A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques-Reference-Cited by-同舟云学术

A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques

Published:2024 Issue:16 Volume:14 Page:11157-11168
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Pham Thanh Hai¹²³^ORCID,Le Phung K.¹²,Son Do Ngoc¹²^ORCID

Affiliation:

1. Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Ho Chi Minh City, Vietnam

3. Vietnam Institute for Tropical Technology and Environmental Protection, 57A Truong Quoc Dung Street, Phu Nhuan District, Ho Chi Minh City, Vietnam

Abstract

An advanced machine learning workflow integrating the gradient boosting decision tree (GB) algorithm and the permutation feature importance (PFI) technique has been proposed to predict the corrosion inhibition efficiency (IE) of organic compounds.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D4RA02159B

Reference62 articles.

1. Green inhibitors for steel corrosion in acidic environment: state of art

2. Application of corrosion inhibitors for steels in acidic media for the oil and gas industry: A review

3. Corrosion inhibitors for carbon steel: A review

4. Corrosion inhibition of steel using different families of organic compounds: Past and present progress

5. Recent developments in sustainable corrosion inhibitors: design, performance and industrial scale applications

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