The development of artificial neural network for prediction of performance and emissions in a compressed natural gas engine with direct injection system

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper describes the applicable and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in the term of indicated power and emissions in the appearance of CO and NO emissions level. A series of numerical computations by mean of computational fluid dynamics (CFD) code were carried out based on the statistics-based design of experiment method. The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN). The operating conditions employed to represent the combustion parameters for controlling the injection and ignition event are start of injection (SOI), end of injection (EOI) and spark advance (SA) timing, which affects to the combustion processes, performance as well as emissions formation. The developed ANN was identified as a black box model with input and output data, which does not require priori knowledge. There were 15 data acquired from the CFD combustion simulation to characterize the combustion behavior of such engine used for training process of ANN, and 5 test data used for verifying the model. The study results showed that the predicted results were in good agreement with the full CFD simulation. This circumstance proves that the developed ANN model has the competence to successfully predict the performance and emissions of CNG-DI engine.

Original languageEnglish
Title of host publicationSAE Technical Papers
DOIs
Publication statusPublished - 2007
EventPowertrain and Fluid Systems Conference and Exhibition - Rosemont, IL
Duration: 29 Oct 20071 Nov 2007

Other

OtherPowertrain and Fluid Systems Conference and Exhibition
CityRosemont, IL
Period29/10/071/11/07

Fingerprint

Compressed natural gas
Gas engines
Direct injection
Neural networks
Engines
Computational fluid dynamics
Backpropagation
Electric sparks
Design of experiments
Artificial intelligence
Ignition
Statistics
Computer simulation

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

Cite this

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title = "The development of artificial neural network for prediction of performance and emissions in a compressed natural gas engine with direct injection system",
abstract = "This paper describes the applicable and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in the term of indicated power and emissions in the appearance of CO and NO emissions level. A series of numerical computations by mean of computational fluid dynamics (CFD) code were carried out based on the statistics-based design of experiment method. The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN). The operating conditions employed to represent the combustion parameters for controlling the injection and ignition event are start of injection (SOI), end of injection (EOI) and spark advance (SA) timing, which affects to the combustion processes, performance as well as emissions formation. The developed ANN was identified as a black box model with input and output data, which does not require priori knowledge. There were 15 data acquired from the CFD combustion simulation to characterize the combustion behavior of such engine used for training process of ANN, and 5 test data used for verifying the model. The study results showed that the predicted results were in good agreement with the full CFD simulation. This circumstance proves that the developed ANN model has the competence to successfully predict the performance and emissions of CNG-DI engine.",
author = "Kurniawan, {Wendy H.} and Shahrir Abdullah and {Mohd Nopiah}, Zulkifli and Kamaruzzaman Sopian",
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