Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil
This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/7548251 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850160211319324672 |
|---|---|
| author | Binghui Xu Tzu-Chia Chen Danial Ahangari S. M. Alizadeh Marischa Elveny Jeren Makhdoumi |
| author_facet | Binghui Xu Tzu-Chia Chen Danial Ahangari S. M. Alizadeh Marischa Elveny Jeren Makhdoumi |
| author_sort | Binghui Xu |
| collection | DOAJ |
| description | This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R2, MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data. |
| format | Article |
| id | doaj-art-b27aada64d8e40baadafec541efa31e5 |
| institution | OA Journals |
| 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-b27aada64d8e40baadafec541efa31e52025-08-20T02:23:12ZengWileyInternational Journal of Chemical Engineering1687-806X1687-80782021-01-01202110.1155/2021/75482517548251Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-OilBinghui Xu0Tzu-Chia Chen1Danial Ahangari2S. M. Alizadeh3Marischa Elveny4Jeren Makhdoumi5Logistics Department, Taizhou Vocational and Technical College, Taizhou 318000, Zhejiang, ChinaCAIC, DPU, Bangkok, ThailandDepartment of Petroleum Engineering, Petroleum University of Technology, Ahwaz, IranPetroleum Engineering Department, Australian College of Kuwait, West Mishref, KuwaitDS and CI Research Group, Universitas Sumatera Utara, Medan, IndonesiaDepartment of Educational Science, Payame Noor University, Damghan, IranThis paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R2, MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data.http://dx.doi.org/10.1155/2021/7548251 |
| spellingShingle | Binghui Xu Tzu-Chia Chen Danial Ahangari S. M. Alizadeh Marischa Elveny Jeren Makhdoumi Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil International Journal of Chemical Engineering |
| title | Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil |
| title_full | Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil |
| title_fullStr | Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil |
| title_full_unstemmed | Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil |
| title_short | Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil |
| title_sort | application of a supervised learning machine for accurate prognostication of hydrogen contents of bio oil |
| url | http://dx.doi.org/10.1155/2021/7548251 |
| work_keys_str_mv | AT binghuixu applicationofasupervisedlearningmachineforaccurateprognosticationofhydrogencontentsofbiooil AT tzuchiachen applicationofasupervisedlearningmachineforaccurateprognosticationofhydrogencontentsofbiooil AT danialahangari applicationofasupervisedlearningmachineforaccurateprognosticationofhydrogencontentsofbiooil AT smalizadeh applicationofasupervisedlearningmachineforaccurateprognosticationofhydrogencontentsofbiooil AT marischaelveny applicationofasupervisedlearningmachineforaccurateprognosticationofhydrogencontentsofbiooil AT jerenmakhdoumi applicationofasupervisedlearningmachineforaccurateprognosticationofhydrogencontentsofbiooil |