Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management
The thermochemical conversion of biomass is a promising technology due to its cost-effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method for its diverse product range. Despite the potential of pyrolysis, commercialization remains elusive, and there is a growi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508123000212 |
| _version_ | 1797822550195044352 |
|---|---|
| author | David Akorede Akinpelu Oluwaseun A. Adekoya Peter Olusakin Oladoye Chukwuma C. Ogbaga Jude A. Okolie |
| author_facet | David Akorede Akinpelu Oluwaseun A. Adekoya Peter Olusakin Oladoye Chukwuma C. Ogbaga Jude A. Okolie |
| author_sort | David Akorede Akinpelu |
| collection | DOAJ |
| description | The thermochemical conversion of biomass is a promising technology due to its cost-effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method for its diverse product range. Despite the potential of pyrolysis, commercialization remains elusive, and there is a growing need to fully understand its dynamics to facilitate process scaling up. However, waste biomass pyrolysis is complex, time-consuming, and capital-intensive. Machine Learning (ML) has emerged as a possible means of supporting and accelerating pyrolysis research despite these challenges. This study provides a comprehensive overview of the use of ML in pyrolysis, from biorefinery to end-of-life product management. In addition, the success of ML in process optimization and control, predicting product yield, real-time monitoring, life-cycle assessment (LCA), and techno-economic analysis (TEA) during biomass pyrolysis is highlighted. Several ML methods have been utilized in a bid to study pyrolysis; the potentiality of artificial neural networks (ANNs) to learn extremely non-linear input-output correlations has led to the widespread adoption of these networks. Furthermore, the current knowledge gaps in ML research in pyrolysis and future recommendations for its application are identified. Finally, this study demonstrates the potential of ML in accelerating research and development as well as the scalability of pyrolysis of biomass. |
| first_indexed | 2024-03-13T10:10:46Z |
| format | Article |
| id | doaj.art-a4310a6520314368b01afdaadb795060 |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-03-13T10:10:46Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-a4310a6520314368b01afdaadb7950602023-05-22T04:05:12ZengElsevierDigital Chemical Engineering2772-50812023-09-018100103Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product managementDavid Akorede Akinpelu0Oluwaseun A. Adekoya1Peter Olusakin Oladoye2Chukwuma C. Ogbaga3Jude A. Okolie4Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USADepartment of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USADepartment of Biological Sciences, Nile University of Nigeria, Airport Road Bypass, Abuja, Nigeria; Department of Microbiology and Biotechnology, Nile University of Nigeria, Airport Road Bypass, Abuja, NigeriaGallogly College of Engineering, University of Oklahoma, USA; Corresponding author.The thermochemical conversion of biomass is a promising technology due to its cost-effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method for its diverse product range. Despite the potential of pyrolysis, commercialization remains elusive, and there is a growing need to fully understand its dynamics to facilitate process scaling up. However, waste biomass pyrolysis is complex, time-consuming, and capital-intensive. Machine Learning (ML) has emerged as a possible means of supporting and accelerating pyrolysis research despite these challenges. This study provides a comprehensive overview of the use of ML in pyrolysis, from biorefinery to end-of-life product management. In addition, the success of ML in process optimization and control, predicting product yield, real-time monitoring, life-cycle assessment (LCA), and techno-economic analysis (TEA) during biomass pyrolysis is highlighted. Several ML methods have been utilized in a bid to study pyrolysis; the potentiality of artificial neural networks (ANNs) to learn extremely non-linear input-output correlations has led to the widespread adoption of these networks. Furthermore, the current knowledge gaps in ML research in pyrolysis and future recommendations for its application are identified. Finally, this study demonstrates the potential of ML in accelerating research and development as well as the scalability of pyrolysis of biomass.http://www.sciencedirect.com/science/article/pii/S2772508123000212Neural networkPyrolysisMachine LearningLife cycle assessmentTechno-economic analysis |
| spellingShingle | David Akorede Akinpelu Oluwaseun A. Adekoya Peter Olusakin Oladoye Chukwuma C. Ogbaga Jude A. Okolie Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management Digital Chemical Engineering Neural network Pyrolysis Machine Learning Life cycle assessment Techno-economic analysis |
| title | Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management |
| title_full | Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management |
| title_fullStr | Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management |
| title_full_unstemmed | Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management |
| title_short | Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management |
| title_sort | machine learning applications in biomass pyrolysis from biorefinery to end of life product management |
| topic | Neural network Pyrolysis Machine Learning Life cycle assessment Techno-economic analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2772508123000212 |
| work_keys_str_mv | AT davidakoredeakinpelu machinelearningapplicationsinbiomasspyrolysisfrombiorefinerytoendoflifeproductmanagement AT oluwaseunaadekoya machinelearningapplicationsinbiomasspyrolysisfrombiorefinerytoendoflifeproductmanagement AT peterolusakinoladoye machinelearningapplicationsinbiomasspyrolysisfrombiorefinerytoendoflifeproductmanagement AT chukwumacogbaga machinelearningapplicationsinbiomasspyrolysisfrombiorefinerytoendoflifeproductmanagement AT judeaokolie machinelearningapplicationsinbiomasspyrolysisfrombiorefinerytoendoflifeproductmanagement |