A Framework for Stochastic and Surrogate-Assisted Optimization using Sequential Modular Process Simulators

被引：4
|
作者：
Penteado, Alberto T. [1 ]
Esche, Erik [1 ]
Weigert, Joris [1 ]
Repke, Jens-Uwe [1 ]
机构：
[1] Tech Univ Berlin, Grp Proc Dynam & Operat, Sekretariat KWT 9, Str 17 Juni 135, D-10623 Berlin, Germany
来源：
30TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A-C | 2020年 / 48卷
关键词：
sequential optimization; black-box optimization; stochastic optimization; surrogate-assisted optimization; probability of improvement; GLOBAL OPTIMIZATION; ALGORITHM;
D O I：
10.1016/B978-0-12-823377-1.50318-9
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A Python framework is introduced enabling automated simulation and stochastic and surrogate-assisted optimization (SAO) using Aspen Plus flowsheets. A new modification to the Probability of Improvement method for SAO is proposed to handle non-converged simulations. Two chemical engineering optimization problems are solved using gradient-based, stochastic, and the new SAO algorithms.
引用
收藏
页码：1903 / 1908
页数：6
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