Browsing by Author "Babalola R"

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    Corrosion inhibition of A36 mild steel in 0.5 M acid medium using waste citrus limonum peels
    (2022) Ayoola AA; Babalola R; Durodola BM; Alagbe EE; Agboola O; Adegbile EO
    Research effort is being intensified on the establishment of organic substances that can actively perform the role of metal inhibition. Investigation on corrosion inhibition of A36 mild steel in 0.5 M H2SO4 medium using waste citrus limonum peels as inhibitor was carried out. Gravimetric tests (weight loss, corrosion rate and inhibition efficiency) involving the variation of citrus limonum peels inhibitor concentration (0–4 w/v%), corrosion time (0–12 h) and reaction temperature (28 °C and 45 °C) were conducted. Langmuir and Freundlich adsorption isotherms were considered in the establishment of the adsorption behavior of citrus limonum peels inhibitor on A36 mild steel surface. The thermodynamic parameters (adsorption equilibrium constant kads, change in Gibbs free energy ΔGads, change in heat of adsorption ΔHads and entropy change ΔSads) of the adsorbed inhibitor on mild steel surface were determined. The results of the study showed that 0.4 w/v% citrus limonum concentration gave highest inhibition efficiency of 94% and 92% on A36 mild steel at 28 °C and 45 °C respectively. And the surface adsorption of citrus limonum inhibitor on A36 mild steel was described by both the Langmuir and Freundlich adsorption isotherms. The negative values of ΔS, ΔGads, ΔHads indicated that the inhibitor adsorption is exothermic and spontaneous (physical adsorption). SEM/EDX analysis showed that inhibitor adsorption of citrus limonum was better at 28 °C compare to 45 °C, by giving a more evenly distributed particles at 0.4 w/v% inhibitor concentration.
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    Data on artificial neural network and response surface methodology analysis of biodiesel production
    (2020) Ayoola AA; Hymore FK; Omonhinmin CA; Babalola PO; Bolujo EO; Adeyemi GA; Babalola R; Olafadehan OA
    The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 – 12), catalyst concentration (0.7 – 1.7 wt/wt%), reaction temperature (48 – 62°C) and reaction time (50 – 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.