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Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Author

Listed:
  • Sojung Kim

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

  • Junyoung Seo

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

  • Sumin Kim

    (Department of Environmental Horticulture & Landscape Architecture, College of Life Science & Biotechnology, Dankook University, Cheonan-si 31116, Republic of Korea)

Abstract

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1316-:d:1354091
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    References listed on IDEAS

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    1. Papapostolou, Christiana & Kondili, Emilia & Kaldellis, John K., 2011. "Development and implementation of an optimisation model for biofuels supply chain," Energy, Elsevier, vol. 36(10), pages 6019-6026.
    2. Leung, Dennis Y.C. & Wu, Xuan & Leung, M.K.H., 2010. "A review on biodiesel production using catalyzed transesterification," Applied Energy, Elsevier, vol. 87(4), pages 1083-1095, April.
    3. de Jong, Sierk & Hoefnagels, Ric & Wetterlund, Elisabeth & Pettersson, Karin & Faaij, André & Junginger, Martin, 2017. "Cost optimization of biofuel production – The impact of scale, integration, transport and supply chain configurations," Applied Energy, Elsevier, vol. 195(C), pages 1055-1070.
    4. Ghelichi, Zabih & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study," Energy, Elsevier, vol. 156(C), pages 661-687.
    5. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
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    Cited by:

    1. 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.
    2. 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.

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