Feed optimization for fluidized catalytic cracking using a multi-objective evolutionary algorithm

Kay Chen Tan, Ko Poh Phang, Ying Jie Yang

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

Feed optimization in the fluidized catalytic cracking (FCC) process is a prominent chemical engineering problem, where the objective is to maximize the production of high-quality gasoline stocks at a low energy consumption level. However, the various feeds, based on the density and volumetric flow rate of its constituent stream, are conflicting in nature and subjected to many practical constraints. As such, this chapter presents the application of a multi-objective evolutionary algorithm (MOEA) which will simultaneously optimize the various flow streams in a FCC feed surge drum of a local refinery. An interactive Graphical User Interface (GUI) based MOEA toolbox developed by the authors is used as the platform for optimization. The various trade-off surfaces between the different objectives evolved by the MOEA provide further insights to this problem and allow more optimal choices during the decision making process. Lastly, a performance comparison based on several key performance indexes shows that the overall economic gain offered by MOEA optimization against the conventional approach like linear programming is significantly higher.

Original languageEnglish
Title of host publicationMulti-Objective Optimization
Subtitle of host publicationTechniques and Applications in Chemical Engineering (Second Edition)
PublisherWorld Scientific Publishing Co. Pte. Ltd.
Pages291-314
Number of pages24
ISBN (Print)9789813148239
DOIs
Publication statusPublished - 22 Dec 2016
Externally publishedYes

Keywords

  • Feed optimization
  • Fluidized catalytic cracking
  • Multi-objective evolutionary algorithm

ASJC Scopus subject areas

  • General Engineering

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