.png){kind=icon}
返回
Design Under Uncertainty Using Parallel Multiperiod Dynamic Optimization
.png){kind=icon}
PRE
AI
DOI：10.1002/aic.14473.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A technique for optimizing dynamic systems under uncertainty using a parallel programming implementation is developed in this article. A multiple-shooting discretization scheme is applied, whereby each shooting interval is solved using an error-controlled differential equation solver. In addition, the uncertain parameter space is discretized, resulting in a multiperiod optimization formulation. Each shooting interval and period (scenario) realization is completely independent, thus a major focus of this article is on demonstrating potential computational performance improvement when the embedded dynamic model solution of the multiperiod algorithm is implemented in parallel. We assess our parallel multiperiod and multiple-shooting-based dynamic optimization algorithm on two case studies involving integrated plant and control system design, where the objective is to simultaneously determine the size of the process equipment and the control system tuning parameters that minimize cost, subject to uncertainty in the disturbance inputs. (C) 2014 American Institute of Chemical Engineers
Keyword:
dynamic optimization
multiple-shooting discretization
multiperiod formulation
design under uncertainty
parallel computing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
机构
引用论文
.png){kind=icon}
Optimal design and control of dynamic systems under uncertainty: A probabilistic approach不确定条件下动态系统的最优设计与控制: 一种概率方法
PREAI
分享收藏
查看更多内容