Extensive literature has considered reduced, but still highly accurate, nonlinear dynamic process models, particularly for distillation columns. Nevertheless, there is a need for continuing research in this field. Herein, opportunities from the integration of machine learning into existing reduction approaches are discussed. First, key concepts for dynamic model reduction and their limitations are briefly reviewed. Afterwards, promising model structures for reduced hybrid mechanistic/data‐driven models are outlined. Finally, crucial future challenges as well as promising research perspectives are presented.

中文翻译：

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减少动力学建模的混合机械/数据驱动方法的潜力：在蒸馏塔中的应用

大量文献考虑了简化的但仍然非常准确的非线性动态过程模型，特别是对于蒸馏塔。尽管如此，仍需要在该领域中继续研究。在此，讨论了将机器学习集成到现有的减少方法中的机会。首先，简要回顾了动态模型简化的关键概念及其局限性。然后，概述了用于简化的混合机械/数据驱动模型的有前途的模型结构。最后，介绍了未来的关键挑战以及有前途的研究前景。