Superstructure Optimization of Oleochemical Processes with Surrogate Models

Mark Jones, Hector Forero-Hernandez, Alexandr Zubov, Bent Sarup, Gürkan Sin

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this work we present a framework to generate surrogate models from rigorous process models embedded in a modelling environment or a process simulator. These unit operations (i.e. process flowsheet subsystems) are treated as black-box models to generate data for fitting and deriving the surrogate. Further, the methodology includes the formulation of a superstructure optimization problem and solving it to identify the optimal process flowsheet structure and point of operation from the possible alternatives. The superstructure optimization incorporates selection and interconnection of each surrogate and multi-objective optimization in respect to total annual cost and environmental impact. In this paper we highlight the surrogate building step of the methodology with a rigorous counter-current spray column model and assess the performance of different surrogate modelling methods.
Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018
EditorsMario R. Eden, Marianthi G. Ierapetritou, Gavin P. Towler
Volume44
PublisherElsevier
Publication date2018
Pages277-282
ISBN (Electronic)978-0-444-64241-7
DOIs
Publication statusPublished - 2018
Event13th International Symposium on Process Systems Engineering (PSE 2018) - San DIego, United States
Duration: 1 Jul 20185 Jul 2018

Conference

Conference13th International Symposium on Process Systems Engineering (PSE 2018)
CountryUnited States
CitySan DIego
Period01/07/201805/07/2018
SeriesComputer Aided Chemical Engineering
ISSN1570-7946

Keywords

  • Polynomial Chaos Expansion
  • Gaussian Process Regression
  • Optimization
  • Superstructure Generation

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