Computers & Chemical Engineering·· 2026-09-30AI 评分18
基于物理信息特征机器学习与概率过程控制的受限多相微流中流体动力学不稳定性:面向软测量、流型分类与闭环微反应器自动化的统一框架
Physics-informed feature-based machine learning and probabilistic process control of hydrodynamic instabilities in confined multiphase microflows: A unified framework for soft sensing, regime classification, and closed-loop microreactor automation
模型导读
研究提出一个统一框架,将物理信息特征机器学习与概率过程控制结合,用于受限多相微流中流体动力学不稳定性的软测量、流型分类与闭环微反应器自动化。作者为 Eric Kwame Owusu、Yue Wang、Na Liu、Tao Yue,成果发表于 Computers & Chemical Engineering 第 215 卷。
来源:Computers & Chemical Engineering · sciencedirect.com