Melaleuca leaves extract as eco-friendly inhibitor for low carbon steel in sulfuric acid

Gapsari, Femiana; Setyarini, Putu H.; Utaminingrum, Fitri; Sulaiman, Abdul M.; Haidar, Muhamad F.; Julian, Tobias S.

doi:10.1016/j.cscee.2024.100657

Cited by 8 publications

(1 citation statement)

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“…The interactions between organic 68 molecules and metal surfaces perform a critical role in reducing corrosion rates and increasing inhibition efficacy [14]. One of the resources for such molecules could be from plant extracts, which have been proven to effectively reduced corrosion rate in previous studies [15], [16]. Extract from Camellia Sinensis (mostly well known as green leaf) [17] has antioxidant compounds that are potential in reducing corrosion rate by acting as electron donors [18].…”

Section: Introductionmentioning

confidence: 99%

Analysis of The Corrosion Inhibition Efficacy of Camellia Sinensis Extract on Aluminium 6061 Alloys using Artificial Neural Network (ANN)

Andrean,

Harmayanti,

Wahjudi

et al. 2024

memi

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Aluminum alloys, well known for their corrosion resistance, could encounter corrosion issues in acidic environments. These environments induced pitting and exfoliation corrosion due to the absence of oxide layers from such alloys. Camellia sinensis extract, one of the organic extract, had inhibition compounds, including tannins and cathecins, that could inhibit corrosion and reduce the corrosion rate. Hence, this study investigated the corrosion inhibition efficacy of Camellia sinensis extract on Aluminum 6061 alloys when exposed to an acidic environment (1M HCl). Different concentrations of the extract were prepared to obtain the optimum concentration and achieve the highest inhibitor efficiency. In addition, an artificial neural network (ANN) model, was employed to predict polarization current in both inhibited and unhibited solutions. The model was designed using a configuration of one input, six hidden and one output ;ayers. The study discovered that the efficiency reached a remarkable level of 82.68% when using a concentration of 3000 ppm of Camellia sinensis extract. Furthermore, the ANN model demonstrated excellent performance in predicting polarization current across all variations, with determinant coefficient (R2) values of 0.995 for 0 ppm, 0.990 for 1000 ppm, 0.997 for 2000 ppm, 0.9996 for 3000 ppm, and 0.996 for 4000 ppm. These results indicated that the model was reliable in simulating the electrochemical analysis of corrosion behavior which could be used to develop a corrosion rate predictor in the future.

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Section: Introductionmentioning

confidence: 99%