Optimum prediction for inhibition efficiency of Sapium ellipticum leaf extract as corrosion inhibitor of aluminum alloy (AA3003) in hydrochloric acid solution using electrochemical impedance spectroscopy and response surface methodology

Statistical optimization was used to optimize corrosion inhibition efficiency of Sapium ellipticum leaf extract as corrosion inhibitor of aluminum in acid medium. Response surface methodology was applied, and the effects of four independent variables; acid concentration, inhibitor concentration, temperature, time, and their expected responses were determined. Central composite design a statistical tool was used to generate a total of 16 individual experimental runs, which was previously design to study the effects of these variables during corrosion process. The uniqueness of the model was scrutinized with various criteria including coefficient of determination (R2 = 0.987), p value (< 0.0001), adequate precision (30.22) and coefficient of variation (5.30). The RSM is well fitted in the model which adequately predicted the optimum inhibition efficiency of 96.73% at optimum inhibitor concentration of 1.5g/L-1, acid concentration 1 M, temperature of 303 K and time of 6 hours. Also the electrochemical concept signifies that Sapium ellipticum acts as a mixed-kind inhibitor. The experimental data obtained is in conformity with other research works.   Bull. Chem. Soc. Ethiop. 2020, 34(1), 175-191. DOI: https://dx.doi.org/10.4314/bcse.v34i1.1

Evaluation of Bitter Kola Leaf Extract as an Anticorrosion Additive for Mild Steel in 1.2 M H2SO4 Electrolyte

Plant-based material, namely bitter kola leaf, as an additive for surface modification of mild steel in H2SO4 solution was thoroughly scrutinized using  electrochemical, theoretical and optimization techniques. The functional groups, of the biomolecules of the bitter kola leaf extract, were examined using  Fourier transform infrared spectrometry (FTIR) and gas chromatography-mass spectrophotometry (GC-MS). For clarification purpose, scanning electron  microscopy (SEM) was used to inspect the texture of the degraded and inhibited steel after 21 h of immersion. For the response surface methodology  (RSM), central composite design of Design-Expert Software was used to optimize the inhibition efficiency as a function of acid concentration, inhibitor  concentration, temperature and time. The optimum inhibition efficiency of 93 % was obtained at 0.9 g L–1 bitter kola leaf. The mutual correlation between  the considered variables and expected response was adequately interpreted by a quadratic model. The fitness of the model was justified by the  following standards which include P-value (<0.0001), adjusted R2 (0.9843), R2 (0.991), adequate precision (43.14) and coefficient of variation (2.59). Bitter  kola leaf extract behaved as a mixed-type inhibitor and adequately satisfied Langmuir adsorption isotherm. Furthermore, the theoretical modelling  revealed the most active molecule of bitter kola leaf responsible for the overall inhibition. The experimental and theoretical results are in agreement that   bitter kola leaf extract is a viable corrosion inhibitor of mild steel in H2SO4 solution

1-Butyl-3-methylimidazolium methane sulfonate ionic liquid corrosion inhibitor for mild steel alloy: Experimental, optimization and theoretical studies

The current research reports the performance of 1-butyl-3-methylimidazolium methane sulfonate ([C4MIM][OMs](IL)) as effective corrosion inhibitor for mild steel in 1 M H2SO4 electrolyte. For proper evaluation, weight loss, electrochemical study, theoretical modeling and optimization techniques were used. Weight loss and electrochemical methods shows that the inhibition performance of [C4MIM][OMs] on the metal surface strengthens as the concentration increases. Maximum inhibition efficiency of 85.71%, 92.5% and 91.1% at 0.8 g L-1 concentration of [C4MIM][OMs] were obtained from the weight loss, polarization and impedance studies, respectively. In addition, response surface methodology (RSM) a statistical tool was used for modeling and optimization of the empirical data. The RSM model validates the empirical findings. Also, DFT/MD-simulation investigations evidenced that [C4MIM][OMs] forms a barrier film on the mild steel surface. The result shows that the synthesized [C4MIM][OMs] could open up opportunities in corrosion and materials protection for sustainability

Statistical computation and artificial neural algorithm modeling for the treatment of dye wastewater using mucuna sloanei as coagulant and study of the generated sludge

Adaptive neuro-fuzzy inference system and response surface methodology techniques were used to predict coagulation-flocculation parameters needed to remove color and COD effectively and produce acceptable sludge using Mucuna sloanei at 298 K from a dye wastewater. The variables input to the network were coagulant dosage (1000, 1400 and 1800 mg/L), solution pH (2, 6 and 10) and stirring time (5, 15 and 25 min). Coefficient of determination, R2 and root mean square, RMSE were used to evaluate the adequacy and predictive relevance of the two techniques. Color removal model indicators are R2 0.9823, RMSE 0.2599 and R2 0.8616, RMSE 21.403 for ANFIS and RSM, respectively; COD removal indicators are respectively, R2 0.9752, RMSE 0.2009, and R2 0.9741, RMSE 0.8118 for ANFIS and RSM; while sludge volume index model indicators are R2 0.9950, RMSE 0.2341, and R2 0.9930, RSME 2.1436, for ANFIS and RSM, respectively. With a limited set of data, the generated models produced idealized findings and were proven to be useful for forecasting color and COD elimination, and SVI. Nevertheless, the ANFIS model is clearly favored because of greater R2 values and lower RMSE