Artificial Intelligence and Machine Learning in Corrosion Research-Reference-Cited by-同舟云学术

Artificial Intelligence and Machine Learning in Corrosion Research

Published:2023-06-30 Issue: Volume: Page:1-23
ISSN:2327-5448
Container-title:Handbook of Research on Corrosion Sciences and Engineering
language:
Short-container-title:

Author:

Anadebe Valentine Chikaodili¹^ORCID,Chukwuike Vitalis Ikenna²,Okoye Chukwunonso Chukwuzuluoke³,Guo Lei⁴,Barik Rakesh Chandra⁵

Affiliation:

1. Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute, Karaikudi, India & Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

2. Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute, Karaikudi, India & Department of Industrial Chemistry, Federal University of Medical Science, Uburu, Nigeria

3. Department of Chemical Engineering, Nnamdi Azikwe University, Awka, Nigeria

4. School of Materials and Chemical Engineering, Tongren University, Tongren, China

5. Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute, India & Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

Abstract

Currently, the world is gradually moving to a digital world with the advent of artificial intelligence and machine learning models. The invention of these algorithms is a very big development for society. Artificial intelligence is a branch of science and engineering discipline concerned with teaching machines to think fast and operate like humans. Machine learning is concerned with using computer software to analyse complex non-linear tasks without the need for complex programming. In the oil and gas sector, the use of artificial intelligence and machine learning to appraise the efficiency of an inhibitor is gradually gaining attention in corrosion inhibition and pipeline integrity assessment. However, the proposed chapter will discuss more on the role of artificial intelligence and machine learning in corrosion related research.

Publisher

IGI Global

Reference50 articles.

1. Neural network modelling of high pressure CO2 corrosion in pipeline steels

2. Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming

3. Machine learning modeling of time-dependent corrosion rates of carbon steel in presence of corrosion inhibitors

4. Nyquist plots prediction using neural networks in corrosion inhibition of steel by schiff base.;K.Akbarzade;Iranian Journal of Chemistry and Chemical Engineering,2018

5. A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process