化工进展 ›› 2021, Vol. 40 ›› Issue (4): 1699-1714.DOI: 10.16085/j.issn.1000-6613.2020-1829
收稿日期:
2020-09-10
出版日期:
2021-04-05
发布日期:
2021-04-14
通讯作者:
罗正鸿
作者简介:
朱礼涛(1991—),男,博士研究生,研究方向为反应器建模及机器学习。E-mail:基金资助:
ZHU Litao(), OUYANG Bo(), ZHANG Xibao, LUO Zhenghong()
Received:
2020-09-10
Online:
2021-04-05
Published:
2021-04-14
Contact:
LUO Zhenghong
摘要:
准确理解并精确预测多相反应器内复杂的流体力学特性、传递现象及反应特征,是过程工程领域的热点方向之一。随着试验测量技术及高性能计算机的快速发展,研究者可以获取高精度的多维瞬态流场数据集。近十年来,机器学习作为一门新兴学科,越来越广泛地应用于数据挖掘、图像识别、智能控制等领域。本文概述了几种常用的机器学习方法(包括神经网络模型、支持向量机模型、决策树模型、聚类算法模型等),总结了机器学习模型的构建过程(包括数据集的建立、特征变量的选择、算法框架的选取、模型参数的调优、模型验证与测试等),综述了机器学习辅助多相反应器中流场本构模型构建、流场图像重构、流型识别、流场关键参数预测及优化、不确定度分析、数字孪生技术平台等方面的应用进展,剖析了机器学习结合多相反应器领域所面临的挑战,展望了机器学习在多相反应器中可能有待拓展的方向。
中图分类号:
朱礼涛, 欧阳博, 张希宝, 罗正鸿. 机器学习在多相反应器中的应用进展[J]. 化工进展, 2021, 40(4): 1699-1714.
ZHU Litao, OUYANG Bo, ZHANG Xibao, LUO Zhenghong. Progress on application of machine learning to multiphase reactors[J]. Chemical Industry and Engineering Progress, 2021, 40(4): 1699-1714.
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