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收稿日期:
2023-12-31
修回日期:
2024-03-11
出版日期:
2024-03-25
通讯作者:
王凯
作者简介:
刘梦绮(2000—),女,博士研究生,liumengqi27@163.com
基金资助:
Mengqi LIU(), Kai WANG(), Guangsheng LUO
Received:
2023-12-31
Revised:
2024-03-11
Online:
2024-03-25
Contact:
Kai WANG
摘要:
微分散是微化工技术的重要组成部分,其装备和过程的复杂性使得相关研究受到诸多限制,传统思想指导下微分散基础研究以“设计-实验-模型”为思路,进展缓慢。近年来,人工智能方法因其强大的识别和回归能力在化工领域倍受关注,人工智能辅助的微分散基础研究有利于形成微化工过程认识的新范式,促进微化工技术发展。本文介绍了人工智能方法的一般思路及其针对微分散研究的适用性,综述了人工智能技术在显微图像识别、液滴和气泡分散尺寸预测以及微分散过程控制和优化中的研究进展,对未来基于人工智能的微化工研究进行了展望。
中图分类号:
刘梦绮, 王凯, 骆广生. 基于人工智能的微分散基础研究[J]. 化工学报, DOI: 10.11949/0438-1157.20231406.
Mengqi LIU, Kai WANG, Guangsheng LUO. Fundamental research on microdispersion based on artificial intelligence[J]. CIESC Journal, DOI: 10.11949/0438-1157.20231406.
几何结构 | 半经验关联式a | 适用范围 | 参考文献 |
---|---|---|---|
T型微通道 | d/W =2.7(Cad/Cac)0.19 | 0.0003<Cac<0.1 | [ |
毛细管嵌入T型微通道 | d/W = 0.75(Qd/Qc)1/3Cac–1/5 | 0.002<Cac<0.01 | [ |
毛细管嵌入阶梯T型微通道 | d/W = 1.10(Qd/Qc)0.49(μd/μc)0.02 | Cac>0.07 | [ |
聚焦流微通道 | d/W = 0.88(Qd/Qc)0.18Cad–0.15 | 0.002<Cac<0.223 | [ |
聚焦流微通道 | d/W = 1.29(Qd/Qc)0.17Cac–0.1 | 0.00065<Cac<0.2 | [ |
表1 不同微分散通道中液滴尺寸的半经验关联式
Table 1 Semi-empirical correlations for droplet size in different microdispersion channel
几何结构 | 半经验关联式a | 适用范围 | 参考文献 |
---|---|---|---|
T型微通道 | d/W =2.7(Cad/Cac)0.19 | 0.0003<Cac<0.1 | [ |
毛细管嵌入T型微通道 | d/W = 0.75(Qd/Qc)1/3Cac–1/5 | 0.002<Cac<0.01 | [ |
毛细管嵌入阶梯T型微通道 | d/W = 1.10(Qd/Qc)0.49(μd/μc)0.02 | Cac>0.07 | [ |
聚焦流微通道 | d/W = 0.88(Qd/Qc)0.18Cad–0.15 | 0.002<Cac<0.223 | [ |
聚焦流微通道 | d/W = 1.29(Qd/Qc)0.17Cac–0.1 | 0.00065<Cac<0.2 | [ |
模型类型 | 输入特征 | 输出目标 | 数据量 | 尺寸预测准确性 | 文献 |
---|---|---|---|---|---|
ANN | Red、Rec、Cac、Cad | 液滴尺寸 | 742 | R2≈1 | [ |
ANFIS | Ca、μd/μc、Qd/Qc | 液滴尺寸 | 36 | R2=0.92 | [ |
ANFIS | 狭缝宽度Or、Qc、Qd、μc、μd、ρc | 液滴尺寸 | 51 | R2=0.96 | [ |
ANN | 聚合物浓度、Qc、Qd、微通道组合形式 | 液滴或颗粒尺寸 | 223 | R2=0.971 | [ |
ANN | Ca、Qd/Qc、连续相通道宽度Wc、分散相通道宽度Wd、Or、缩口长度L、下游通道宽度Wm、通道深度H | 液滴尺寸 生成频率 | 998 | R2=0.893(滴状流) R2=0.966(射流) | [ |
DNN | Wed、Wec、Ca、Qd/Qc、Wd、Wc、Wm、αlm、αgm | 气泡尺寸 | 854 | MRE=2.02% | [ |
BRANN XGBoost | Qc、Qd、γ、浓度比ϕ/ϕCMC | 液滴尺寸 | 468 | MAPE=3.9% | [ |
DNN Deep-FM | Ca、通道夹角、Qd/Qc、μd/μc | 液滴尺寸 | 298 | MRE=4.09% | [ |
ANN | 饱和度、Qc、Qd、通道夹角、W | 颗粒尺寸 | 71 | R2=0.89 | [ |
表2 应用于微分散过程预测的人工智能模型
Table 2 Artificial intelligence models applied to the prediction of microdispersion processes
模型类型 | 输入特征 | 输出目标 | 数据量 | 尺寸预测准确性 | 文献 |
---|---|---|---|---|---|
ANN | Red、Rec、Cac、Cad | 液滴尺寸 | 742 | R2≈1 | [ |
ANFIS | Ca、μd/μc、Qd/Qc | 液滴尺寸 | 36 | R2=0.92 | [ |
ANFIS | 狭缝宽度Or、Qc、Qd、μc、μd、ρc | 液滴尺寸 | 51 | R2=0.96 | [ |
ANN | 聚合物浓度、Qc、Qd、微通道组合形式 | 液滴或颗粒尺寸 | 223 | R2=0.971 | [ |
ANN | Ca、Qd/Qc、连续相通道宽度Wc、分散相通道宽度Wd、Or、缩口长度L、下游通道宽度Wm、通道深度H | 液滴尺寸 生成频率 | 998 | R2=0.893(滴状流) R2=0.966(射流) | [ |
DNN | Wed、Wec、Ca、Qd/Qc、Wd、Wc、Wm、αlm、αgm | 气泡尺寸 | 854 | MRE=2.02% | [ |
BRANN XGBoost | Qc、Qd、γ、浓度比ϕ/ϕCMC | 液滴尺寸 | 468 | MAPE=3.9% | [ |
DNN Deep-FM | Ca、通道夹角、Qd/Qc、μd/μc | 液滴尺寸 | 298 | MRE=4.09% | [ |
ANN | 饱和度、Qc、Qd、通道夹角、W | 颗粒尺寸 | 71 | R2=0.89 | [ |
图3 贝叶斯优化和深度神经网络结合的两步算法优化微流控平台中纳米颗粒的合成[54]
Fig. 3 Two-step algorithm combining BO and DNN to optimize nanoparticle synthesis in microfluidic platforms[54]
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