Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions
<p dir="ltr">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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2021
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513505689862144 |
|---|---|
| author | Iftikhar Ahmad (2793085) |
| author2 | Adil Sana (19646095) Manabu Kano (106494) Izzat Iqbal Cheema (17415267) Brenno C. Menezes (1831792) Junaid Shahzad (19646098) Zahid Ullah (7620161) Muzammil Khan (3520076) Asad Habib (19646101) |
| author2_role | author author author author author author author author |
| author_facet | Iftikhar Ahmad (2793085) Adil Sana (19646095) Manabu Kano (106494) Izzat Iqbal Cheema (17415267) Brenno C. Menezes (1831792) Junaid Shahzad (19646098) Zahid Ullah (7620161) Muzammil Khan (3520076) Asad Habib (19646101) |
| author_role | author |
| dc.creator.none.fl_str_mv | Iftikhar Ahmad (2793085) Adil Sana (19646095) Manabu Kano (106494) Izzat Iqbal Cheema (17415267) Brenno C. Menezes (1831792) Junaid Shahzad (19646098) Zahid Ullah (7620161) Muzammil Khan (3520076) Asad Habib (19646101) |
| dc.date.none.fl_str_mv | 2021-08-18T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/en14165072 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning_Applications_in_Biofuels_Life_Cycle_Soil_Feedstock_Production_Consumption_and_Emissions/26984137 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence bio-energy artificial intelligence industry 4.0 biodiesel biogas renewable energy supply chain |
| dc.title.none.fl_str_mv | Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en14165072" target="_blank">https://dx.doi.org/10.3390/en14165072</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_318f70d20d130c04632aaf5e876bfe50 |
| identifier_str_mv | 10.3390/en14165072 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26984137 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and EmissionsIftikhar Ahmad (2793085)Adil Sana (19646095)Manabu Kano (106494)Izzat Iqbal Cheema (17415267)Brenno C. Menezes (1831792)Junaid Shahzad (19646098)Zahid Ullah (7620161)Muzammil Khan (3520076)Asad Habib (19646101)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligencebio-energyartificial intelligenceindustry 4.0biodieselbiogasrenewable energysupply chain<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en14165072" target="_blank">https://dx.doi.org/10.3390/en14165072</a></p>2021-08-18T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/en14165072https://figshare.com/articles/journal_contribution/Machine_Learning_Applications_in_Biofuels_Life_Cycle_Soil_Feedstock_Production_Consumption_and_Emissions/26984137CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269841372021-08-18T03:00:00Z |
| spellingShingle | Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions Iftikhar Ahmad (2793085) Engineering Electrical engineering Information and computing sciences Artificial intelligence bio-energy artificial intelligence industry 4.0 biodiesel biogas renewable energy supply chain |
| status_str | publishedVersion |
| title | 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_short | 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 |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence bio-energy artificial intelligence industry 4.0 biodiesel biogas renewable energy supply chain |