This 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 R ² 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 R ² 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.

Bu çalışma, Sinop Üniversitesi Gerze Mes+lek Yüksekokulu’nun 2024 yılı karbon ayak izini IPCC Tier 1 ve DEFRA yöntemleri kullanarak analiz etmektedir. Hesaplamalarda doğalgaz, elektrik tüketimi ve ulaşım faaliyetlerinden kaynaklanan sera gazı emisyonları değerlendirilmiştir. Bulgular, ısıtma için kullanılan doğalgazın en yüksek emisyona neden olduğunu, ardından elektrik tüketimi ve ulaşım kaynaklı emisyonların geldiğini göstermiştir. Emisyon hesaplamalarında belirsizlikleri değerlendirmek amacıyla 10.000 iterasyon üzerinden Monte Carlo simülasyonu gerçekleştirilmiştir. Simülasyon sonuçları, toplam emisyon değerinin %95 güven aralığında 124.555 kg ile 139.163 kg arasında değiştiğini ortaya koymuştur. Ortalama karbon emisyon değeri ise 131.895 kg CO₂ olarak hesaplanmıştır. Çalışmanın sonucunda, Gerze MYO’nun toplam karbon emisyonu 137.404,94 kg olarak belirlenmiştir. Bulgular, enerji verimliliğini artırma, yenilenebilir enerji kullanımını teşvik etme ve yeşil ulaşım çözümlerine yönelmenin önemini vurgulamaktadır. Bu çalışma, eğitim kurumları için karbon nötrlüğüne ulaşmada yol gösterici bir rehber niteliğindedir.

This study comparatively investigates the effects of graphite and titanium electrodes on process performance, energy consumption, and product quality in the electrolysis-based production of biodiesel from waste cooking oil (WCO). To optimize the production parameters, a Taguchi L18 orthogonal array and Response Surface Methodology (RSM) were systematically employed. The results demonstrated that while graphite electrodes operate effectively at lower voltages (approximately 20 V), titanium electrodes require higher voltage levels (60 V) to achieve significant conversion. Under optimized conditions, titanium electrodes achieved a remarkably high biodiesel conversion yield of 93.00% with a specific energy consumption (SEC) of approximately 1.19 kWh/kg, whereas graphite electrodes yielded 83.45% with a significantly lower SEC of 0.25 kWh/kg. Statistical analysis (ANOVA) revealed that the KOH catalyst concentration was not a statistically significant factor within the narrow range tested (0.5-1.0 wt%), suggesting that electrolysis operates through a different catalytic mechanism involving in-situ ion generation. Fuel property characterizations, including GC-MS analysis and cold-flow properties (CFPP of -26.86 C for graphite and − 23.67 C for titanium), confirmed that biodiesels produced with both electrode types show a close alignment with EN 14,214 standards. This research highlights that electrode material selection is a decisive factor influencing not only the conversion efficiency and energy intensity but also the molecular-level characteristics of the resulting fuel.