Experimental and modelling waste rice husk ash as a novel green corrosion inhibitor under acidic environment

Agus Paul Setiawan Kaban, Wahyu Mayangsari, Mochammad Syaiful Anwar, Ahmad Maksum, Rini Riastuti, Taufik Aditiyawarman, Johny Wahyuadi Soedarsono

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The ubiquitous role of green corrosion inhibitors and their adsorption nature unveils deeper insight into forming inhibitor-metal binding complexes. This work studied mild steel's corrosion and mitigation behaviors under HCl 1 M solution with the newly waste rice husks inhibitor at the 298 K temperature. The liquid smoke (LS) inhibitor is synthesized and prepared from the waste rice husks with the help of the Pyrometallurgy process. The anti-corrosion performance was evaluated using Electrochemical Impedance Spectroscopy (EIS), Raman Spectroscopy, Scanning Electronic Microscopy and Machine Learning. Moreover, the time to corrosion method is considered to predict inhibitor duration at their respective concentration. Total Phenolic Content (TPC) advanced characterization was used to determine the amount of phenolic ligand's molecule to donate its electron to 3d orbital of Fe. The inhibition efficiency stood at 97.73% of 80 ppm solution with approximate inhibition effectiveness for 17 days. The Raman Spectroscopy results show that LS comprises phenolic, cellulose, and complex heterocyclic functional groups. The latter becomes the key factor influencing the strength of inhibitors’ adsorption. The Raman Shift at approximately 3500–4000, 3000, 1800–2000, 800–1000 nm correlated to the presence of –OH, cellulose, C=O, and complex molecules. Furthermore, the experimental and modelling results are in good agreement. The Multicollinear Matrix shows a strong correlation between impedance and Bode phase angle. This work can be used as practical guidance to analyze and evaluate the effectiveness of inhibitors using intelligence systems and laboratory activities.

Original languageEnglish
Pages (from-to)4225-4234
Number of pages10
JournalMaterials Today: Proceedings
Volume62
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Corrosion Machine Learning
  • Corrosion Modelling
  • Green corrosion inhibitor
  • Liquid smoke corrosion inhibitor
  • Waste rice husks

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