Version 1
: Received: 2 September 2020 / Approved: 3 September 2020 / Online: 3 September 2020 (09:32:40 CEST)
How to cite:
Ahmad, I.; Kano, M.; Menezes, B.C.; Cheema, I.I.; Sana, A.; Shahzad, J.; Ullah, Z.; Khan, M.; Habib, A. Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Preprints2020, 2020090068. https://doi.org/10.20944/preprints202009.0068.v1
Ahmad, I.; Kano, M.; Menezes, B.C.; Cheema, I.I.; Sana, A.; Shahzad, J.; Ullah, Z.; Khan, M.; Habib, A. Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Preprints 2020, 2020090068. https://doi.org/10.20944/preprints202009.0068.v1
Ahmad, I.; Kano, M.; Menezes, B.C.; Cheema, I.I.; Sana, A.; Shahzad, J.; Ullah, Z.; Khan, M.; Habib, A. Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Preprints2020, 2020090068. https://doi.org/10.20944/preprints202009.0068.v1
APA Style
Ahmad, I., Kano, M., Menezes, B.C., Cheema, I.I., Sana, A., Shahzad, J., Ullah, Z., Khan, M., & Habib, A. (2020). Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Preprints. https://doi.org/10.20944/preprints202009.0068.v1
Chicago/Turabian Style
Ahmad, I., Muzammil Khan and Asad Habib. 2020 "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions" Preprints. https://doi.org/10.20944/preprints202009.0068.v1
Abstract
Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. This study reviews the state-of-the-art ML applications in the biofuels’ life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx, CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.
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.