Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload
This study explores non-linear tree-based learning algorithms for modeling biodiesel reactions. A dataset of 3038 reaction samples from 111 published studies was compiled, each optimizing distinct biodiesel reaction systems. Key operational parameters were selected to represent the dataset's di...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Fuel Processing Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S037838202500089X |
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| _version_ | 1849689440335691776 |
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| author | Iver Bergh Hvidsten Kristian Hovde Liland Oliver Tomic Jorge Mario Marchetti |
| author_facet | Iver Bergh Hvidsten Kristian Hovde Liland Oliver Tomic Jorge Mario Marchetti |
| author_sort | Iver Bergh Hvidsten |
| collection | DOAJ |
| description | This study explores non-linear tree-based learning algorithms for modeling biodiesel reactions. A dataset of 3038 reaction samples from 111 published studies was compiled, each optimizing distinct biodiesel reaction systems. Key operational parameters were selected to represent the dataset's diversity. Random forest (RF) and gradient boosting regressor (GBR) models were employed to predict biodiesel yield across the various reaction systems. GBR, with 1000 estimators and a tree depth of 5, achieved the best performance (R2 = 0.744, RMSE = 10.783). The global GBR model was comprehensively evaluated for accuracy and physical relevance, with proposed applications in component screening and reaction optimization using the DIRECT-l (DIviding RECTangles - locally biased version) algorithm. Additionally, an experimental reaction was optimized via the global model and DIRECT-l, then refined using a retrained local model for improved system-specific predictions. These models offer researchers a data-driven approach to selecting and optimizing biodiesel reactions, reducing laboratory time and improving predictive accuracy for specific systems. |
| format | Article |
| id | doaj-art-b96422cb2e844360b51ad054e301b146 |
| institution | DOAJ |
| issn | 0378-3820 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Fuel Processing Technology |
| spelling | doaj-art-b96422cb2e844360b51ad054e301b1462025-08-20T03:21:38ZengElsevierFuel Processing Technology0378-38202025-10-0127510826510.1016/j.fuproc.2025.108265Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workloadIver Bergh Hvidsten0Kristian Hovde Liland1Oliver Tomic2Jorge Mario Marchetti3Faculty of Science and Technology, Realtek, Department of Physics, Norwegian University of Life Sciences, Ås, NorwayFaculty of Science and Technology, Realtek, Department of Industrial Economics & Technology Management, Norwegian University of Life Sciences, Ås, NorwayFaculty of Science and Technology, Realtek, Department of Data Science, Norwegian University of Life Sciences, Ås, NorwayFaculty of Science and Technology, Realtek, Department of Physics, Norwegian University of Life Sciences, Ås, Norway; Corresponding author at: Faculty of Science and Technology, Realtek, Norwegian University of Life Sciences, Drøbakveien 31, 1432 ÅS, Norway.This study explores non-linear tree-based learning algorithms for modeling biodiesel reactions. A dataset of 3038 reaction samples from 111 published studies was compiled, each optimizing distinct biodiesel reaction systems. Key operational parameters were selected to represent the dataset's diversity. Random forest (RF) and gradient boosting regressor (GBR) models were employed to predict biodiesel yield across the various reaction systems. GBR, with 1000 estimators and a tree depth of 5, achieved the best performance (R2 = 0.744, RMSE = 10.783). The global GBR model was comprehensively evaluated for accuracy and physical relevance, with proposed applications in component screening and reaction optimization using the DIRECT-l (DIviding RECTangles - locally biased version) algorithm. Additionally, an experimental reaction was optimized via the global model and DIRECT-l, then refined using a retrained local model for improved system-specific predictions. These models offer researchers a data-driven approach to selecting and optimizing biodiesel reactions, reducing laboratory time and improving predictive accuracy for specific systems.http://www.sciencedirect.com/science/article/pii/S037838202500089XBiodieselTransesterificationData miningGradient boosting regressorSHAPDIRECT-l |
| spellingShingle | Iver Bergh Hvidsten Kristian Hovde Liland Oliver Tomic Jorge Mario Marchetti Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload Fuel Processing Technology Biodiesel Transesterification Data mining Gradient boosting regressor SHAP DIRECT-l |
| title | Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload |
| title_full | Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload |
| title_fullStr | Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload |
| title_full_unstemmed | Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload |
| title_short | Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload |
| title_sort | modeling of biodiesel production using optimization designs from literature aiming to reduce the laboratory workload |
| topic | Biodiesel Transesterification Data mining Gradient boosting regressor SHAP DIRECT-l |
| url | http://www.sciencedirect.com/science/article/pii/S037838202500089X |
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