机器学习在化工过程优化中的应用OA

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.