IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v216y2023ics096014812300945x.html
   My bibliography  Save this article

Model construction and optimization for raising the concentration of industrial bioethanol production by using a data-driven ANN model

Author

Listed:
  • Niaze, Ambereen A.
  • Sahu, Rohit
  • Sunkara, Mahendra K.
  • Upadhyayula, Sreedevi

Abstract

This work aims to model and predict the bioethanol produced from a conventional fermentation process. An industrial dataset was obtained from a sugar mill in India. These datasets comprised a total of 1300 experimental values acquired from a total 100 days of production of the sugar mill in the year 2021. Using this data, a framework based on deep learning Artificial Neural Network (ANN) technique model was developed and validated with the test data. Specifically, the normalized dataset was passed through an ANN model consisting of one input layer, two hidden layers and one output layer, and the percent model error calculated by using average absolute deviation metric and was found to be 4.48 and 1.99% for training and testing data, respectively. The optimization of the process variables was performed for the first time using a data synthesis technique in which the normalized dataset was first synthesized and then passed through an ANN model to get an optimized input variable set for an increase in bioethanol concentration (BEC) in the final product by 1°GL. The operating parameters which significantly influenced the BEC are concentration of cell in pre-fermentation (PFM), water fermentation pH (WFMpH), and water fermentation hardness (WFH). An increase of 8.45% in BEC was obtained for Sugar mill, India.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s096014812300945x
    DOI: 10.1016/j.renene.2023.119031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812300945X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.119031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sahar Safarian & Seyed Mohammad Ebrahimi Saryazdi & Runar Unnthorsson & Christiaan Richter, 2021. "Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation," Biophysical Economics and Resource Quality, Springer, vol. 6(1), pages 1-13, March.
    2. Li, Xinzhe & Dong, Yufeng & Chang, Lu & Chen, Lifan & Wang, Guan & Zhuang, Yingping & Yan, Xuefeng, 2023. "Dynamic hybrid modeling of fuel ethanol fermentation process by integrating biomass concentration XGBoost model and kinetic parameter artificial neural network model into mechanism model," Renewable Energy, Elsevier, vol. 205(C), pages 574-582.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naveed, Muhammad Hamza & Khan, Muhammad Nouman Aslam & Mukarram, Muhammad & Naqvi, Salman Raza & Abdullah, Abdullah & Haq, Zeeshan Ul & Ullah, Hafeez & Mohamadi, Hamad Al, 2024. "Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. 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.
    3. 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.
    4. 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.
    5. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    6. Qingqing Liu & Nianping Li & Yongga A & Jiaojiao Duan & Wenyun Yan, 2021. "The Evaluation of the Corrosion Rates of Alloys Applied to the Heating Tower Heat Pump (HTHP) by Machine Learning," Energies, MDPI, vol. 14(7), pages 1-13, April.
    7. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    8. 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).
    9. 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.
    10. Moises Ramos-Martinez & Carlos Alberto Torres-Cantero & Gerardo Ortiz-Torres & Felipe D. J. Sorcia-Vázquez & Himer Avila-George & Ricardo Eliú Lozoya-Ponce & Rodolfo A. Vargas-Méndez & Erasmo M. Rente, 2023. "Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network," Mathematics, MDPI, vol. 11(18), pages 1-26, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:216:y:2023:i:c:s096014812300945x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.