Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions
Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satelli...
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MDPI AG
2021-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/16/5072 |
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doaj-5f98a4289383403ba53921b6bd0acd6b2021-08-26T13:43:21ZengMDPI AGEnergies1996-10732021-08-01145072507210.3390/en14165072Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and EmissionsIftikhar Ahmad0Adil Sana1Manabu Kano2Izzat Iqbal Cheema3Brenno C. Menezes4Junaid Shahzad5Zahid Ullah6Muzammil Khan7Asad Habib8Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Systems Science, Kyoto University, Kyoto 606-8501, JapanDepartment of Chemical, Polymer and Composite Materials Engineering, University of Engineering and Technology, New Campus, Lahore 54890, PakistanDivision of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, QatarDepartment of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat 26000, PakistanMachine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.https://www.mdpi.com/1996-1073/14/16/5072bio-energyartificial intelligenceindustry 4.0biodieselbiogasrenewable energy |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Iftikhar Ahmad Adil Sana Manabu Kano Izzat Iqbal Cheema Brenno C. Menezes Junaid Shahzad Zahid Ullah Muzammil Khan Asad Habib |
| spellingShingle |
Iftikhar Ahmad Adil Sana Manabu Kano Izzat Iqbal Cheema Brenno C. Menezes Junaid Shahzad Zahid Ullah Muzammil Khan Asad Habib Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions Energies bio-energy artificial intelligence industry 4.0 biodiesel biogas renewable energy |
| author_facet |
Iftikhar Ahmad Adil Sana Manabu Kano Izzat Iqbal Cheema Brenno C. Menezes Junaid Shahzad Zahid Ullah Muzammil Khan Asad Habib |
| author_sort |
Iftikhar Ahmad |
| title |
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions |
| title_short |
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions |
| title_full |
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions |
| title_fullStr |
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions |
| title_full_unstemmed |
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions |
| title_sort |
machine learning applications in biofuels’ life cycle: soil, feedstock, production, consumption, and emissions |
| publisher |
MDPI AG |
| series |
Energies |
| issn |
1996-1073 |
| publishDate |
2021-08-01 |
| description |
Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle. |
| topic |
bio-energy artificial intelligence industry 4.0 biodiesel biogas renewable energy |
| url |
https://www.mdpi.com/1996-1073/14/16/5072 |
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