Machine learning based prediction of TOPSIS scores for internal combustion engine performance and emissions
Scientific Reports, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-64316-y
- Dergi Adı: Scientific Reports
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Artificial intelligence (Industry innovation and infrastructure), Artificial intelligence Engine Performance Modeling, Ensemble Learning, Internal Combustion Engine, Multi-Criteria Decision Making, TOPSIS
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Yozgat Bozok Üniversitesi Adresli: Evet
Özet
Internal combustion engines operating on alternative fuels face an inherent challenge: optimizing power output and fuel efficiency while simultaneously minimizing exhaust emissions requires the simultaneous management of multiple, often conflicting objectives. Although multi-criteria decision-making (MCDM) and machine learning (ML) methods have each been applied extensively in engine research, they are typically used in isolation, and the direct prediction of a composite MCDM decision score from engine control parameters has not previously been demonstrated. This study addresses that gap by proposing a hybrid TOPSIS-Stacking framework in which a TOPSIS-based composite score, aggregating eight performance and emission indicators into a single figure of merit, is itself treated as a continuous regression target. Experimental data from a single-cylinder Otto-cycle engine operating across a range of ethanol-water blend ratios (0 to 50% water by volume), two compression ratios (7.44:1 and 9.44:1), and five rotational speeds (2000 to 4000 rpm) were used. Entropy-based weighting was adopted to assign objective criterion importance, and three regression models (RSM, XGBoost, and a stacking ensemble combining both) were trained to predict the TOPSIS score from four engine control parameters. A 5-fold Group Cross-Validation strategy was implemented to prevent repetition leakage between experimental replicates and ensure conservative generalization estimates. The stacking ensemble achieved the highest predictive accuracy (R² = 0.814, RMSE = 0.061, MAPE = 6.36%), outperforming both individual models. Feature importance analysis identified water fraction as the dominant predictor, followed by compression ratio, consistent with their thermodynamic influence on NOx formation and thermal efficiency. These results demonstrate that the proposed framework provides a reliable and computationally efficient tool for virtual engine calibration and scenario-based performance evaluation, significantly reducing the need for exhaustive multi-criteria experimental campaigns.