机器学习辅助在双 T 型微通道内定制双乳液

IF 2.3 4区 工程技术 Q2 INSTRUMENTS & INSTRUMENTATION
Saeed Ghasemzade Bariki, Salman Movahedirad, Mohadeseh Babaei layaei
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引用次数: 0

摘要

微通道中双乳液或核/壳微滴的形成对各种化学应用至关重要,但传统上依赖于昂贵且耗时的实验室方法。在这方面,采用了计算流体动力学(CFD)和人工神经网络(ANN)技术。本研究利用由 180 个实验数据点和 CFD 数据点组成的两个数据集开发了 ANN 模型,用于预测双 T 微通道中的壳厚度和双乳液大小之间的关系。评估这种关系涉及分析各种输入因素,包括核心、外壳和连续相的毛细管数、韦伯数(情况 A)和雷诺数(情况 B)。在 12 种训练算法和 4 种激活函数中,与线性激活函数(Poslin 和 Purelin)相比,采用西格码激活函数(Tansig 和 Logsig)的 Levenberg-Marquardt 算法(LM)获得了最高的预测精度。此外,与仅使用毛细管数、韦伯数和雷诺数训练的模型相比,使用毛细管数和韦伯数组合训练的 ANN 模型的预测准确性显著提高。最佳的神经网络结构为:情况 A(tansig 和 logsig)为 [10 5] 个神经元,情况 B(tansig)为 [8] 个神经元,其决定系数 (R2) 分别为 0.99 和 0.98。从 R2、MSE、RMSE、AAD、%AARD 和计算时间等统计指标来看,这些模型表现出了高精度和有效的泛化能力。此外,这些模型在训练数据集中的泛化能力也进一步证实了它们的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning-aided tailoring of double-emulsions within double-T microchannel

Machine learning-aided tailoring of double-emulsions within double-T microchannel

Machine learning-aided tailoring of double-emulsions within double-T microchannel

The formation of double-emulsions or core/shell microdroplets in microchannels, essential for various chemical applications, traditionally relies on costly and time-consuming laboratory methods. In this regard, computational fluid dynamics (CFD) and artificial neural network (ANN) techniques were employed. The present study developed ANN models to predict the relationship between shell thickness and double-emulsion size in a double-T microchannel, using two datasets comprising 180 experimental and CFD data points. Assessing this relationship involved analyzing various input factors, including the Capillary, Weber (case A), and Reynolds numbers (case B) of the core, shell, and continuous phases. Among twelve training algorithms and four activation functions, the Levenberg–Marquardt (LM) algorithm with sigmoidal activation functions (Tansig and Logsig), in contrast to the linear activation functions (Poslin and Purelin), achieved the highest predictive accuracy. Additionally, the predictive accuracy of ANN models was found to be significantly improved when trained using a combination of capillary and Weber numbers, as opposed to models trained only using capillary, Weber, and Reynolds numbers. The optimal neural network architectures were [10 5] neurons for case A (tansig and logsig) and [8] neurons for case B (tansig), yielding coefficients of determination (R2) of 0.99 and 0.98, respectively. These models demonstrated high precision and effective generalization, evidenced by statistical measures such as R2, MSE, RMSE, AAD, %AARD, and computational time. Moreover, their ability to generalize within the training dataset further substantiates their predictive capacity.

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来源期刊
Microfluidics and Nanofluidics
Microfluidics and Nanofluidics 工程技术-纳米科技
CiteScore
4.80
自引率
3.60%
发文量
97
审稿时长
2 months
期刊介绍: Microfluidics and Nanofluidics is an international peer-reviewed journal that aims to publish papers in all aspects of microfluidics, nanofluidics and lab-on-a-chip science and technology. The objectives of the journal are to (1) provide an overview of the current state of the research and development in microfluidics, nanofluidics and lab-on-a-chip devices, (2) improve the fundamental understanding of microfluidic and nanofluidic phenomena, and (3) discuss applications of microfluidics, nanofluidics and lab-on-a-chip devices. Topics covered in this journal include: 1.000 Fundamental principles of micro- and nanoscale phenomena like, flow, mass transport and reactions 3.000 Theoretical models and numerical simulation with experimental and/or analytical proof 4.000 Novel measurement & characterization technologies 5.000 Devices (actuators and sensors) 6.000 New unit-operations for dedicated microfluidic platforms 7.000 Lab-on-a-Chip applications 8.000 Microfabrication technologies and materials Please note, Microfluidics and Nanofluidics does not publish manuscripts studying pure microscale heat transfer since there are many journals that cover this field of research (Journal of Heat Transfer, Journal of Heat and Mass Transfer, Journal of Heat and Fluid Flow, etc.).
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