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《工程(英文)》 >> 2019年 第5卷 第6期 doi: 10.1016/j.eng.2019.10.003

提高在线模型识别平台效率的多目标最优实验设计框架

Department of Chemical Engineering, University College London, London WC1E 7JE, UK

收稿日期: 2018-11-04 修回日期: 2018-12-19 录用日期: 2019-03-06 发布日期: 2019-10-15

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摘要

自动化和数字化方面的最新进展使得物理设备与其对应的虚拟设备紧密集成,从而促进了实时建模与多个过程的自动优化。此类系统提供了丰富且不断更新的数据环境,使得系统随着时间的推移做出决策,并将过程推向最优目标成为可能。在许多制造过程中,为了实现整体最优过程,必须同时评估与过程性能和成本有关的多个目标函数。本文提出了一个多目标最优实验设计框架,用于提高在线模型识别平台的效率。所提出的框架能够灵活权衡实验设计解决方案,这些解决方案可以在线计算(即在执行实验期间)。将该框架应用于流动反应器中动力学模型在线识别的案例研究,并确定了微反应器中苯甲酸(benzoic acid, BA)和乙醇酯化的动力学模型。

 

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