Abstract
Five 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.
Highlights
Today, biofuel production obtained by the biological fermentation process is an interesting subject in the field of renewable energy
In this view, knowing the optimum experimental conditions and estimation of the bioethanol produced by glucose can be very useful in industrial applications because raw materials as carbon sources must first be converted to glucose before the bioethanol fermentation is performed
This research aimed to consider the accurate prediction of the Adaptive-Network-Fuzzy Inference System (ANFIS) model for bioethanol produced in biomass fermentation of oil palm trunk sap
Summary
Biofuel production obtained by the biological fermentation process is an interesting subject in the field of renewable energy. The cost of bioethanol production needs to be reduced via fermentation processes. In this view, knowing the optimum experimental conditions and estimation of the bioethanol produced by glucose can be very useful in industrial applications because raw materials as carbon sources must first be converted to glucose before the bioethanol fermentation is performed. The economic analysis of bioethanol production from lignocellulosic biomasses showed the necessity of process optimization due to its high price in the case of large-scale production. The first and vital step to achieve the optimal production from lignocellulosic biomasses is the process modelling study. Being nonlinear and dynamic are two inherent properties of the fermentation process, which make modelling the proposed process challenging [1]. Significant efforts have been made to examine the mathematical models and propose a methodology as a solution [2,3,4]
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