This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously “unseen” real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement.


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    Titel :

    Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    International Fuels & Lubricants Meeting & Exposition ; 1999



    Erscheinungsdatum :

    1999-05-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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