Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming
The aim of this study was to utilise artificial neural network (ANN) and AdaBoost (AB) algorithms to model the synthesis gas composition from the steam reforming of biomass bio-oil. At testing on training data, it was observed that R2 > 0.999 was achieved for both algorithms for all product selec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-04-01
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| Series: | International Journal of Sustainable Energy |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/14786451.2020.1803862 |
| _version_ | 1797680324962942976 |
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| author | Adewale George Adeniyi Joshua O. Ighalo Gonçalo Marques |
| author_facet | Adewale George Adeniyi Joshua O. Ighalo Gonçalo Marques |
| author_sort | Adewale George Adeniyi |
| collection | DOAJ |
| description | The aim of this study was to utilise artificial neural network (ANN) and AdaBoost (AB) algorithms to model the synthesis gas composition from the steam reforming of biomass bio-oil. At testing on training data, it was observed that R2 > 0.999 was achieved for both algorithms for all product selectivity indicating a 99.9% capture of data variability. Also, the RMSE values were <0.007 in most cases. The MAE values were <0.005 in most cases. The ANN predictions were observed to be more accurate than AB predictions for the current application. On the other hand, considering stratified 10-fold cross-validation the proposed models present R2 > 0.9 using AB considering hydrogen and carbon dioxide, and using ANN considering methane and carbon monoxide. |
| first_indexed | 2024-03-11T23:28:17Z |
| format | Article |
| id | doaj.art-dba7146f775349caaeb5456144bea59d |
| institution | Directory Open Access Journal |
| issn | 1478-6451 1478-646X |
| language | English |
| last_indexed | 2024-03-11T23:28:17Z |
| publishDate | 2021-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Energy |
| spelling | doaj.art-dba7146f775349caaeb5456144bea59d2023-09-20T10:33:46ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2021-04-0140431032510.1080/14786451.2020.18038621803862Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reformingAdewale George Adeniyi0Joshua O. Ighalo1Gonçalo Marques2Department of Chemical Engineering, University of IlorinDepartment of Chemical Engineering, University of IlorinInstitute of Telecommunications, University of Beira InteriorThe aim of this study was to utilise artificial neural network (ANN) and AdaBoost (AB) algorithms to model the synthesis gas composition from the steam reforming of biomass bio-oil. At testing on training data, it was observed that R2 > 0.999 was achieved for both algorithms for all product selectivity indicating a 99.9% capture of data variability. Also, the RMSE values were <0.007 in most cases. The MAE values were <0.005 in most cases. The ANN predictions were observed to be more accurate than AB predictions for the current application. On the other hand, considering stratified 10-fold cross-validation the proposed models present R2 > 0.9 using AB considering hydrogen and carbon dioxide, and using ANN considering methane and carbon monoxide.http://dx.doi.org/10.1080/14786451.2020.1803862annbiomassbio-oilhydrogenmachine learningsteam reforming |
| spellingShingle | Adewale George Adeniyi Joshua O. Ighalo Gonçalo Marques Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming International Journal of Sustainable Energy ann biomass bio-oil hydrogen machine learning steam reforming |
| title | Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming |
| title_full | Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming |
| title_fullStr | Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming |
| title_full_unstemmed | Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming |
| title_short | Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming |
| title_sort | utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio oil steam reforming |
| topic | ann biomass bio-oil hydrogen machine learning steam reforming |
| url | http://dx.doi.org/10.1080/14786451.2020.1803862 |
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