Comparative Study of Emission Prediction Using Deep Learning Models

Authors : İhsan Uluocak

Pages : 337-346

Doi:10.21605/cukurovaumfd.1648164

View : 40 | Download : 23

Publication Date : 2025-07-02

Article Type : Research Paper

Abstract :This study investigates the prediction of exhaust emissions (CO, CO₂, and NOx) from a diesel engine fueled with biodiesel-diesel blends and compressed natural gas (CNG) using deep learning models. Biodiesel derived from canola, sunflower, and corn oils was blended with conventional, while CNG was introduced at flow rates of 0, 5, 10, and 15 liters per minute (lt/min). Two deep learning architectures, Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM), were employed to predict emissions. The models\\\' performance was evaluated using metrics such as R², RMSE, and Kling-Gupta Efficiency (KGE). The results demonstrated that both models achieved high accuracy, with R² and KGE values exceeding 0.93 for all emission types. The GRU model showed superior performance in predicting CO and NOx emissions, while the LSTM model excelled in predicting CO₂ emissions. The study highlights the potential of deep learning models in accurately predicting exhaust emissions and optimizing fuel blends for reduced environmental impact.
Keywords : Optimizasyon, Derin Öğrenme, CNG, Biyodizel

ORIGINAL ARTICLE URL