Experimental and Computational Modelling for Optimised Biodiesel Production from Waste Ram Fat Using a Kaolinite-Clay and Eggshell-Derived Catalyst
DOI:
https://doi.org/10.4314/njtd.v22i1.3322Keywords:
Response Surface Methodology, Particle Swamp Algorithm, Firefly Optimisation, Genetic AlgorithmAbstract
Efficacy of some optimisation techniques for biodiesel production were studied for the transesterification of waste ram fat (WRF) with an activated kaolinite-clay/chicken-eggshell (AKC-CE) catalyst. The optimisation study focused on the design of the experiment through response surface methodology by I-optimal experimental design, algorithms (Particle Swamp Algorithm (PSO), Firefly Optimisation (FO) and Genetic Algorithm (GA)) and machine learning (Artificial Neural Network (ANN)). The biodiesel produced was characterised by its Fatty Acid Methyl Esters (FAME) composition using a Gas Chromatography Mass Spectrophotometer (GC-MS). Among the models investigated, the GA, and FA outperformed the others, as they predicted the highest biodiesel yield of 99.00% and experimentally validated as a 99.24% yield. This optimum yield was attained at the optimal input variables of methanol: oil molar ratio (10.33 w/w), reaction time (2.33 h), reaction temperature (95oC), and catalyst quantity (6.44%, w/w). The GCMS accounted for 97.42% of FAME composition at the same optimal input variables. Thus, the research validates the usage of modern algorithms in exploring the best operating conditions to achieve optimum biodiesel yield. The fuel properties obtained confirmed the suitability of the biodiesel produced as an alternative to diesel. This research concludes that the AKC-CE has potential application for optimum yield of biodiesel production.
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