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.
In recent years, producing economical biofuels especially bio-ethanol from lignocellulosic materials has been widely considered. Fermentation is an important step in ethanol production process. Fermentation process is completely nonlinear and depends on some parameters such as temperature, sugar content, and PH. One of the difficulties in producing biomass is finding the optimum point of the interrelated parameters in the fermentation step. In this study, an elaborate prediction Neuro-Fuzzy model was built to predict the bio-ethanol production from corn stover. Also, particle swarm optimization (PSO) method was used to optimize the three studied parameters: temperature, glucose content, and fermentation time. The attained correlation coefficient (0.99), and root mean square error (0.637) for model validation show the reliability of the model. Optimization of the model shows the optimum fermentation time and required temperature quantities, 69.39hours and 34.50 ͦC, respectively. The good result for ANFIS modeling on fermentation process in bio-ethanol production from corn stover shows that this method can be used to investigate more about other biomass lignocellulos sources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.