Surrogate-based process synthesis

Carlos A. Henao, Christos T. Maravelias

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Superstructure optimization-based process synthesis is generally regarded as theoretically powerful; however, it has not been widely used in practice since it typically results in large-scale non-convex Mixed-Integer Non-Linear Programs (MINLP) which are very hard to solve effectively. To address this limitation, we propose a framework leading to substantially simpler formulations through the replacement of complex first-principle unit models by compact and yet accurate surrogate models. We show how all the relevant variable relationships established by a unit model, can be expressed in terms of a subset of the original model variables. We discuss how this subset of variables can be identified, and we present a method to develop high quality surrogate models through artificial neural networks. Finally, we propose a tailored surrogate model reformulation to incorporate binary variables that allow activation/deactivation of particular units within the superstructure model. An example is presented to illustrate the application of the proposed framework.

Original languageEnglish (US)
Pages (from-to)1129-1134
Number of pages6
JournalComputer Aided Chemical Engineering
Issue numberC
StatePublished - 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications


  • Process optimization
  • Process synthesis
  • Surrogate models


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