Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors

LT Zhu, XZ Chen, B Ouyang, WC Yan… - Industrial & …, 2022 - ACS Publications
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

[HTML][HTML] Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

M Kambara, S Kawaguchi, HJ Lee… - Japanese Journal of …, 2022 - iopscience.iop.org
Low-temperature plasma-processing technologies are essential for material synthesis and
device fabrication. Not only the utilization but also the development of plasma-related …

Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications

WH Chen, JS Wang, MH Chang, AT Hoang… - Energy Conversion and …, 2022 - Elsevier
Vertical axis wind turbines (VAWTs), so named because of their vertical axis of rotation, are
a sustainable, opportune, and versatile means of producing energy. Their operation is not …

Combining computational fluid dynamics, photon fate simulation and machine learning to optimize continuous-flow photocatalytic systems

GX de Oliveira, S Kuhn, HG Riella, C Soares… - Reaction Chemistry & …, 2023 - pubs.rsc.org
Photoredox catalysis is a well-established area with great potential for industrial
applications. The need for an optimum process with less waste generation and economic …

Rapid monitoring of indoor air quality for efficient HVAC systems using fully convolutional network deep learning model

S Shin, K Baek, H So - Building and Environment, 2023 - Elsevier
Indoor air quality (IAQ) monitoring technology is crucial for achieving optimized heating,
ventilation, and air conditioning (HVAC) strategies for efficient energy management. In this …

Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability …

T Yasuda, S Ookawara, S Yoshikawa… - Chemical Engineering …, 2023 - Elsevier
This study proposes a material discovery framework for porous materials to identify design
variable recipes and the corresponding material structures that can be utilized to improve …

Physics-informed deep learning for multi-species membrane separations

D Rehman, JH Lienhard - Chemical Engineering Journal, 2024 - Elsevier
Conventional continuum models for ion transport across polyamide membranes require
solving partial differential equations (PDEs). These models typically introduce a host of …

Intensification of catalytic reactors: a synergic effort of multiscale modeling, machine learning and additive manufacturing

M Bracconi - Chemical Engineering and Processing-Process …, 2022 - Elsevier
The intensification of catalytic reactors is expected to play a crucial role to address the
challenges that the chemical industry is facing in the transition to more sustainable …

From computational fluid dynamics to structure interpretation via neural networks: an application to flow and transport in porous media

A Marcato, G Boccardo, D Marchisio - Industrial & Engineering …, 2022 - ACS Publications
The modeling of flow and transport in porous media is of the utmost importance in many
chemical engineering applications, including catalytic reactors, batteries, and CO2 storage …

[HTML][HTML] Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

A Marcato, JE Santos, G Boccardo… - Chemical Engineering …, 2023 - Elsevier
The study of solute transport in porous media is of interest in many chemical engineering
systems. Some example applications include packed bed catalytic reactors, filtration …