Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction
The present work compared the predictive abilities of response surface methodology (RSM) and adaptive neuro fuzzy inference systems (ANFIS) in modeling of carbon steel corrosion inhibition by Thaumatococcus daniellii leaf extract (TDE). Thaumatococcus daniellii leaf extract was examined using Fourie...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Applied Surface Science Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666523923000910 |
| _version_ | 1797389396026064896 |
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| author | Fidelis Ebunta Abeng Igwe O. Ewona |
| author_facet | Fidelis Ebunta Abeng Igwe O. Ewona |
| author_sort | Fidelis Ebunta Abeng |
| collection | DOAJ |
| description | The present work compared the predictive abilities of response surface methodology (RSM) and adaptive neuro fuzzy inference systems (ANFIS) in modeling of carbon steel corrosion inhibition by Thaumatococcus daniellii leaf extract (TDE). Thaumatococcus daniellii leaf extract was examined using Fourier Transform Infrared Spectroscopy (FTIR) methods which revealed that the phytochemical components present in TDE has high-value flavonoids, tannins, and dominating functional groups needed to support long-term corrosion inhibition. Error indices revealed the superiority of ANFIS (R2 = 0.99443) and RSM (R2 = 0.98932) in predicting the inhibition efficiency of carbon steel corrosion, while statistical metrics confirmed the application of RSM and ANFIS techniques in modeling the corrosion inhibition of carbon steel. Weight loss and electrochemical, methods were used to validate the predictive abilities of RSM and ANFIS. In addition, the carbon steel surface was examined post immersion using fourier transform infrared spectroscopy (FTIR), UV–Visible spectroscopy, scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), and atomic force microscopy (AFM). According to the results, the optimal values of the percentage inhibition efficiencies (IE%) of TDE from weight loss, electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP) were found to be in close relationship as 88 %, 86 %, and 81 %, respectively, at a concentration of 2.0 g/L. TDE was confirmed to function as a mixed-type corrosion inhibitor according to potentiodynamic polarization results. The results of the machine learning are in line with the experimental findings. |
| first_indexed | 2024-03-08T22:55:16Z |
| format | Article |
| id | doaj.art-32ccd79c280c4554a3e2d6c893c003e7 |
| institution | Directory Open Access Journal |
| issn | 2666-5239 |
| language | English |
| last_indexed | 2024-03-08T22:55:16Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Surface Science Advances |
| spelling | doaj.art-32ccd79c280c4554a3e2d6c893c003e72023-12-16T06:09:20ZengElsevierApplied Surface Science Advances2666-52392023-12-0118100457Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning predictionFidelis Ebunta Abeng0Igwe O. Ewona1Materials and Electrochemistry Research Group, Department of Chemistry, Cross River University of Technology. P. M. B., Calabar 1123, Nigeria; Corresponding author.Atmospheric Physics Research Group, Department of Physics, Cross River University of Technology. P. M. B., Calabar 1123, NigeriaThe present work compared the predictive abilities of response surface methodology (RSM) and adaptive neuro fuzzy inference systems (ANFIS) in modeling of carbon steel corrosion inhibition by Thaumatococcus daniellii leaf extract (TDE). Thaumatococcus daniellii leaf extract was examined using Fourier Transform Infrared Spectroscopy (FTIR) methods which revealed that the phytochemical components present in TDE has high-value flavonoids, tannins, and dominating functional groups needed to support long-term corrosion inhibition. Error indices revealed the superiority of ANFIS (R2 = 0.99443) and RSM (R2 = 0.98932) in predicting the inhibition efficiency of carbon steel corrosion, while statistical metrics confirmed the application of RSM and ANFIS techniques in modeling the corrosion inhibition of carbon steel. Weight loss and electrochemical, methods were used to validate the predictive abilities of RSM and ANFIS. In addition, the carbon steel surface was examined post immersion using fourier transform infrared spectroscopy (FTIR), UV–Visible spectroscopy, scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), and atomic force microscopy (AFM). According to the results, the optimal values of the percentage inhibition efficiencies (IE%) of TDE from weight loss, electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP) were found to be in close relationship as 88 %, 86 %, and 81 %, respectively, at a concentration of 2.0 g/L. TDE was confirmed to function as a mixed-type corrosion inhibitor according to potentiodynamic polarization results. The results of the machine learning are in line with the experimental findings.http://www.sciencedirect.com/science/article/pii/S2666523923000910Sea water corrosionCarbon steelElectrochemicalFTIRUV–VisibleSurface analyses machine learning |
| spellingShingle | Fidelis Ebunta Abeng Igwe O. Ewona Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction Applied Surface Science Advances Sea water corrosion Carbon steel Electrochemical FTIR UV–Visible Surface analyses machine learning |
| title | Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction |
| title_full | Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction |
| title_fullStr | Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction |
| title_full_unstemmed | Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction |
| title_short | Insights into the corrosion resistance of Thaumatococcus daniellii on carbon steel in simulated sea water: Experimental and machine learning prediction |
| title_sort | insights into the corrosion resistance of thaumatococcus daniellii on carbon steel in simulated sea water experimental and machine learning prediction |
| topic | Sea water corrosion Carbon steel Electrochemical FTIR UV–Visible Surface analyses machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666523923000910 |
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