Modeling of pressure drop in reverse osmosis feed channels using multilayer artificial neural networks-Reference-Cited by-同舟云学术

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Modeling of pressure drop in reverse osmosis feed channels using multilayer artificial neural networks

Published:2020-07 Issue: Volume:159 Page:146-156
ISSN:0263-8762
Container-title:Chemical Engineering Research and Design
language:en
Short-container-title:Chemical Engineering Research and Design

Author:

Gu Jianghang,Luo Jiu,Li Mingheng,Huang Chunyan,Heng Yi

Funder

“Young Overseas High-Level Talents Introduction Plan” funding of China

Zhujiang Talent Program of Guangdong Province

National Natural Science Foundation of China

the Young Talents Program of CUFE

Publisher

Elsevier BV

Subject

General Chemical Engineering,General Chemistry

Reference38 articles.

1. Experimental and numerical characterization of the water flow in spacer-filled channels of spiral-wound membranes;Bucs;Water Res.,2015

2. Effect of different commercial feed spacers on biofouling of reverse osmosis membrane systems: a numerical study;Bucs;Desalination,2014

3. Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning;Carlberg;J. Comput. Phys.,2019

4. A cyber-secure control-detector architecture for nonlinear processes;Chen;AlChE J.,2020

5. Advantages and disadvantages of techniques used for wastewater treatment;Crini;Environ. Chem. Lett.,2019

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multiscale Analysis of Permeable and Impermeable Wall Models for Seawater Reverse Osmosis Desalination;Separations;2023-02-15

2. Supercomputing and machine learning-aided optimal design of high permeability seawater reverse osmosis membrane systems;Science Bulletin;2023-02

3. Establishment of MLP models for the three-dimensional transport process in reverse osmosis desalination based onhigh-throughput computing;Chinese Science Bulletin;2023-02-01

4. Surrogate modeling of pressure loss & mass transfer in membrane channels via coupling of computational fluid dynamics and machine learning;Desalination;2023-02

5. A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants;Membranes;2022-02-09

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