A Review on Machine Learning Application in Biodiesel Production Studies
The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce vario...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/2154258 |
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| author | Yuanzhi Xing Zile Zheng Yike Sun Masoome Agha Alikhani |
| author_facet | Yuanzhi Xing Zile Zheng Yike Sun Masoome Agha Alikhani |
| author_sort | Yuanzhi Xing |
| collection | DOAJ |
| description | The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production. |
| format | Article |
| id | doaj-art-278a90dbb5514e67b153a3baeb3e55b4 |
| institution | Kabale University |
| issn | 1687-806X 1687-8078 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-278a90dbb5514e67b153a3baeb3e55b42025-02-03T01:25:48ZengWileyInternational Journal of Chemical Engineering1687-806X1687-80782021-01-01202110.1155/2021/21542582154258A Review on Machine Learning Application in Biodiesel Production StudiesYuanzhi Xing0Zile Zheng1Yike Sun2Masoome Agha Alikhani3School of Management, Shanghai University, Shanghai 201800, ChinaSchool of Management, Shanghai University, Shanghai 201800, ChinaSchool of Management, Shanghai University, Shanghai 201800, ChinaFouman Faculty of Engineering, College of Engineering, University of Tehran, Fouman, IranThe consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.http://dx.doi.org/10.1155/2021/2154258 |
| spellingShingle | Yuanzhi Xing Zile Zheng Yike Sun Masoome Agha Alikhani A Review on Machine Learning Application in Biodiesel Production Studies International Journal of Chemical Engineering |
| title | A Review on Machine Learning Application in Biodiesel Production Studies |
| title_full | A Review on Machine Learning Application in Biodiesel Production Studies |
| title_fullStr | A Review on Machine Learning Application in Biodiesel Production Studies |
| title_full_unstemmed | A Review on Machine Learning Application in Biodiesel Production Studies |
| title_short | A Review on Machine Learning Application in Biodiesel Production Studies |
| title_sort | review on machine learning application in biodiesel production studies |
| url | http://dx.doi.org/10.1155/2021/2154258 |
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