Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Iftikhar Ahmad, Adil Sana, Manabu Kano, Izzat Iqbal Cheema, Brenno C. Menezes, Junaid Shahzad, Zahid Ullah, Muzammil Khan and Asad Habib
Additional contact information
Iftikhar Ahmad: Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Adil Sana: Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Manabu Kano: Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan
Izzat Iqbal Cheema: Department of Chemical, Polymer and Composite Materials Engineering, University of Engineering and Technology, New Campus, Lahore 54890, Pakistan
Brenno C. Menezes: Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
Junaid Shahzad: Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Zahid Ullah: Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Muzammil Khan: Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Asad Habib: Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan

Energies, 2021, vol. 14, issue 16, 1-27

Abstract: 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.

Keywords: bio-energy; artificial intelligence; industry 4.0; biodiesel; biogas; renewable energy; supply chain (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/16/5072/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/16/5072/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:16:p:5072-:d:616653

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().