Machine Learning-RSM Hybridized Evaluation of the Kinetics and Thermodynamics of Mild Steel Corrosion Inhibition Using Lagenaria Breviflora Extract
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The inhibition of mild steel corrosion in dilute hydrochloric acid (1 M HCl) by the xylene extract of Lagenaria breviflora (XEL-B) was studied using a Central Composite Design (CCD) structured Response Surface Methodology (RSM). A statistically optimised 20-run experimental matrix was employed to evaluate the simultaneous effects of inhibitor concentration, immersion time and temperature on mass loss, corrosion rate (Rc), inhibitor efficiency (IE), and surface coverage (θ). The fitted quadratic response surface model was highly significant (F = 55.81, p < 0.0001) with a non-significant lack of fit (p = 0.2730), confirming adequate model predictability across the experimental domain. Inhibitor efficiency ranged from 28.68% at 31 ppm of inhibitor concentration to 77.01% at 368 ppm, with inhibitor concentration identified as the dominant process variable statistically validated (F = 423.40, p < 0.0001) cosnsistent with 4D response surface analysis, and a 500-tree Random Forest ensemble machine learning model (factor importance: IE = 77.54%, Rc = 74.20%). Adsorption of XEL-B on mild steel conformed to the Langmuir monolayer isotherm (R² = 0.9950), equilibrium adsorption constant Kads of 11.3649Lg⁻¹ and standard Gibbs free energy of adsorption ΔG°ads of −16.24 kJ/mol, confirming spontaneous, thermodynamically favourable adsorption with a mixed physisorptive–chemisorptive mechanism. These integrated experimental-computational results established XEL-B as a potential green corrosion inhibitor for mild steel in dilute acidic environments relevant to oilfield and industrial acid-treatment operations.
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