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Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks

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  • Grahovac, Jovana
  • Jokić, Aleksandar
  • Dodić, Jelena
  • Vučurović, Damjan
  • Dodić, Siniša

Abstract

The aim of this work was to model and predict the process of bioethanol production from intermediates and byproduct of sugar beet processing by applying artificial neural networks. Prediction of one substrate fermentation by neural networks had the same input variables (fermentation time and starting sugar content) and one output value (ethanol content, yeast cell number or sugar content). Results showed that a good prediction model could be obtained by networks with single hidden layer. The neural network configuration that gave the best prediction for raw or thin juice fermentation was one with 8 neurons in hidden layer for all observed outputs. On the other side, the optimal number of neurons in hidden layer was found to be 9 and 10 for thick juice and molasses, respectively. Further, all substrates data were merged, which led to introducing an additional input (substrate type) and defining all outputs optimal network architecture to 3-12-1. From the results the conclusion was that artificial neural networks are a good prediction tool for the selected network outputs. Also, these predictive capabilities allowed the application of the Garson's equation for estimating the contribution of selected process parameters on the defined outputs with satisfactory accuracy.

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  • Grahovac, Jovana & Jokić, Aleksandar & Dodić, Jelena & Vučurović, Damjan & Dodić, Siniša, 2016. "Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 953-958.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:953-958
    DOI: 10.1016/j.renene.2015.07.054
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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. Grahovac, Jovana A. & Dodić, Jelena M. & Dodić, Siniša N. & Popov, Stevan D. & Vučurović, Damjan G. & Jokić, Aleksandar I., 2012. "Future trends of bioethanol co-production in Serbian sugar plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3270-3274.
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    1. Narisetty, Vivek & Narisetty, Sudheera & Jacob, Samuel & Kumar, Deepak & Leeke, Gary A. & Chandel, Anuj Kumar & Singh, Vijai & Srivastava, Vimal Chandra & Kumar, Vinod, 2022. "Biological production and recovery of 2,3-butanediol using arabinose from sugar beet pulp by Enterobacter ludwigii," Renewable Energy, Elsevier, vol. 191(C), pages 394-404.
    2. Gniewko Niedbała, 2019. "Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    3. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    4. Jafar Tavoosi & Amir Abolfazl Suratgar & Mohammad Bagher Menhaj & Amir Mosavi & Ardashir Mohammadzadeh & Ehsan Ranjbar, 2021. "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    5. Niaze, Ambereen A. & Sahu, Rohit & Sunkara, Mahendra K. & Upadhyayula, Sreedevi, 2023. "Model construction and optimization for raising the concentration of industrial bioethanol production by using a data-driven ANN model," Renewable Energy, Elsevier, vol. 216(C).
    6. Małgorzata Smuga-Kogut & Tomasz Kogut & Roksana Markiewicz & Adam Słowik, 2021. "Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment," Energies, MDPI, vol. 14(1), pages 1-16, January.

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