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