Process optimization using machine learning enhanced design of experiments (DOE): ranibizumab refolding as a case study-Reference-Cited by-同舟云学术

Process optimization using machine learning enhanced design of experiments (DOE): ranibizumab refolding as a case study

Published:2023 Issue:3 Volume:8 Page:592-603
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Nikita Saxena¹,Sharma Rashmi²,Fahmi Jawad¹,Rathore Anurag Singh¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India

2. School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India

Abstract

An application of ML enhanced DOE has been demonstrated to capture the process nonlinearity more effectively. The proposed approach is applied for modelling and optimization of protein refolding, resulting in 3% improved prediction efficiency.

Funder

Department of Biotechnology, Ministry of Science and Technology, India

Council of Scientific and Industrial Research, India

Publisher

Royal Society of Chemistry (RSC)

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RE/D2RE00440B

Reference51 articles.

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4. Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement

5. Folding and association of proteins

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