A New Approach for Improving Biodiesel Conversion Efficiency: A Stacking Ensemble Model Based on Linear Regression Approach with GAN-Enhanced

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dc.contributor.authorKaraoglu, Ahmet
dc.contributor.authorSoyler, Huseyin
dc.date.accessioned2026-04-25T14:19:54Z
dc.date.available2026-04-25T14:19:54Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractThis study employs a Linear Regression-based stacking ensemble learning approach as a novel method to enhance biodiesel conversion efficiency. Initially, a dataset derived from the literature was used to train an ensemble model that combines predictions from Random Forest, XGBoost, and Deep Neural Network (DNN) through a Linear Regression-based fusion approach. This model outperformed individual models (Random Forest: - 0.16, XGBoost: - 0.67, and DNN: 0.36) by achieving an R2 score of 0.45. To further improve model performance, 4900 synthetic data samples were generated and integrated into the dataset. Leveraging the stacking ensemble learning approach with this expanded dataset, the model demonstrated a significant improvement in predictive accuracy, achieving an R2 score of 0.81. This corresponds to an approximate 4% increase in performance compared to individual models (Random Forest: 0.78, XGBoost: 0.78, and DNN: 0.77), highlighting the effectiveness of ensemble learning in optimizing biodiesel conversion efficiency. Additionally, the model exhibited high accuracy with low error rates (MAE: 1.16 and MAPE: 1.24%), effectively compensating for the weaknesses of individual models and providing more stable and generalized predictions. To the best of our knowledge, this is the first study to incorporate a Linear Regression-based stacking method to enhance biodiesel conversion efficiency. These findings underscore the potential of ensemble learning techniques and synthetic data integration in improving renewable fuel efficiency. Future research can further enhance model performance by incorporating larger datasets and exploring more advanced ensemble strategies.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK).
dc.identifier.doi10.1007/s13369-025-10227-5
dc.identifier.endpage19441
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue23
dc.identifier.orcid0000-0002-7507-3031
dc.identifier.orcid0000-0002-1216-7049
dc.identifier.scopus2-s2.0-105005235462
dc.identifier.scopusqualityQ1
dc.identifier.startpage19421
dc.identifier.urihttps://doi.org/10.1007/s13369-025-10227-5
dc.identifier.urihttps://hdl.handle.net/11486/8232
dc.identifier.volume50
dc.identifier.wosWOS:001494448200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal for Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectBiodiesel conversion
dc.subjectStacking ensemble
dc.subjectLinear regression
dc.subjectSynthetic data
dc.subjectMachine learning
dc.titleA New Approach for Improving Biodiesel Conversion Efficiency: A Stacking Ensemble Model Based on Linear Regression Approach with GAN-Enhanced
dc.typeArticle

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