Parametric sensitivity analysis for the optimal design of a bitumen upgrading plant using a steady-state model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The exploitation of non-conventional oil resources have gained global attention in the last decades given their abundance; particularly the Canadian Oil Sands is perceived as a key global energy supplier for the future. The Canadian non-conventional oil resources originate from Oil Sands or Tart Sands deposits; which are technically defined as extra-heavy crude bitumen. This work proposes an optimization model for the design of the most suitable bitumen upgrading pathway in terms of utility operating costs. The optimization approach chooses amongst different available upgrading steps to form the most cost-effective pathway. A sensitivity analysis was performed on the natural gas price for identifying its impact level over the crude bitumen upgrading process. Natural gas is the main fuel feedstock used for the generation of most Oil Sands industry utilities. The optimization results show that thermocracking-based upgraders are most cost-effective than hydrocracking-based pathways. This because hydrocracking-based upgraders are more energy-intensive mainly due to their high hydrogen requirements.

Original languageBritish English
Title of host publicationProcess Development Division 2016 - Core Programming Area at the 2016 AIChE Meeting
Pages52-60
Number of pages9
ISBN (Electronic)9781510834460
StatePublished - 2016
EventProcess Development Division 2016 - Core Programming Area at the 2016 AIChE Meeting - San Francisco, United States
Duration: 13 Nov 201618 Nov 2016

Publication series

NameProcess Development Division 2016 - Core Programming Area at the 2016 AIChE Meeting

Conference

ConferenceProcess Development Division 2016 - Core Programming Area at the 2016 AIChE Meeting
Country/TerritoryUnited States
CitySan Francisco
Period13/11/1618/11/16

Keywords

  • Bitumen upgrading
  • Design
  • Economics
  • Optimization
  • Parametric analysis

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