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Aegle marmelos (AM), commonly known as bael fruit, is a medicinal plant that has been used for centuries in traditional Indian medicine. In this work, the predictive capabilities of response surface methodology (RSM) and artificial neural networks (ANN) using MATLAB in modeling brass corrosion inhibition by AM pulp extract in acidic environment (1M H2SO4) were comparatively analyzed. The RSM studies measured the corrosion rate of brass in a 1M H2SO4 solution under different conditions, i.e., time, temperature, and the concentration of AM extract. The maximum inhibition efficiency was achieved at a time period of 24.72 h, 600 ppm of AM pulp extract concentration, and a temperature of 25.8°C. The UV studies showed that AM pulp extract adsorbed on the surface of brass, forming a protective layer that prevented the metal from corroding. The SEM images showed that the surface of brass in the presence of AM pulp extract was smoother than that of brass in the absence of AM pulp extract. The AFM images showed that the roughness of brass in the presence of AM pulp extract was lower than that of brass in the absence of AM pulp extract. The FTIR spectra showed that the absorption bands of the metal oxide on the surface of brass were weakened in the presence of AM pulp extract, indicating that the formation of metal oxide was inhibited. The results of this study suggest that AM pulp extract is an effective and eco-friendly corrosion inhibitor for brass in acidic environment. The study also provides insights into the mechanism of corrosion inhibition by AM pulp extract. [ABSTRACT FROM AUTHOR]

Cadmium biosorption capacity of the biosorbent prepared from Gelidiella acerosa (brown algae) from synthetic cadmium solutions at optimum process conditions was investigated. The surface functional groups of the biosorbent responsible for biosorption were identified using FTIR analysis and the surface texture and porosity of the biosorbent was analyzed by SEM analysis. Response surface methodology (RSM) and artificial neural network (ANN) combined with central composite design (CCD) were used for modeling and optimization of biosorption and to study interaction effects of the three effective process variables such as solution pH, initial cadmium ion concentration and biosorbent dosage. Prediction capacities of RSM and ANN were compared and found that RSM showed better prediction performance than ANN. Kinetic data were well fitted to second order rate equation showing maximum biosorption capacity of 21.73 mg/g for 100 mg/l metal solution concentration. It was further confirmed by fitting the data to Elovich model. Biosorption mechanism was investigated using Intra-particle diffusion and Boyd models. The optimum cadmium removal efficiency of the biosorbent was found as 90.25%. [ABSTRACT FROM AUTHOR]