Article
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Deep Learning and Machine Learning Models in Biofuels Research: Systematic Review
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: Received: 28 September 2020 / Approved: 30 September 2020 / Online: 30 September 2020 (11:19:52 CEST)
How to cite: ardabili, S.; Mosavi, A.; Várkonyi Kóczy, A. Deep Learning and Machine Learning Models in Biofuels Research: Systematic Review. Preprints 2020, 2020090741 ardabili, S.; Mosavi, A.; Várkonyi Kóczy, A. Deep Learning and Machine Learning Models in Biofuels Research: Systematic Review. Preprints 2020, 2020090741
Abstract
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.
Keywords
Deep learning; Big data; Machine learning; Biofuels
Subject
Engineering, Energy and Fuel Technology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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