Waste tyre pyrolysis oil–hemp biodiesel ternary blends with graphene oxide and thermal barrier coating: CI engine performance and machine learning prediction

emissions. GO and TBC were the main determining variables for HC and smoke. Overall, the results prove XGBoost as the most efficient multi-output model for characterisation of CI engines, particularly in terms of complex interactions involved between fuel, additives, and hardware. The B20GO50 + TBC configuration achieved a BTE of 36.22%, an absolute gain of 6.65% points over neat diesel at full load, yielding an estimated fuel cost saving of INR 6000-7800 per 1000 operating hours at current diesel prices. Concurrently, CO was reduced by 40% and HC by 25.6%, demonstrating regulatory compliance potential under BS-VI emission norms without engine hardware modification.