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Biodiesel was produced from waste frying oil using calcium oxide derived from the calcination of egg shell as a heterogeneous base catalyst. The catalyst was characterized by X‐ray diffraction, Fourier transform infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller analysis, scanning electron microscopy, and energy‐dispersive atomic X‐ray spectrometry. The effects of reaction variables such as methanol/oil molar ratio, amount of catalyst, reaction temperature, and reaction time on biodiesel yield were investigated. The activity of the egg shell‐derived CaO was compared with the commercial CaO. A high biodiesel yield of 95.05% and conversion of 96.11% were obtained at 3 wt % catalyst (based on oil weight), methanol/oil molar ratio of 9:1, reaction temperature of 65°C, and reaction time of 3 h. The structure of biodiesel was characterized by FTIR, and the biodiesel conversion was determined by 1H nuclear magnetic resonance spectroscopy. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 248–254, 2015

In the present study, Calcium oxide (CaO) obtained from eggshells has been used as a heterogeneous catalyst for biodiesel production from highly viscous non-edible rubber seed oil (RSO). Characterization of synthesized catalyst was done with the help of scanning electron microscope equipped with Energy dispersive spectrometry (SEM-EDS), X-ray diffraction (XRD) and Fourier transform Infrared spectroscopy (FTIR). Response surface methodology (RSM) with central composite design (CCD) was used to optimize the process parameters and 1H-NMR (Nuclear Magnetic Resonance) spectroscopy analysis was performed to find the conversion of RSO to biodiesel. A conversion of 99.7% of RSO to biodiesel was obtained at 12:1 methanol to oil molar ratio, 4 (wt%) of catalyst, and 3 hour reaction time with a quadratic regression model of R2 of value 0.9566 was obtained. The composition of prepared biodiesel is estimated with the help of Gas Chromatogram-Mass Spectroscopy (GC-MS) analysis. Artificial Neural Network (ANN) with Levenberg-Marquardt algorithm was also trained to predict biodiesel conversion and the value of R2 obtained was 0.9976. It was observed that predicted conversion values from ANN were better when compared to prediction using RSM.