文献类型：期刊文献

中文题名：机器学习在结晶过程建模与控制中的研究进展

英文题名：Recent advances in machine learning for modeling and control of crystallization processes

作者：Yan, Yihang[1,2]; Ma, Yuan[1,2]; Liu, Chenglin[1,2]; Yu, Jianguo[1,2]

机构：[1] National Engineering Research Center for Integrated Utilization of Salt Lake Resources, East China University of Science and Technology, Shanghai, 200237, China; [2] Engineering Research Center of Resource Process Engineering, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

年份：2026

卷号：77

期号：5

起止页码：2307

外文期刊名：Huagong Xuebao/CIESC Journal

收录：EI(收录号：20262921108458)

语种：中文

外文关键词：Decision making - Feature extraction - Learning systems - Model predictive control - Predictive analytics - Predictive control systems - Process monitoring

摘要：Crystallization is a fundamental solid-liquid separation operation in chemical, pharmaceutical, food and materials industries. Achieving high product quality requires precise control of critical process parameters such as temperature, feed rate and solvent composition. Conventional modeling and control strategies that rely on mechanistic formulations or empirical correlations often fail to represent the highly nonlinear and multiscale nature of crystallization systems. In recent years, machine learning, with its powerful data-driven feature extraction and pattern recognition capabilities, has provided new ideas for the intelligent research of crystallization processes. This review summarizes recent developments in machine learning enabled process monitoring, predictive modeling and control optimization for crystallization. The potential of these methods to enhance process characterization, improve model fidelity and support intelligent decision making is highlighted. Challenges related to data quality, model generalization and reliable industrial deployment are also discussed. ? 2026, Materials China. All rights reserved.