BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes
This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe2O3-based ฺBCLpro combining steam gasification for H2 production. Aspen Plus is used as...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114514/ http://psasir.upm.edu.my/id/eprint/114514/1/114514.pdf |
| _version_ | 1848866516293910528 |
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| author | Tuntiwongwat, Thanadol Thammawiset, Sippawit Srinophakun, Thongchai Rohitatisha Ngamcharussrivichai, Chawalit Sukpancharoen, Somboon |
| author_facet | Tuntiwongwat, Thanadol Thammawiset, Sippawit Srinophakun, Thongchai Rohitatisha Ngamcharussrivichai, Chawalit Sukpancharoen, Somboon |
| author_sort | Tuntiwongwat, Thanadol |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe2O3-based ฺBCLpro combining steam gasification for H2 production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H2 yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe2O3/Al2O3 mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H2 yield in BCLpro systems. Access to the tool can be obtained through the following link: http://bclh2pro.pythonanywhere.com/. |
| first_indexed | 2025-11-15T14:21:50Z |
| format | Article |
| id | upm-114514 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:21:50Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1145142025-01-16T11:50:46Z http://psasir.upm.edu.my/id/eprint/114514/ BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes Tuntiwongwat, Thanadol Thammawiset, Sippawit Srinophakun, Thongchai Rohitatisha Ngamcharussrivichai, Chawalit Sukpancharoen, Somboon This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe2O3-based ฺBCLpro combining steam gasification for H2 production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H2 yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe2O3/Al2O3 mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H2 yield in BCLpro systems. Access to the tool can be obtained through the following link: http://bclh2pro.pythonanywhere.com/. Elsevier B.V. 2024 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/114514/1/114514.pdf Tuntiwongwat, Thanadol and Thammawiset, Sippawit and Srinophakun, Thongchai Rohitatisha and Ngamcharussrivichai, Chawalit and Sukpancharoen, Somboon (2024) BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes. Energy and AI, 18. art. no. 100414. pp. 1-14. ISSN 2666-5468; eISSN: 2666-5468 https://linkinghub.elsevier.com/retrieve/pii/S2666546824000806 10.1016/j.egyai.2024.100414 |
| spellingShingle | Tuntiwongwat, Thanadol Thammawiset, Sippawit Srinophakun, Thongchai Rohitatisha Ngamcharussrivichai, Chawalit Sukpancharoen, Somboon BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| title | BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| title_full | BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| title_fullStr | BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| title_full_unstemmed | BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| title_short | BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| title_sort | bclh2pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes |
| url | http://psasir.upm.edu.my/id/eprint/114514/ http://psasir.upm.edu.my/id/eprint/114514/ http://psasir.upm.edu.my/id/eprint/114514/ http://psasir.upm.edu.my/id/eprint/114514/1/114514.pdf |