4.7 Article

A general neural network model co-driven by mechanism and data for the reliable design of gas-liquid T-junction microdevices

Related references

Note: Only part of the references are listed.
Article Engineering, Chemical

Microdispersion of Gas or Water in an Anthraquinone Working Solution for the H2O2 Synthesis Process Intensification

Junjie Wang et al.

Summary: This study investigates the microdispersion performances of hydrogen, oxygen, or water in the anthraquinone working solution using an observation platform, aiming to provide reliable data for developing new microchemical processes. The influence of two-phase flow rate, viscosity, and channel size on bubble size in a circular microchannel was studied. A mathematical prediction model for gas-liquid and liquid-liquid microdispersion was developed, which can be used for future practical industrial system process intensification.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2023)

Article Multidisciplinary Sciences

Organic reaction mechanism classification using machine learning

Jordi Bures et al.

Summary: In this study, a deep neural network model is used to analyze kinetic data and automate mechanistic elucidation. The model can accurately identify various classes of mechanisms, including non-steady state mechanisms, and performs well even with data containing errors or limited time points. The model is freely available and is expected to contribute to the development of fully automated organic reaction discovery and development.

NATURE (2023)

Article Engineering, Chemical

Nature and characteristics of gas-liquid flow regimes in a micro-packed bed reactor

Wei Liu et al.

Summary: Micro-packed bed reactors (mu PBRs) have been successfully used in hydrogenation and oxidation reactions due to their high heat and mass transfer efficiency as well as excellent safety. However, the study of gas-liquid flow regimes in mu PBRs is still lacking due to the micro-scale limitation and complexity of capillary force. In this study, the flow regimes in a two-dimensional mu PBR were systematically investigated using a high-performance camera. Four typical flow regimes and their characteristics were observed, and the transition between flow regimes was analyzed. The effects of gas and liquid superficial velocities, liquid physical properties, and particle sizes on liquid spreading and pressure drop were also studied. Additionally, a correlation between churn flow and pseudo-static flow in mu PBRs was provided for the first time based on a summary of previous research.

AICHE JOURNAL (2023)

Article Engineering, Chemical

Deep learning-based tomographic imaging of ECT for characterizing particle distribution in circulating fluidized bed

Jian Li et al.

Summary: This article develops a deep learning-based tomographic imaging technique using electrical capacitance tomography (ECT) to characterize particle concentration distribution in a circulating fluidized bed (CFB). Experimental results demonstrate that the deep tomographic imaging technique of ECT can successfully measure particle distribution in both 60 mm and 100 mm pipes, showing good prediction and generality of the designed convolutional neural network (CNN) model. The experimental findings reveal a flow regime transformation from annular flow to core-annular flow and pneumatic conveying, which are highly affected by the fluidized gas flow rate and the initial bed height.

AICHE JOURNAL (2023)

Article Chemistry, Multidisciplinary

A general design procedure for gas-liquid Taylor flow T-junction microreactors

Yu Chang et al.

Summary: This study aims to develop general design rules for gas-liquid Taylor flow T-junction microreactors. Two major issues to address are the relationship between gas flow rate and pressure drop, and the bubble generation and hydrodynamics in different devices. A dimensionless relationship between gas pressure and flow rate is proposed, along with a model to predict bubble generation and investigate hydrodynamics. Lastly, a design procedure for gas-liquid Taylor flow T-junction microreactors is proposed.

REACTION CHEMISTRY & ENGINEERING (2023)

Review Engineering, Chemical

Reaction kinetics determination based on microfluidic technology

Zifei Yan et al.

Summary: This review comprehensively organizes the recent progress in microfluidic technology for measuring reaction kinetics, summarizing the kinetic modeling thoughts, determination methods, and essential regularities. It covers various reaction types and important innovations in microplatform.

CHINESE JOURNAL OF CHEMICAL ENGINEERING (2022)

Article Engineering, Chemical

Taylor Bubble Generation Rules in Liquids with a Higher Viscosity in a T-Junction Microchannel

Lin Sheng et al.

Summary: This study investigates the generation rules and behavior of bubbles in liquids with higher viscosity through experiments and mathematical modeling. The results show that the bubble formation process in higher viscous liquids consists of three stages: expansion, shrinking, and necking, and the generation of satellite bubbles as a new bubble generation performance is observed.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2022)

Article Engineering, Chemical

3D multiphase flow simulation of Marangoni convection on reactive absorption of CO2 by monoethanolamine in microchannel

Shuai Chen et al.

Summary: A multiphase flow 3D numerical simulation method employing the coupled volume of fluid (VOF) and level set model is established to study the reactive absorption of CO2 by the monoethanolamine (MEA) aqueous solution in a falling film microchannel. The enhancement effect of the Marangoni convection in this reactive absorption process is analyzed, and the influence of different MEA concentrations on CO2 absorption is investigated. The appropriate MEA concentration for absorption enhanced by the Marangoni convection is determined.

CHINESE JOURNAL OF CHEMICAL ENGINEERING (2022)

Article Engineering, Chemical

Predictive analysis of gas hold-up in bubble column using machine learning methods

Sumit R. Hazare et al.

Summary: A machine learning-based data-driven methodology is proposed to predict the gas hold-up in a gas-liquid bubble column. The performance of various machine learning methods is compared, and the extra tree method is found to be the most suitable for gas hold-up prediction.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2022)

Article Engineering, Chemical

Liquid-liquid colliding micro-dispersion and general scaling laws in novel T-junction microdevices

Jing Song et al.

Summary: The impact of colliding microdispersion on droplet generation has been investigated, and novel T-junction microdevices and scaling laws for droplet size have been proposed. Moreover, the flow regime distribution and liquid-liquid flow pattern under high Weber number were explored.

CHEMICAL ENGINEERING SCIENCE (2022)

Article Engineering, Chemical

Mechanism and modeling of Taylor bubble generation in viscous liquids via the vertical squeezing route

Lin Sheng et al.

Summary: This study focuses on the vertical squeezing bubble generation mechanism in viscous liquids in a T-junction microchannel. The results show the stages of retraction, expansion, shrinking, and necking in the vertical squeezing route, with a new formation stage, the retraction stage, compared to horizontal squeezing. The influence of operating conditions on the generation mechanism and the different formation mechanisms of satellite bubbles were investigated. Models for the generation frequency and bubble size were proposed based on the findings.

CHEMICAL ENGINEERING SCIENCE (2022)

Article Thermodynamics

Reliable predictions of bubble departure frequency in subcooled flow boiling: A machine learning-based approach

Yichuan He et al.

Summary: This study predicts bubble departure frequency in subcooling flow boiling using machine learning-based approaches. By comparing a consolidated dataset and nine regression models, it is found that the XGBoost model performs the best in predicting bubble departure frequency, providing a reliable tool.

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER (2022)

Article Biochemical Research Methods

Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets

Karl Gardner et al.

Summary: This study develops an automated droplet and cell detector using YOLOv3 and YOLOv5 object detectors, and demonstrates its capability in real-time cell encapsulation optimization.

LAB ON A CHIP (2022)

Article Biochemical Research Methods

Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach

Loic Chagot et al.

Summary: The control of droplet formation and size using microfluidic devices is essential for laboratory and industrial applications. This study investigates how surfactants can improve stability and control droplet size in silicone oil. Two data-driven models, based on Bayesian regularised artificial neural network and XGBoost, accurately predict droplet size as a function of flow rates and surfactant properties. Compared to semi-empirical models, the data-driven models have higher accuracy. The models were also trained with simplified inputs, showing good prediction results. Additionally, a synthetic data set was generated, proving the feasibility of using this method in future lab on a chip applications.

LAB ON A CHIP (2022)

Review Biochemical Research Methods

Machine learning for microfluidic design and control

David McIntyre et al.

Summary: Microfluidics is a mature field with wide-ranging applications. However, the complexity of designing and controlling microfluidic devices hinders its adoption. Integration of machine learning with microfluidics can overcome these barriers and enable broader applications.

LAB ON A CHIP (2022)

Article Multidisciplinary Sciences

Machine learning enables design automation of microfluidic flow-focusing droplet generation

Ali Lashkaripour et al.

Summary: Microfluidic devices based on droplet generation show great potential in various life science applications, but the lack of predictive understanding makes the design process iterative and resource-intensive. The DAFD tool utilizes machine learning algorithms to predict performance and automate the design of flow-focusing droplet generators, reducing the need for design iterations.

NATURE COMMUNICATIONS (2021)

Article Engineering, Chemical

Geometric Effect on Gas-Liquid Bubbly Flow in Capillary-Embedded T-Junction Microchannels

Yuchao Chen et al.

Summary: This study investigated gas-liquid bubbly flows in T-junction microdevices with different microstructures and found that the gas injector diameter has a significant influence on bubble dispersion. When the gas injector diameter is small and the gas-phase flow rate is lower than the liquid-phase flow rate, bubbly microflows are easily formed. Furthermore, designing a step T-junction microchannel with a small capillary and a narrow slit can improve bubble generation ability.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2021)

Article Engineering, Chemical

High-frequency formation of bubble with short length in a capillary embedded step T-junction microdevice

Lin Sheng et al.

Summary: This work reports the generation of short bubbles with high frequency in a capillary embedded step T-junction microdevice for gas-liquid process intensification. The specific surface area of the generated gas-liquid microdispersion system is larger than 10,400 m(2)/m(3), and the bubble formation mechanism is mainly driven by the relatively higher pressure drop providing a much higher breakup force for the squeezing flow.

AICHE JOURNAL (2021)

Article Engineering, Chemical

General rules of bubble formation in viscous liquids in a modified step T-junction microdevice

Lin Sheng et al.

Summary: The study systematically investigated the general rules of bubble formation in viscous liquids, revealing that the formation frequencies can be divided into four periods and the bubble diameter is scaled perfectly with the gas-liquid flow rate ratio and the capillary number.

CHEMICAL ENGINEERING SCIENCE (2021)

Article Biochemical Research Methods

Machine learning assisted fast prediction of inertial lift in microchannels

Jinghong Su et al.

Summary: This study presents a fast numerical algorithm and machine learning techniques for analysis and design of inertial microfluidic devices, generating a database of inertial lift forces in straight microchannels and developing a machine learning assisted model to predict particle trajectories quickly and accurately.

LAB ON A CHIP (2021)

Review Biochemical Research Methods

Droplet-based microreactor for the production of micro/nano-materials

Linbo Liu et al.

ELECTROPHORESIS (2020)

Article Engineering, Aerospace

Numerical and Experimental Study of the Squeezing-to-Dripping Transition in a T-Junction

S. Arias et al.

MICROGRAVITY SCIENCE AND TECHNOLOGY (2020)

Article Engineering, Chemical

Effects of the Gas Feed on Bubble Formation in a Microfluidic T-Junction: Constant-Pressure versus Constant-Flow-Rate Injection

Sheng Mi et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2019)

Review Green & Sustainable Science & Technology

Overview of micro-channel design for high heat flux application

Nor Haziq Naqiuddin et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Engineering, Environmental

A capillary-assembled micro-device for monodispersed small bubble and droplet generation

Y. K. Li et al.

CHEMICAL ENGINEERING JOURNAL (2016)

Review Chemistry, Multidisciplinary

Liquid phase oxidation chemistry in continuous-flow microreactors

Hannes P. L. Gemoets et al.

CHEMICAL SOCIETY REVIEWS (2016)

Article Biochemistry & Molecular Biology

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

Evan Z. Macosko et al.

Article Engineering, Chemical

Bubble formation and breakup dynamics in microfluidic devices: A review

Taotao Fu et al.

CHEMICAL ENGINEERING SCIENCE (2015)

Article Engineering, Chemical

The Effect of System Pressure on Gas-Liquid Slug Flow in a Microchannel

Chaoqun Yao et al.

AICHE JOURNAL (2014)

Article Computer Science, Interdisciplinary Applications

Numerical simulation of bubble generation in a T-junction

S. Arias et al.

COMPUTERS & FLUIDS (2012)

Article Biochemical Research Methods

Microbubble generation in a co-flow device operated in a new regime

Elena Castro-Hernandez et al.

LAB ON A CHIP (2011)

Article Engineering, Environmental

Gas-liquid flow in T-junction microfluidic devices with a new perpendicular rupturing flow route

J. Tan et al.

CHEMICAL ENGINEERING JOURNAL (2009)

Article Engineering, Chemical

μ-PIV study of the formation of segmented flow in microfluidic T-junctions

Volkert van Steijn et al.

CHEMICAL ENGINEERING SCIENCE (2007)

Article Biochemical Research Methods

Formation of droplets and bubbles in a microfluidic T-junction - scaling and mechanism of break-up

P Garstecki et al.

LAB ON A CHIP (2006)