基于人工智能的微分散基础研究

doi:10.11949/0438-1157.20231406

摘要/Abstract

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备受关注，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。

关键词: 微流体学, 分散, 模型, 算法, 人工智能

Abstract:

Microdispersion is an important part of micro chemical engineering technology. The complexity of equipment and processes imposes many limitations on its related research. Under the guidance of traditional thinking, the basic research on microdispersion follows the approach of “design-experiment-modeling”, resulting in slow progress. In recent years, artificial intelligence (AI) methods have received increasing attention in chemical engineering due to their powerful recognition and regression capabilities. AI-assisted basic research on microdispersion is conducive to forming a new paradigm for understanding micro chemical engineering processes, promoting the development of micro chemical engineering technology. This article introduces the general idea of AI methods and their applicability to microdispersion investigation, reviews the developments of AI technology in microscopic image recognition, droplet and bubble dispersion size prediction, and microdispersion process control and optimization, and offers insights into the prospective directions of micro chemical engineering based on AI technology.

Key words: microfluidics, dispersion, model, algorithm, artificial intelligence

中图分类号:

TQ 021.1

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, 2024, 75(4): 1096-1104.

Mengqi LIU, Kai WANG, Guangsheng LUO. Fundamental research on microdispersion based on artificial intelligence[J]. CIESC Journal, 2024, 75(4): 1096-1104.

图/表 5

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几何结构	半经验关联式	适用范围	文献
T型微通道	d/W =2.7(Ca_d/Ca_c)^0.19	0.0003<Ca_c<0.1	[23]
毛细管嵌入T型微通道	d/W = 0.75(Q_d/Q_c)^1/3Ca_c^–1/5	0.002<Ca_c<0.01	[24]
毛细管嵌入阶梯T型微通道	d/W = 1.10(Q_d/Q_c)^0.49(μ_d/μ_c)^0.02	Ca_c>0.07	[25]
聚焦流微通道	d/W = 0.88(Q_d/Q_c)^0.18Ca_d^–0.15	0.002<Ca_c<0.223	[26]
聚焦流微通道	d/W = 1.29(Q_d/Q_c)^0.17Ca_c^–0.1	0.00065<Ca_c<0.2	[27]

几何结构	半经验关联式	适用范围	文献
T型微通道	d/W =2.7(Ca_d/Ca_c)^0.19	0.0003<Ca_c<0.1	[23]
毛细管嵌入T型微通道	d/W = 0.75(Q_d/Q_c)^1/3Ca_c^–1/5	0.002<Ca_c<0.01	[24]
毛细管嵌入阶梯T型微通道	d/W = 1.10(Q_d/Q_c)^0.49(μ_d/μ_c)^0.02	Ca_c>0.07	[25]
聚焦流微通道	d/W = 0.88(Q_d/Q_c)^0.18Ca_d^–0.15	0.002<Ca_c<0.223	[26]
聚焦流微通道	d/W = 1.29(Q_d/Q_c)^0.17Ca_c^–0.1	0.00065<Ca_c<0.2	[27]

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	Re_d、Re_c、Ca_c、Ca_d	液滴尺寸	742	R²≈1	[39]
ANFIS	Ca、μ_d/μ_c、Q_d/Q_c	液滴尺寸	36	R²=0.92	[40]
ANFIS	狭缝宽度Or、Q_c、Q_d、μ_c、μ_d、ρ_c	液滴尺寸	51	R²=0.96	[41]
ANN	聚合物浓度、Q_c、Q_d、微通道组合形式	液滴或颗粒尺寸	223	R²=0.971	[42]
ANN	Ca、Q_d/Q_c、连续相通道宽度W_c、分散相通道宽度W_d、Or、缩口长度L、下游通道宽度W_m、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）	[43]
DNN	We_d、We_c、Ca、Q_d/Q_c、d_l 、d_g、d_m、α_l_m、α_gm	气泡尺寸	854	MRE=2.02%	[44]
BRANN XGBoost	Q_c、Q_d、γ、浓度比ϕ/ϕ_CMC	液滴尺寸	468	MAPE=3.9%	[45]
DNN Deep-FM	Ca、通道夹角、Q_d/Q_c、μ_d/μ_c	液滴尺寸	298	MRE=4.09%	[46]
ANN	饱和度、Q_c、Q_d、通道夹角、W	颗粒尺寸	71	R²=0.89	[47]

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	Re_d、Re_c、Ca_c、Ca_d	液滴尺寸	742	R²≈1	[39]
ANFIS	Ca、μ_d/μ_c、Q_d/Q_c	液滴尺寸	36	R²=0.92	[40]
ANFIS	狭缝宽度Or、Q_c、Q_d、μ_c、μ_d、ρ_c	液滴尺寸	51	R²=0.96	[41]
ANN	聚合物浓度、Q_c、Q_d、微通道组合形式	液滴或颗粒尺寸	223	R²=0.971	[42]
ANN	Ca、Q_d/Q_c、连续相通道宽度W_c、分散相通道宽度W_d、Or、缩口长度L、下游通道宽度W_m、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）	[43]
DNN	We_d、We_c、Ca、Q_d/Q_c、d_l 、d_g、d_m、α_l_m、α_gm	气泡尺寸	854	MRE=2.02%	[44]
BRANN XGBoost	Q_c、Q_d、γ、浓度比ϕ/ϕ_CMC	液滴尺寸	468	MAPE=3.9%	[45]
DNN Deep-FM	Ca、通道夹角、Q_d/Q_c、μ_d/μ_c	液滴尺寸	298	MRE=4.09%	[46]
ANN	饱和度、Q_c、Q_d、通道夹角、W	颗粒尺寸	71	R²=0.89	[47]

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