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提高在线模型识别平台效率的多目标最优实验设计框架
Arun Pankajakshan, Conor Waldron, Marco Quaglio, Asterios Gavriilidis, Federico Galvanin
工程（英文） ›› 2019, Vol. 5 ›› Issue (6) : 1049-1059.
PDF(1381 KB)
PDF(1381 KB)
提高在线模型识别平台效率的多目标最优实验设计框架
A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms
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。
Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many manufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of multiple objective functions related to process performance and cost is necessary. In this work, a multiobjective optimal experimental design framework is proposed to enhance the efficiency of online model-identification platforms. The proposed framework permits flexibility in the choice of trade-off experimental design solutions, which are calculated online—that is, during the execution of experiments. The application of this framework to improve the online identification of kinetic models in flow reactors is illustrated using a case study in which a kinetic model is identified for the esterification of benzoic acid and ethanol in a microreactor.
Multi-objective optimization / Optimal design of experiments / Online
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