宁夏大学学报（自然科学版中英文）2026，Vol.47Issue(5)：421-429,440,10.DOI:10.20176/j.cnki.nxdz.20260903
机器学习在化工过程优化中的应用
Application of Machine Learning in Chemical Process Optimization
摘要
Abstract
Chemical processes and material development involve complex multiphase interactions and dynamic variations.Traditional mechanistic models have limitations when dealing with unknown mechanisms or non-ideal conditions.This paper systematically reviews the current applications and cutting-edge progress of machine learning in chemistry and related fields,focusing on core areas such as process optimization and auto-matic control,reverse screening and design of new materials,intelligent closed-loop experimental platforms,and large-scale molecular dynamics simulations.The results show that data-driven models provide effective methods for solving fault diagnosis and nonlinear control challenges in complex chemical processes.The deep integration of generative algorithms and automated robotics remarkably accelerates the closed-loop iteration speed of physical experiments.Equivariant graph neural networks and machine learning force fields break through the spatiotemporal scale limitations of traditional quantum chemical calculations.Machine learning pro-motes the shift from traditional trial-and-error R&D models to fully automated and self-correcting proactive design paradigms.The findings offer theoretical references for the intelligent transformation of complex chemi-cal systems and the next generation of precise chemical design.关键词
机器学习/数据驱动/化工过程/过程控制/材料设计Key words
machine learning/data-driven/chemical process/process control/material design分类
建筑与水利引用本文复制引用
刘馨羽,唐旻萌,李宏平,吴沛文,朱文帅..机器学习在化工过程优化中的应用[J].宁夏大学学报（自然科学版中英文）,2026,47(5):421-429,440,10.基金项目
国家自然科学基金资助项目(22578187,22508419) （22578187,22508419）
国家自然科学基金联合基金重大项目(U25D9013) （U25D9013）
