Malaysian Journal of Analytical Sciences, Vol 28 No 2 (2024): 265 - 276

 

CORROSION INHIBITION STUDY OF CARBOXYMETHYL CELLULOSE-IONIC LIQUID VIA ELECTROCHEMICAL AND MACHINE LEARNING TECHNIQUE

 

(Kajian Perencatan Kakisan Menggunakan Karboksimetil Selulosa-Cecairan Ionik Melalui Teknik Elektrokimia dan Pembelajaran Mesin)

 

Adi Hafizamri Ariffin1, Wan Mohd Norsani Wan Nik1, Samsuri Abdullah1, Mohd Ikmar Nizam Mohamad Isa2, Vincent Izionworu3, and Mohammad Fakhratul Ridwan Zulkifli1*

 

1Marine Materials Research Group, Faculty of Ocean Engineering Technology, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

2Energy Storage Research, Advanced Materials Team, Ionic & Kinetic Materials Research (iKMaR) Laboratory, Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia

3Department of Chemical/Petrochemical Engineering, Rivers State University, Nkpolu-Oroworukwo, Port Harcourt, Nigeria

 

*Corresponding author: fakhratulz@umt.edu.my

 

Received: 5 October 2023; Accepted: 25 February 2024; Published:  29 April 2024

 

 

Abstract

Corrosion 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.

 

Keywords: carboxymethyl cellulose, corrosion inhibitor, electrochemical, ionic liquid, machine learning

 

 

Abstrak

Kakisan adalah fenomena semula jadi yang ditakrifkan sebagai kemerosotan bahan atau sifatnya akibat interaksi antara bahan dan persekitaran. Pendedahan yang berpanjangan kepada persekitaran yang mengkakis memberi kesan yang negatif, termasuk peningkatan kos pembaikan dan penyelenggaraan, penurunan integriti keatas struktur dan mengakibatkan kemusnahan. Salah satu penyelesaian kepada isu ini ialah menggunakan perencat kakisan. Banyak perencat kakisan telah dibangunkan atau boleh didapati di pasaran. Namun, sebahagian daripadanya mungkin berbahaya atau mengandungi banyak sebatian organik meruap (VOC). Kajian ini memperkenalkan karboksimetil selulosa (CMC) dalam 1-etil-3-metilimidazolium asetat ([EMIM][Ac]) cecair ionik, juga dikenali sebagai CIL, sebagai perencat kakisan pada keluli lembut dalam air laut untuk menangani isu alam sekitar. Kumpulan berfungsi kimia hasil dr campuran CIL ini telah diperiksa menggunakan spektroskopi inframerah transformasi Fourier (FTIR). Kajian ini menggunakan spesimen keluli lembut yang direndam dalam kepekatan CIL yang berbeza, diuji menggunakan spektroskopi impedans elektrokimia (EIS) pada suhu yang berbeza. data pengukuran EIS yang diperolehi dianalisis untuk mengira kecekapan perencatan kakisan (IE) CIL. 46 data elektrokimia digunakan di dalam teknik pembelajaran mesin (ML) untuk meramalkan keberkesanan perencatan. Keputusan menunjukkan bahawa apabila kepekatan perencatan meningkat, kecekapan perencatan juga menunjukkan peningkatan. Kecekapan perencatan CIL menurun apabila suhu larutan ujian meningkat. Pada kepekatan 950 ppm pada suhu ambang, keputusan kecekapan perencatan dengan peratusan 83% telah direkodkan. Algoritma latihan Levenberg-Marquardt (LM), Bayesian Regulisasi (BR), dan Gradien Konjugasi Skala (SCG) telah dibandingkan melalui aplikasi Ketepatan Jaringan Saraf (NNtool). LM didapati sebagai algoritma latihan rambatan belakang terbaik, memberikan nilai regresi (R) tertinggi 0.9072580 dan ralat min kuasa dua (MSE) terendah sebanyak 0.0054787 jika dibandingkan dengan BR dan SCG. Dengan nilai R yang menghampiri 1 dan MSE menghampiri 0, penggunaan Rangkaian Saraf Tiruan (ANN) memberi pandangan baharu dalam kaedah peramalam dengan matlamat untuk mengurangkan kerumitan dan penghambatan kerja eksperimen yang memakan masa.

 

Kata kunci: karboksimetil selulosa, penghambat pengaratan, elektrokimia, cairan ionik, pembelajaran mesin

 

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