IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5191-d1501649.html
   My bibliography  Save this article

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

Author

Listed:
  • Hyeongjun Lim

    (Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

  • Sojung Kim

    (Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

Abstract

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks.

Suggested Citation

  • Hyeongjun Lim & Sojung Kim, 2024. "Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production," Energies, MDPI, vol. 17(20), pages 1-19, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5191-:d:1501649
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5191/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/20/5191/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Héctor García-Martínez & Héctor Flores-Magdaleno & Roberto Ascencio-Hernández & Abdul Khalil-Gardezi & Leonardo Tijerina-Chávez & Oscar R. Mancilla-Villa & Mario A. Vázquez-Peña, 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-24, July.
    2. Sojung Kim & Junyoung Seo & Sumin Kim, 2024. "Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review," Energies, MDPI, vol. 17(6), pages 1-15, March.
    3. Yazdanparast, R. & Jolai, F. & Pishvaee, M.S. & Keramati, A., 2022. "A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions," Renewable Energy, Elsevier, vol. 184(C), pages 799-819.
    4. Youngjin Kim & Yeongjae On & Junyong So & Sumin Kim & Sojung Kim, 2023. "A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    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. Yu Wang & Zhongfa Zhou & Denghong Huang & Tian Zhang & Wenhui Zhang, 2022. "Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
    2. Dejan Ranković & Goran Todorović & Marijenka Tabaković & Slaven Prodanović & Jan Boćanski & Nenad Delić, 2021. "Direct and Joint Effects of Genotype, Defoliation and Crop Density on the Yield of Three Inbred Maize Lines," Agriculture, MDPI, vol. 11(6), pages 1-14, May.
    3. Lili Jiang & Yunfei Wang & Chong Wu & Haibin Wu, 2024. "Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach," Agriculture, MDPI, vol. 14(10), pages 1-16, October.
    4. Joanna Alicja Dyczkowska & Norbert Chamier-Gliszczynski & Waldemar Woźniak & Roman Stryjski, 2024. "Management of the Fuel Supply Chain and Energy Security in Poland," Energies, MDPI, vol. 17(22), pages 1-20, November.
    5. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    6. He, Shuaijia & Gao, Hongjun & Tang, Zao & Chen, Zhe & Jin, Xiaolong & Liu, Junyong, 2023. "Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems," Renewable Energy, Elsevier, vol. 203(C), pages 255-266.
    7. Pompilica Iagăru & Pompiliu Pavel & Romulus Iagăru & Anca Șipoș, 2022. "Aerial Monitorization—A Vector for Ensuring the Agroecosystems Sustainability," Sustainability, MDPI, vol. 14(10), pages 1-12, May.
    8. Taheri, Nima & Jahani, Hamed & Pishvaee, Mir Saman, 2024. "Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach," Renewable Energy, Elsevier, vol. 227(C).
    9. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    10. Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    11. Bahmani, Pardis & Dehghani Sadrabadi, Mohammad Hossein & Makui, Ahmad & Jafari-Nodoushan, Abbasali, 2024. "An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study," Renewable Energy, Elsevier, vol. 229(C).
    12. Flavio Borfecchia & Paola Crinò & Angelo Correnti & Anna Farneti & Luigi De Cecco & Domenica Masci & Luciano Blasi & Domenico Iantosca & Vito Pignatelli & Carla Micheli, 2020. "Assessing the Impact of Water Salinization Stress on Biomass Yield of Cardoon Bio-Energetic Crops through Remote Sensing Techniques," Resources, MDPI, vol. 9(10), pages 1-27, October.
    13. Romeu Gerardo & Isabel P. de Lima, 2023. "Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal," Agriculture, MDPI, vol. 13(10), pages 1-18, September.
    14. Oppong, Francis & Zhongyang, Luo & Li, Xiaolu & Song, Yang & Xu, Cangsu & Diaby, Abdullatif Lacina, 2022. "Methyl pentanoate laminar burning characteristics: Experimental and numerical analysis," Renewable Energy, Elsevier, vol. 197(C), pages 228-236.

    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:gam:jeners:v:17:y:2024:i:20:p:5191-:d:1501649. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.