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Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2020-09-30 , DOI: 10.1016/j.compchemeng.2020.107118
Resul Al , Chitta Ranjan Behera , Krist V. Gernaey , Gürkan Sin

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.



中文翻译:

基于随机仿真的不确定性过程综合与设计的上层建筑优化框架

仿真和优化技术的进步以及计算能力的持续增长现在为基于模型的高级工程设计框架的开发铺平了道路。在这项工作中,我们提出了一个广泛的计算框架,该框架汇集了最新的工程实践,例如高保真过程仿真,基于上层建筑的概念设计,全局灵敏度分析,不确定性量化的蒙特卡洛程序以及基于随机仿真的设计空间优化器,以促进不确定性下的决策。案例研究突出了该框架的功能,该案例解决了在不确定性影响下如何综合和设计废水处理厂配置的挑战。为了处理多个随机约束,还提出了一种使用新填充准则进行基于代理的优化的黑盒求解器。结果表明,基于仿真和采样的框架在有效解决更广泛的工程领域中出现的随机设计问题方面具有广阔的潜力。

更新日期:2020-10-08
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