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<p><i>Garcinia cambogia</i> extract (GCE) was employed as a green corrosion inhibitor, a sustainable alternative to toxic and non-biodegradable synthetic inhibitors, on mild steel in 1&#xa0;M HCl and 0.5&#xa0;M H<sub>2</sub>SO<sub>4</sub>. Corrosion inhibition efficiency of GCE was assessed using physicochemical, electrochemical, morphological, and quantum chemical techniques. Moreover, the effects of inhibitor concentration, acid concentration, and temperature on the inhibition efficiency (IE) were modelled and optimised by response surface methodology (RSM) and artificial neural network (ANN). Electrochemical studies revealed that GCE acts as a mixed type corrosion inhibitor. Kinetic and thermodynamic data suggested that GCE adsorbs onto the metal surface via both physisorption and chemisorption. Modelling of parameters using an artificial neural network (ANN) indicated that the model was trained effectively. The dual use of advanced computational modelling, RSM and ANN, along with their good agreement with experimental results, constitutes the novelty of this work. The future scope of this work involves testing GCE as a green corrosion inhibitor under real-world industrial conditions.</p>

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Modelling the Corrosion Inhibition Efficiency of Garcinia Cambogia Extract on Mild Steel in Acid Media Using ANN and RSM Approaches

  • 期刊论文

  • 发表日期：

    2025年9月13日

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  • K. Vidhya Thomas,
  • K. Joby Thomas,
  • A. S. Sabu,
  • C. Sini Varghese,
  • Reeja Jhonson

摘要

Garcinia cambogia extract (GCE) was employed as a green corrosion inhibitor, a sustainable alternative to toxic and non-biodegradable synthetic inhibitors, on mild steel in 1 M HCl and 0.5 M H2SO4. Corrosion inhibition efficiency of GCE was assessed using physicochemical, electrochemical, morphological, and quantum chemical techniques. Moreover, the effects of inhibitor concentration, acid concentration, and temperature on the inhibition efficiency (IE) were modelled and optimised by response surface methodology (RSM) and artificial neural network (ANN). Electrochemical studies revealed that GCE acts as a mixed type corrosion inhibitor. Kinetic and thermodynamic data suggested that GCE adsorbs onto the metal surface via both physisorption and chemisorption. Modelling of parameters using an artificial neural network (ANN) indicated that the model was trained effectively. The dual use of advanced computational modelling, RSM and ANN, along with their good agreement with experimental results, constitutes the novelty of this work. The future scope of this work involves testing GCE as a green corrosion inhibitor under real-world industrial conditions.