Production of biodiesel from waste vegetable oil and methanol using industrial brine sludge waste from chloro-alkali as a nanocomposite heterogeneous catalyst
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Date
2023-02
Authors
Muthubi, Shonisani Salvation
Journal Title
Journal ISSN
Volume Title
Publisher
Vaal University of Technology
Abstract
Biodiesel production as a diesel engine fuel has increased substantially in recent years and is expected to rise more in the near future. Rapidly increasing biodiesel production necessitates efficient production processes that enable huge production capacities, simplified operations, high yields, and inexpensive feedstocks such as waste oils and animal fats. In this work, biodiesel was produced using waste vegetable oil (WVO) and Methanol (CH3OH) in the presence of a catalyst synthesized from industrial waste, primarily calcium carbonate (CaCO3). Fourier Transform Infrared (FTIR), Scanning Electron Microscope (SEM), and X-ray diffraction (XRD) were used to analyze the manufactured nano-particle catalyst. The optimum operating conditions for the highest biodiesel yield after applying the artificial neural network (ANN) approach was 96.41% yield at a temperature of 60°C, catalyst loading of 1% w/v, methanol to oil ratio of 1:5 w/w and reaction time of 80 min. The FTIR showed the presents of the CaO and NCC functional groups. Based on the SEM image, the catalyst produced was more porous, with small particle size. The XRD pattern indicates calcium oxide (CaO) and cellulose (NCC) nanoparticles. The ANN approach was suitable for predicting biodiesel with an overall correlation coefficient (of 0.990). Furthermore, the Response surface methodology (RSM) was used to determine the optimum operating conditions for the highest biodiesel yield. After applying the RSM methods using the CCD experimental design, the optimum biodiesel production was found at a temperature of 55°C, catalyst loading of 1.25% w/v, methanol to oil ratio of 1:5 w/w, and reaction time of 75 min with an average yield of 94.01%. The R2 of 0.963 was found for the mathematical models to predict biodiesel production.
Description
M. Tech. (Department of Chemical Engineering, Faculty of Engineering and Technology), Vaal University of Technology.
Keywords
Biodiesel, Nanocrystalline cellulose, Nano-catalyst, Nanoparticles, Composites, Artificial neural network