Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty

Resul Al, Chitta Ranjan Behera, Krist V. Gernaey, Gürkan Sin*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Advances in simulation and optimization technologies coupled with the continued growth in computing power now increasingly pave the way for the development of advanced model-based engineering design frameworks. In this work, we propose an extensive computational framework, which brings together state-of-the-art engineering practices, such as high fidelity process simulation, superstructure-based conceptual design, global sensitivity analysis, Monte Carlo procedures for uncertainty quantification, and a stochastic simulation-based design space optimizer in order to foster decision making under uncertainty. The capabilities of the framework are highlighted in a case study, which addresses the challenges of how to synthesize and design wastewater treatment plant configurations under influent uncertainties. In order to handle multiple stochastic constraints, a black-box solver using a new infill criterion for surrogate-based optimization is also proposed. The results demonstrate the promising potential of the simulation and sampling-based framework for effectively addressing stochastic design problems arising in broader engineering domains.

Original languageEnglish
Article number107118
JournalComputers and Chemical Engineering
Number of pages20
Publication statusPublished - 2020


  • Monte Carlo simulation
  • Simulation-based optimization
  • Stochastic Kriging
  • Superstructure optimization
  • Wastewater treatment plant design


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