Preparation of alkaline polymer catalyst by radiation induced grafting for transesterification of triacetin under neural network optimized conditions

ABSTRACT

A simple and flexible method was used to develop new alkaline polymer catalyst through radiation induced grafting of glycidylmethacrylate (GMA) onto polyethylene/polypropylene (PE/PP) nonwoven sheet followed by amination reaction and alkalisation. The chemical structure and morphology of catalyst was evaluated by Fourier transform-infrared (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD) and thermal gravimetric analyzer (TGA). The catalyst was examined for the transesterification of triacetin/methanol mixtures in a batch mode and the obtained methyl ester was detected by GC-MS. In order to optimize the reaction parameters towards getting the higher yield, an artificial neural network (ANN) was used to develop a non-linear model correlating the four independent reaction parameters including catalyst dosage, triacetin/methanol molar ratio, reaction time and temperature. The maximum conversion obtained via the simulated annealing (SA) algorithm was 86.2% at the optimal conditions of 5.01 wt% catalyst dosage, triacetin/methanol 1:12 molar ratio, 8 h reaction time and 62.8°C temperature. Upon using these optimal conditions in the experimental reaction, the conversion of as high as 85% was achieved. These results suggest that the simply modified low cost PE/PP fibrous sheet has a potential to catalyze biodiesel production. Moreover, the combined ANN-SA modelling method is highly effective in predicting the conversion of transesterification reaction and optimizing its parameters.

KEYWORDS:

• Fibrous catalyst
• radiation-induced grafting
• optimization of transesterification
• biodiesel production
• artificial neural network

Funding

The authors from Universiti Teknologi Malaysia (UTM) wish to acknowledge the financial support from the Research University fund (grant # 09H46).