Akademik Veri Yönetim Sistemi
Energies, cilt.19, sa.7, 2026 (SCI-Expanded, Scopus)
In this study, a novel “Physics-Informed Hybrid Machine Learning” framework was developed to model and optimize the complex combustion and carbon-based emission characteristics of Cu2O nano-additive doped diesel fuel. To reduce reliance on purely empirical correlations, the proposed framework integrates alterations in fuel physical properties into the prediction loop, thereby enhancing physical consistency and model generalizability. The methodology comprises data pre-processing, modeling via Gaussian Process Regression (GPR) with an Automatic Relevance Determination (ARD) kernel, and multi-objective optimization using NSGA-II. Experimental tests were conducted at a constant engine speed of 2000 rpm under varying load conditions. The developed hybrid model exhibited high predictive accuracy, particularly for performance metrics and gaseous emissions (e.g., R2 > 0.95 for BSFC and CO). ARD-based feature importance analysis confirmed that nano-additive dosage plays a critical role in the fine-tuning of emissions. Crucially, the optimization algorithm identified a nano-additive dosage of ~29 ppm and an engine load of 15.5 Nm as the optimal operating point for the simultaneous improvement of performance and carbonaceous emissions. This finding, exploring the unmeasured design space, demonstrates the framework’s capability to discover optimal conditions beyond discrete experimental points.