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Corrosion Inhibition Study Of Carboxymethyl Celluloseionic Liquid Via Electrochemical And Machine Learning Technique

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dc.contributor.authorAdi Hafizamri Ariffin
dc.contributor.authorWan Mohd Norsani Wan Nik
dc.contributor.authorSamsuri Abdullah
dc.contributor.authorMohd Ikmar Nizam Mohamad Isa
dc.contributor.authorVincent Izionworu
dc.contributor.authorMohammad Fakhratul Ridwan Zulkifli
dc.date.accessioned2024-09-05T17:05:08Z
dc.date.available2024-09-05T17:05:08Z
dc.date.issued2024
dc.date.submitted2024-8-31
dc.descriptionMalaysian Journal of Analytical Sciences, Volume 28 Issue 2 Page (265–276)
dc.description.abstractCorrosion is a natural phenomenon defined as the deterioration of a substance or its properties due to interactions between the substance and the environment. Prolonged exposure to corrosive environment had negative consequences, including increased repair and maintenance costs, decreased structural integrity, and fatalities. An approach to address the issue is to use a corrosion inhibitor. Numerous inhibitors have lately been developed or made accessible on the market. However, some could be dangerous or contain of volatile organic compounds (VOCs). Our study introduces carboxymethyl cellulose (CMC) mixed with 1-ethyl-3- methylimidazolium acetate ([EMIM][Ac]) ionic liquid, also known as CIL, as a corrosion inhibitor on mild steel in seawater. The functional group of combined CIL was examined using Fourier transform infrared spectroscopy (FTIR). The study employed mild steel specimens immersed in varying concentrations of CIL, subject to electrochemical impedance spectroscopy (EIS) measurements at different temperatures. The obtained result of EIS measurement were analyzed to calculate corrosion inhibition efficiency (IE) of CIL. 46 of electrochemical data were fed into a machine learning technique to forecast the effectiveness of the inhibition. Results indicate when inhibition concentration rises, so does inhibition efficiency (IE). The inhibitory efficiency of CIL decreased as the temperature of the test solution rose. At an ambient temperature of 950 ppm, an IE result of 83% was recorded. Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scale Conjugate Gradient (SCG) training algorithms were compared via Neural Network Fitting Tool (NNTool). LM was found to be the best backpropagation training algorithm, providing the highest regression value (R) of 0.907 and the lowest mean square error (MSE) of 0.006 when compared to BR and SCG. With R value closer to 1 and MSE close to 0, the use of Artificial Neural Networks (ANN) appears to offer a new insight in predicting methods with the goal of easing the hassle and time-consuming of experimental work.
dc.identifier.citationAdi Hafizamri Ariffin, Wan Mohd Norsani Wan Nik , Samsuri Abdullah, Mohd Ikmar Nizam Mohamad Isa , Vincent Izionworu, and Mohammad Fakhratul Ridwan Zulkifli Corrosion Inhibition Study Of Carboxymethyl Celluloseionic Liquid Via Electrochemical And Machine Learning Technique. (2024). Malaysian Journal of Analytical Sciences, 28(2), 265–276.
dc.identifier.epage276
dc.identifier.issn1394-2506
dc.identifier.issue2
dc.identifier.other2334-93
dc.identifier.spage265
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/22505
dc.identifier.urihttps://mjas.analis.com.my/mjas/v28_n2/pdf/Ariffin_28_2_3.pdf
dc.identifier.volume28
dc.language.isoen_US
dc.publisherPERSATUAN SAINS ANALISIS MALAYSIA
dc.relation.ispartofMalaysian Journal of Analytical Sciences
dc.relation.issn1394-2506
dc.relation.journalMalaysian Journal of Analytical Sciences
dc.subjectcarboxymethyl cellulose
dc.subjectcorrosion inhibitor
dc.subjectelectrochemical
dc.subjectionic liquid
dc.subjectmachine learning
dc.titleCorrosion Inhibition Study Of Carboxymethyl Celluloseionic Liquid Via Electrochemical And Machine Learning Technique
dc.title.alternativeKajian Perencatan Kakisan Menggunakan Karboksimetil Selulosa-Cecairan Ionik Melalui Teknik Elektrokimia dan Pembelajaran Mesin
dc.typetext::journal::journal article::research article
dspace.entity.typePublication
oaire.citation.endPage276
oaire.citation.issue2
oaire.citation.startPage265
oaire.citation.volume28
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliationUniversiti Sains Islam Malaysia
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#

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