人工智能（AI）在复杂化工过程设计中的应用：现状、挑战与展望

doi:10.16085/j.issn.1000-6613.2025-0549

化工进展 ›› 2025, Vol. 44 ›› Issue (8): 4821-4837.DOI: 10.16085/j.issn.1000-6613.2025-0549

• 过程模拟与仿真前沿与趋势 • 上一篇

人工智能（AI）在复杂化工过程设计中的应用：现状、挑战与展望

陈嵩嵩¹^,²^,³(), 鲍艾丽³^,⁴, 霍锋³, 侯亚慧³, 崔改静³^,⁵, 张军平³()

^1.中国科学院成都有机化学研究所, 四川成都 610041
^2.中国科学院大学, 北京 100049
^3.中国科学院过程工程研究所介科学与工程全国重点实验室, 北京 100190
^4.渤海大学数学科学学院, 辽宁锦州 121013
^5.龙子湖新能源实验室，河南郑州 450046

收稿日期:2025-04-14 修回日期:2025-07-02 出版日期:2025-08-25 发布日期:2025-09-08
通讯作者: 张军平
作者简介:陈嵩嵩（1988—），男，博士研究生，高级工程师，研究方向为化学工程。E-mail: sschen@ipe.ac.cn。
基金资助:
国家重点研发计划(2022YFB3807501);中国科学院战略性科技先导项目“煤炭清洁燃烧与低碳利用”(XDA29030900)

Application of artificial intelligence (AI) in the design of complex chemical engineering processes: Status, challenges and prospects

CHEN Songsong¹^,²^,³(), BAO Aili³^,⁴, HUO Feng³, HOU Yahui³, CUI Gaijing³^,⁵, ZHANG Junping³()

^1.Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, Sichuan, China
^2.University of Chinese Academy of Sciences, Beijing 100049, China
^3.State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
^4.College of Mathematics and Physics, Bohai University, Jinzhou 121013, Liaoning, China
^5.Longzihu New Energy Laboratory, Zhengzhou 450046, Henan, China

Received:2025-04-14 Revised:2025-07-02 Online:2025-08-25 Published:2025-09-08
Contact: ZHANG Junping

摘要/Abstract

摘要：

基于人工智能的数据驱动复杂化工过程设计新模式发展迅速，成为推动化工行业变革性发展的强大动力，对引领研究范式变革、新技术开发及流程再造具有重要理论指导与实践意义。本文聚焦人工智能在复杂化工过程设计中的研究进展，系统阐述其在分子结构物性预测、热/动力学预测、反应分离路径推荐、工艺过程优化四个核心环节的作用及应用成效，总结分析了在数据收集与清洗、模式识别与趋势预测的表现，深入剖析了当前面临的专业特征数据质量不稳定、模型可解释性不足等挑战，并提出了未来应构建全要素、多层级化工大数据库，持续探究智能算法与化工流程结构信息关联机制，着力提升模型的可解释和架构稳定性，为构建从分子识别到过程设计的智能模型框架、实现化工过程智能设计提供新思路。

关键词: 人工智能, 分子指纹, 热力学性质, 多尺度预测, 优化设计

Abstract:

Artificial intelligence has driven rapid advancements in the design of complex chemical engineering processes with a new data-driven model, serving as a powerful force behind transformative developments in the chemical industry and holding significant implications for the evolution of research paradigms, new technologies, and industrial processes. This paper focuses on the progress of intelligent algorithms in the design of complex chemical processes, and systematically outlines the applications in molecular structure and property prediction, recommendation of reaction and separation pathways, and intelligent optimization of process parameters. The paper also summarizes the performance of intelligent algorithms in big datasets collection and cleaning, pattern recognition, and trend prediction. It provides an in-depth analysis of the challenges faced by intelligent algorithms in chemical process design, including issues related to the lack of professional feature data quality and the insufficient interpretability of models. The paper proposes the practical need for the development of comprehensive multi-level chemical big datasets, the continuous exploration of the relationship between intelligent algorithm structures and chemical process node information, and further improvements in the interpretability and structural stability of intelligent models. These efforts aim to construct a large-scale model framework for intelligent chemical process design, from molecular structure recognition to process design, and ultimately realize intelligent design in the chemical industry.

Key words: artificial intelligence, molecular fingerprint, thermodynamic properties, multiscale prediction, optimal design

中图分类号:

TP181

陈嵩嵩, 鲍艾丽, 霍锋, 侯亚慧, 崔改静, 张军平. 人工智能（AI）在复杂化工过程设计中的应用：现状、挑战与展望[J]. 化工进展, 2025, 44(8): 4821-4837.

CHEN Songsong, BAO Aili, HUO Feng, HOU Yahui, CUI Gaijing, ZHANG Junping. Application of artificial intelligence (AI) in the design of complex chemical engineering processes: Status, challenges and prospects[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4821-4837.

图/表 14

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描述符	特征参量	优势	劣势
拓扑结构描述符	分子的拓扑连接结构	计算相对简单快速，能反映分子的整体形状和连接性	计算量大，无法准确捕捉分子的三维结构信息和电子性质，相似拓扑结构区分能力有限
几何构型描述符	分子的体积、表面积、形状因子	描述分子的大小和形状，与分子的物理性质如溶解性、渗透性等有一定关联	计算相对复杂，尤其是对于大分子或柔性分子
量子化学描述符	分子轨道能、电子亲和能、电离能等	与分子的化学活性、反应性密切相关	通常比较耗时，对于大规模分子体系的计算成本较高
物化性质描述符	熔点、沸点、溶解度、分配系数等	通常可以通过实验测量得到，具有较高的可靠性	实验测量可能需要耗费大量时间和资源

描述符	特征参量	优势	劣势
拓扑结构描述符	分子的拓扑连接结构	计算相对简单快速，能反映分子的整体形状和连接性	计算量大，无法准确捕捉分子的三维结构信息和电子性质，相似拓扑结构区分能力有限
几何构型描述符	分子的体积、表面积、形状因子	描述分子的大小和形状，与分子的物理性质如溶解性、渗透性等有一定关联	计算相对复杂，尤其是对于大分子或柔性分子
量子化学描述符	分子轨道能、电子亲和能、电离能等	与分子的化学活性、反应性密切相关	通常比较耗时，对于大规模分子体系的计算成本较高
物化性质描述符	熔点、沸点、溶解度、分配系数等	通常可以通过实验测量得到，具有较高的可靠性	实验测量可能需要耗费大量时间和资源

应用领域	AI模型架构	研究内容	相关文献
分子识别	ANN/BPNN/GCN/G-CNNs/UG-RNN/SVR/RT/MLP/VMC	分子数据集构建、分子功能识别、同质/异质分子分类、结构与物化性质关联	[9,16-17,20-22]
分子设计	ANN/GNN/GCN/RF/SVM	分子间相互作用、催化剂结构设计与优化	[23-26, 108-110]
物性预测	ANN/GNN/pS-CNN/DL/MLP/RF/SVM/GAGBM/SE/RF/XRT	密度、黏度、熔点、电导率、临界性质等热物性预测	[30-33, 35,37,41-42,44]
反应过程	ANN/FNN/BPNN/SVR/GPR/GA/MLP	反应网络、反应速率、条件优化	[49-56,28-62]
分离过程	ANN/D-MPNN/AL/MLP/GPR/SPT	相平衡、热力学函数、活度系数、交互参数	[56,63-68]
流程设计	ANN/DE/GA/MOEA/NSGA-Ⅱ/LLM	分离序列、制备过程、PFD图绘制	[69-75,77-78]
过程控制	NN/BPNN/RBFNN/WNN/GA/NSGA-Ⅱ/RL/DRL/PSO/FGS	PID控制、精馏控制、反应控制、锅炉燃烧、PID图	[80-87]
过程优化	ANN/DNN/DBN/GA/NSGA-Ⅱ/GAGA/GP/MOWCA/MOGA/PSO/DL/NLP/FC-ResNet	反应分离过程、精馏过程、氧气脱硫、碳捕集	[70, 89-95,102-107, 111-113]

应用领域	AI模型架构	研究内容	相关文献
分子识别	ANN/BPNN/GCN/G-CNNs/UG-RNN/SVR/RT/MLP/VMC	分子数据集构建、分子功能识别、同质/异质分子分类、结构与物化性质关联	[9,16-17,20-22]
分子设计	ANN/GNN/GCN/RF/SVM	分子间相互作用、催化剂结构设计与优化	[23-26, 108-110]
物性预测	ANN/GNN/pS-CNN/DL/MLP/RF/SVM/GAGBM/SE/RF/XRT	密度、黏度、熔点、电导率、临界性质等热物性预测	[30-33, 35,37,41-42,44]
反应过程	ANN/FNN/BPNN/SVR/GPR/GA/MLP	反应网络、反应速率、条件优化	[49-56,28-62]
分离过程	ANN/D-MPNN/AL/MLP/GPR/SPT	相平衡、热力学函数、活度系数、交互参数	[56,63-68]
流程设计	ANN/DE/GA/MOEA/NSGA-Ⅱ/LLM	分离序列、制备过程、PFD图绘制	[69-75,77-78]
过程控制	NN/BPNN/RBFNN/WNN/GA/NSGA-Ⅱ/RL/DRL/PSO/FGS	PID控制、精馏控制、反应控制、锅炉燃烧、PID图	[80-87]
过程优化	ANN/DNN/DBN/GA/NSGA-Ⅱ/GAGA/GP/MOWCA/MOGA/PSO/DL/NLP/FC-ResNet	反应分离过程、精馏过程、氧气脱硫、碳捕集	[70, 89-95,102-107, 111-113]

人工智能（AI）在复杂化工过程设计中的应用：现状、挑战与展望

Application of artificial intelligence (AI) in the design of complex chemical engineering processes: Status, challenges and prospects

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