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

Sojung Kim, Junyoung Seo, Sumin Kim

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations

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.

Original languageEnglish
Article number1316
JournalEnergies
Volume17
Issue number6
DOIs
StatePublished - Mar 2024

Keywords

  • biodiesel
  • machine learning
  • renewable energy
  • supply chain management
  • sustainability

Fingerprint

Dive into the research topics of 'Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review'. Together they form a unique fingerprint.

Cite this