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

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  • Leila Ezzatzadegan

    (Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Center of Lipids Engineering and Applied Research (CLEAR), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Rubiyah Yusof

    (Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Noor Azian Morad

    (Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Center of Lipids Engineering and Applied Research (CLEAR), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Parvaneh Shabanzadeh

    (Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Nur Syuhana Muda

    (Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Center of Lipids Engineering and Applied Research (CLEAR), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Tohid N. Borhani

    (Division of Chemical Engineering, School of Engineering, University of Wolverhampton, Wolverhampton WV1 1LY, UK)

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.

Suggested Citation

  • Leila Ezzatzadegan & Rubiyah Yusof & Noor Azian Morad & Parvaneh Shabanzadeh & Nur Syuhana Muda & Tohid N. Borhani, 2021. "Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation," Energies, MDPI, vol. 14(8), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2137-:d:534119
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    References listed on IDEAS

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    1. Gueguim Kana, E.B. & Oloke, J.K. & Lateef, A. & Adesiyan, M.O., 2012. "Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm," Renewable Energy, Elsevier, vol. 46(C), pages 276-281.
    2. Firdaus E. Udwadia & Artin Farahani, 2008. "Accelerated Runge-Kutta Methods," Discrete Dynamics in Nature and Society, Hindawi, vol. 2008, pages 1-38, September.
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