Predicting the Nitrogen Oxides Emissions of a Diesel Engine using Neural Networks Univ. of Bath

Format:

Conference/Event

Author/Creator:

Zhang, Zhang, author.

Contributor:

Akehurst, Sam

Brace, Chris

Burke, Richard

Pennycott, Andrew

Conference Name:

SAE 2015 World Congress & Exhibition (2015-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2015

Summary:

AbstractNitrogen oxides emissions are an important aspect of engine design and calibration due to increasingly strict legislation. As a consequence, accurate modeling of nitrogen oxides emissions from Diesel engines could play a crucial role during the design and development phases of vehicle powertrain systems. A key step in future engine calibration will be the need to capture the nonlinear behavior of the engine with respect to nitrogen oxides emissions within a rapid-calculating mathematical model. These models will then be used in optimization routines or on-board control features.In this paper, an artificial neural network structure incorporating a number of engine variables as inputs including torque, speed, oil temperature and variables related to fuel injection is developed as a method of predicting the production of nitrogen oxides based on measured test data. A multi-layer perceptron model is identified and validated using data from dynamometry tests.The model predicts exhaust nitrogen oxide concentrations under different engine conditions with satisfactory accuracy. The developed neural network model has potential applications in real-time control aimed at reducing nitrogen oxides emission levels

Notes:

Vendor supplied data

Publisher Number:

2015-01-1626

Access Restriction:

Restricted for use by site license