Experimental and artificial intelligence modelling study of oil palm trunk sap fermentation
MetadataShow full item record
AbstractFive major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization (PSO) to predict bioethanol production in biomass fermentation of oil palm trunk sap. The model was used to find the best experimental conditions to achieve the maximum bioethanol concentration. Graphical sensitivity analysis techniques were also used to identify the most effective parameters in the bioethanol process.
CitationEzzatzadegan, L., Yusof, R., Morad, N.A., Shabanzadeh, P., Muda, N.S. and Borhani, T.N. (2021) Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation. Energies, 14(8):2137. https://doi.org/10.3390/en14082137
Description© 2021 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/en14082137
SponsorsThis work supported by the Ministry of Education Malaysia through a Research University Grant of the University Technology Malaysia (UTM) (Award Number: Rk430000.7743.4J010).
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/