Deterministic Global Process Optimization: Flash Calculations via Artificial Neural Networks

Artur M. Schweidtmann, Dominik Bongartz, Wolfgang R. Huster, Alexander Mitsos

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

8 Citations (Scopus)

Abstract

We recently demonstrated the potential of deterministic global optimization in a reduced-space formulation for flowsheet optimization. However, the consideration of implicit unit operations such as flash calculations is still challenging and the solution of complex flowsheets incorporating such operations can be intractable. We show that the solution of flash equations can be integrated in global optimization via artificial neural networks (ANNs). Thus, flash calculations are no longer performed within the flowsheet optimization. Instead, flash equations are solved offline and then learned using ANNs. ANNs have been used successfully in the literature to learn flash equilibria but have not yet been included in deterministic global optimization for this task. We embed the ANNs in a hybrid model and use deterministic global optimization to solve it. In addition, we utilize deterministic global optimization to calculate a guaranteed worst-case accuracy of ANNs compared to a rigorous model. We demonstrate the proposed approach on an illustrative five-component vapor-liquid equilibrium flash using our in-house solver MAiNGO.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier
Pages937-942
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameComputer Aided Chemical Engineering
Volume46
ISSN (Print)1570-7946

Keywords

  • Flowsheet
  • Guaranteed accuracy
  • McCormick relaxations
  • Reduced-space

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