Global Optimization Strategies

期刊

AICHE JOURNAL
卷 59, 期 2, 页码 505-531

出版社

WILEY
DOI: 10.1002/aic.13996

关键词

process synthesis with heat; power; water integration; GTL; Fischer-Tropsch; methanol to gasoline; methanol to olefins and distillate

资金

  1. National Science Foundation [NSF EFRI-0937706, NSF CBET-1158849]
  2. Directorate For Engineering
  3. Emerging Frontiers & Multidisciplinary Activities [0937706] Funding Source: National Science Foundation
  4. Div Of Chem, Bioeng, Env, & Transp Sys
  5. Directorate For Engineering [1158849] Funding Source: National Science Foundation

向作者/读者索取更多资源

An optimization-based process synthesis framework is proposed for the conversion of natural gas to liquid transportation fuels. Natural gas conversion technologies including steam reforming, autothermal reforming, partial oxidation to methanol, and oxidative coupling to olefins are compared to determine the most economic processing pathway. Hydrocarbons are produced from Fischer-Tropsch (FT) conversion of syngas, ZSM-5 catalytic conversion of methanol, or direct natural gas conversion. Multiple FT units with different temperatures, catalyst types, and hydrocarbon effluent compositions are investigated. Gasoline, diesel, and kerosene are generated through upgrading units involving carbon-number fractionation or ZSM-5 catalytic conversion. A powerful deterministic global optimization method is introduced to solve the mixed-integer nonlinear optimization model that includes simultaneous heat, power, and water integration. Twenty-four case studies are analyzed to determine the effect of refinery capacity, liquid fuel composition, and natural gas conversion technology on the overall system cost, the process material/energy balances, and the life cycle greenhouse gas emissions. (C) 2013 American Institute of Chemical Engineers AIChE J, 59: 505-531, 2013

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据