Can a computer “learn” nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes-Reference-Cited by-同舟云学术

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Can a computer “learn” nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes

Published:2022-06 Issue: Volume:1672 Page:463037
ISSN:0021-9673
Container-title:Journal of Chromatography A
language:en
Short-container-title:Journal of Chromatography A

Author:

Subraveti Sai Gokul^ORCID,Li Zukui,Prasad Vinay^ORCID,Rajendran Arvind^ORCID

Funder

Compute Canada

Natural Sciences and Engineering Research Council of Canada

University of Alberta

Canada First Research Excellence Fund

Publisher

Elsevier BV

Subject

Organic Chemistry,General Medicine,Biochemistry,Analytical Chemistry

Reference38 articles.

1. Tensorflow: a system for large-scale machine learning;Abadi;arXiv,2016

2. Automatic differentiation in machine learning: a survey;Baydin;arXiv,2018

3. The theory of chromatography;DeVault;J. Am. Chem. Soc.,1943

4. Understanding the difficulty of training deep feedforward neural networks;Glorot,2010

5. Preparative liquid chromatography;Guiochon;J. Chromatogr. A,2002

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