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Deep Learning and Machine Learning Models in Biofuels Research: Systematic Review

EasyChair Preprint no. 4324

12 pagesDate: October 6, 2020


 The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations.

Keyphrases: Big Data., Biofuels., Deep learnin., Machine Learning.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sina Ardabili and Amir Mosavi and A.R Várkonyi Kóczy},
  title = {Deep Learning and Machine Learning Models in Biofuels Research: Systematic Review},
  howpublished = {EasyChair Preprint no. 4324},

  year = {EasyChair, 2020}}
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