An assessment of greenhouse gases emission from diesel engine by adding carbon nanotube to biodiesel fuel using machine learning technique

Abstract

Abstract Due to the depletion of fossil fuel reserves, the significant pollution produced during their combustion and the increasing costs, biodiesel sources have gained recognition as an attractive alternative energy source. The integration of carbon nanotubes (CNTs) as a catalyst with biofuels such as biodiesel and bioethanol has the potential to optimize engine performance and reduce emissions when used in conjunction with diesel fuel. An emissions and performance prediction model for diesel engines is introduced in this research, utilizing biodiesel and CNTs in conjunction with machine learning. Due to its proficiency in forecasting systems with limited data, the emotional artificial neural network (EANN) model of machine learning was implemented. As an innovative approach, this study considers the following variables: fuel calorific value, fuel speed, engine density, viscosity, fuel consumption, exhaust gas temperature, oil temperature, oxygen output from exhaust gas, humidity, ambient temperature and ambient air pressure. The model was informed of every effective technical and functional environment parameter. This study additionally assessed the pollution and engine performance forecasts generated by the EANN model. Adding 5% biodiesel to gasoline fuel decreased carbon monoxide emissions while increasing torque and braking power, according to the findings. The fuel’s specific consumption increased. These findings were consistent with previous investigations. Moreover, as the concentration of CNTs in the fuel mixture increased, NOx, NO, CO2 and CO emissions decreased. The addition of 120 ppm of CNT to biodiesel–diesel fuel decreased emissions of CO, NO, NO2 and NO by 12.90%, 14.53%, 18.80% and 47.68%, respectively. The performance of the EANN model was found to be optimal when trained with the rectified linear unit activation function, as demonstrated by the evaluation results using various neurons.