文章基本信息

标题：Non-Dimensional Feature Engineering and Data-Driven Modeling for Microchannel Reactor Control
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作者：Calvin Tsay ; Michael Baldea
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：11295-11300
DOI：10.1016/j.ifacol.2020.12.526
语种：English
出版社：Elsevier
摘要：AbstractCatalytic plate microchannel reactors (CPRs) are a promising means for modular hydrogen/fuels production from distributed natural gas resources. However, the equipment miniaturization presents challenges for process control, including spatially-distributed models, limited availability of measurements, and fast process time constants. In the present paper, we investigate the use of data-driven models—specifically, artificial neural networks (ANNs)—to estimate temperature “hotspots” within CPRs. We prescribe nonlinear transformations of the model inputs in the form of well-known dimensionless quantities (e.g., Reynolds number), and we show that these engineered features can improve the prediction capability of computationally parsimonious ANNs using a first-principles reactor model. Finally, we present a simulation case study that demonstrates the use of a trained ANN for inferential model predictive control.
关键词：Keywordsfeature engineeringmanufacturing processesprocess controlchemical industry