An analysis for effect of cetane number on exhaust emissions from engine with the neural network

Decoupling cetane number from the other compositions and properties of diesel fuel, the individual effect of cetane number on the exhaust emissions from an engine may be researched. This paper has presented a back-propagation neural network model predicting the exhaust emissions from an engine with the inputs of total cetane number, base cetane number and cetane improver, total cetane number and nitrogen content in the diesel fuel; as well as the output of the exhaust emissions of hydrocarbon (HC), carbon oxide (CO), particulate matter (PM) and nitrogen oxide (NO_x). An optimal design has been completed for the number of hidden layers, the number of hidden neurons, the activation function, and the goal errors, along with the initial weights and biases in the back-propagation neural network model. HC, CO, PM and NO_x have been predicted with the model, the effects of cetane improver and nitrogen content on them have also been analyzed, and better results have been achieved.