Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation

Leila Ezzatzadegan, Rubiyah Yusof, Noor Azian Morad, Parvaneh Shabanzadeh, Nur Syuhana Muda and Tohid N. Borhani
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Leila Ezzatzadegan: Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Rubiyah Yusof: Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Noor Azian Morad: Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Parvaneh Shabanzadeh: Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Nur Syuhana Muda: Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Tohid N. Borhani: Division of Chemical Engineering, School of Engineering, University of Wolverhampton, Wolverhampton WV1 1LY, UK

Energies, 2021, vol. 14, issue 8, 1-22

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.

Keywords: fermentation; oil palm trunk sap; neuro-fuzzy; ANFIS; particle swarm optimization (PSO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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