机器学习在结晶过程建模与控制中的研究进展

闫艺航; 马渊; 刘程琳; 于建国

doi:10.11949/0438-1157.20251297

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机器学习在结晶过程建模与控制中的研究进展

综述与专论 | 更新时间：2026-06-18

- 机器学习在结晶过程建模与控制中的研究进展
- Recent advances in machine learning for modeling and control of crystallization processes
- 化工学报 2026年77卷第5期页码：2307-2321
- 作者机构：
  
  1.华东理工大学国家盐湖资源综合利用工程技术研究中心，上海 200237
  2.华东理工大学资源过程工程教育部工程研究中心，上海 200237
- 作者简介：
  
  闫艺航 (2002—)，男，博士研究生，yanyihang1@163.com
  刘程琳 (1986—)，男，博士，副教授，liuchenglin@ecust.edu.cn
  于建国 (1960—)，男，博士，教授，jgyu@ecust.edu.cn
- 基金信息：
  
  上海市自然科学基金项目(25ZR1401083);江西省重点研发计划项目(20223BBG74008)
- DOI：10.11949/0438-1157.20251297
  中图分类号： TP 181
- 收稿：2025-11-22，
  
  修回：2026-01-13，
  
  纸质出版：2026-05-25
- 稿件说明：
移动端阅览
闫艺航, 马渊, 刘程琳, 于建国. 机器学习在结晶过程建模与控制中的研究进展[J]. 化工学报, 2026, 77(5): 2307-2321

YAN Yihang, MA Yuan, LIU Chenglin, YU Jianguo. Recent advances in machine learning for modeling and control of crystallization processes[J]. CIESC Journal, 2026, 77(5): 2307-2321
闫艺航, 马渊, 刘程琳, 于建国. 机器学习在结晶过程建模与控制中的研究进展[J]. 化工学报, 2026, 77(5): 2307-2321 DOI： 10.11949/0438-1157.20251297.

YAN Yihang, MA Yuan, LIU Chenglin, YU Jianguo. Recent advances in machine learning for modeling and control of crystallization processes[J]. CIESC Journal, 2026, 77(5): 2307-2321 DOI： 10.11949/0438-1157.20251297.

摘要

结晶是化工、制药、食品和材料等领域最重要的固液分离操作单元，高质量结晶高度依赖于精准控制结晶温度、进料流率和溶剂组成等过程参数。传统基于机理和经验的建模与控制方法难以准确描述结晶体系的复杂非线性行为。近年来，机器学习凭借其强大的数据驱动特征提取与模式识别能力，为结晶过程的智能化研究提供了新的思路。通过系统梳理机器学习在结晶过程监测、预测建模与控制优化方面的主要进展，阐明了其在过程表征、模型精度提升及智能调控中的应用潜力，并对数据质量、模型泛化等问题及未来工业安全可靠应用进行了简要展望。

Abstract

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.

关键词

Keywords

references

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