化工进展 ›› 2021, Vol. 40 ›› Issue (4): 1765-1776.DOI: 10.16085/j.issn.1000-6613.2020-2139
张梦轩(), 刘洪辰, 王敏, 蓝兴英(), 石孝刚, 高金森
收稿日期:
2020-10-26
出版日期:
2021-04-05
发布日期:
2021-04-14
通讯作者:
蓝兴英
作者简介:
张梦轩(1992—),男,博士研究生。E-mail:基金资助:
ZHANG Mengxuan(), LIU Hongchen, WANG Min, LAN Xingying(), SHI Xiaogang, GAO Jinsen
Received:
2020-10-26
Online:
2021-04-05
Published:
2021-04-14
Contact:
LAN Xingying
摘要:
随着人工智能技术和配套数据系统的快速发展,化工过程建模技术达到了新的高度,将多个机理模型和数据驱动模型以合理的结构加以组合的智能混合建模方法,可以综合利用化工过程的第一性原理及过程数据,结合人工智能算法以串联、并联或者混联的形式解决化工过程中的模拟、监测、优化和预测等问题,建模目的明确,过程灵活,形成的混合模型有着更好的整体性能,是近年来过程建模技术的重要发展趋势。本文围绕近年来针对化工过程的智能混合建模工作进行了总结,包括应用的机器学习算法、混合结构设计、结构选择等关键问题,重点论述了混合模型在不同任务场景下的应用。指出混合建模的关键在于问题和模型结构的匹配,而提高机理子模型性能,获取高质量宽范围的数据,深化对过程机理的理解,形成更有效率的混合建模范式,这些都是现阶段提高混合建模性能的研究方向。
中图分类号:
张梦轩, 刘洪辰, 王敏, 蓝兴英, 石孝刚, 高金森. 化工过程的智能混合建模方法及应用[J]. 化工进展, 2021, 40(4): 1765-1776.
ZHANG Mengxuan, LIU Hongchen, WANG Min, LAN Xingying, SHI Xiaogang, GAO Jinsen. Intelligence hybrid modeling method and applications in chemical process[J]. Chemical Industry and Engineering Progress, 2021, 40(4): 1765-1776.
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