Enhanced Production Of Levulinic Acid Using Carbon Source From Oil Palm Biomass And Matured Coconut Water
Keywords:
Neural networks, levulinic acid, oil palm biomassAbstract
Abstract Levulinic acid (LA), a powerful platform chemical that can only be synthesised from biomass. LA have the capability to act as a substitute intermediate chemical in the production of hydrocarbon-based products. The initial purpose of this research project was to investigate different types of metal chloride catalyst along with determining the optimum process conditions to synthesis LA from oil palm biomass such as oil palm sap (OPS) and matured coconut water. However, the project was disrupted due to the COVID-19 pandemic. Thus, the completion to this project is via artificial intelligence approach. Artificial neural network (ANN) was employed to create a shallow neural network to predict the LA yield using experimentation data sourced from a relatable journal, whereby the inputs were amount of sugar (g/L), catalyst loading (g), temperature () and reaction time (hr) and the output was LA yield (%). Database for sugar composition in both oil palm biomass and matured coconut water was developed to illustrate that it has the capability to produce LA. Second, a performance comparison and evaluation was carried out to analyse and validate the Levenberg-Marquardt (LM) & Bayesian Regularisation (BR) based-ANN models by altering the number of neurons. Overall, LM generalised better than BR algorithm as all of LMANNs’ MSE values were kept below and values were more than 0.88. It was determined that LMANN with 21 hidden neurons is suitable for the experimental database used to build the ANN models.
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