Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR

Sumit Roy; Ashmita Ghosh; Ajoy Kumar Das; Rahul Banerjee

doi:10.1016/j.apenergy.2014.11.065

Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR

Sumit Roy, Ashmita Ghosh, Ajoy Kumar Das, Rahul Banerjee

NIT Agartala

Research output: Contribution to journal › Article › peer-review

46 Citations (Scopus)

Abstract

Gene Expression Programming was employed to express the relationship between the inputs and the outputs of a single cylinder four-stroke CRDI engine coupled with EGR. The performance and emission parameters (BSFC, BTE, CO₂, NOx and PM) have been modelled by Gene Expression Programming where load, fuel injection pressure, EGR and fuel injected per cycle were chosen as input parameters. From the results it was found that the GEP can consistently emulate actual engine performance and emission characteristics proficiently even under different modes of CRDI operation with EGR with significant accuracy. Moreover, the GEP obtained results were also compared with an ANN model, developed on the same parametric ranges. The comparison of the obtained results showed that the GEP model outperforms the ANN model in predicting the desired response variables.

Original language	English
Pages (from-to)	52-64
Number of pages	13
Journal	Applied Energy
Volume	140
Early online date	13 Dec 2014
DOIs	https://doi.org/10.1016/j.apenergy.2014.11.065
Publication status	Published - 5 Feb 2015
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2014 Elsevier Ltd.

Keywords

Artificial neural network
CRDI
EGR
Engine performance
Exhaust emissions
Gene expression programming

ASJC Scopus subject areas

Building and Construction
Mechanical Engineering
Energy(all)
Management, Monitoring, Policy and Law

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10.1016/j.apenergy.2014.11.065

Cite this

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title = "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR",

abstract = "Gene Expression Programming was employed to express the relationship between the inputs and the outputs of a single cylinder four-stroke CRDI engine coupled with EGR. The performance and emission parameters (BSFC, BTE, CO2, NOx and PM) have been modelled by Gene Expression Programming where load, fuel injection pressure, EGR and fuel injected per cycle were chosen as input parameters. From the results it was found that the GEP can consistently emulate actual engine performance and emission characteristics proficiently even under different modes of CRDI operation with EGR with significant accuracy. Moreover, the GEP obtained results were also compared with an ANN model, developed on the same parametric ranges. The comparison of the obtained results showed that the GEP model outperforms the ANN model in predicting the desired response variables.",

keywords = "Artificial neural network, CRDI, EGR, Engine performance, Exhaust emissions, Gene expression programming",

author = "Sumit Roy and Ashmita Ghosh and Das, {Ajoy Kumar} and Rahul Banerjee",

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doi = "10.1016/j.apenergy.2014.11.065",

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AU - Ghosh, Ashmita

AU - Das, Ajoy Kumar

AU - Banerjee, Rahul

PY - 2015/2/5

Y1 - 2015/2/5

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AB - Gene Expression Programming was employed to express the relationship between the inputs and the outputs of a single cylinder four-stroke CRDI engine coupled with EGR. The performance and emission parameters (BSFC, BTE, CO2, NOx and PM) have been modelled by Gene Expression Programming where load, fuel injection pressure, EGR and fuel injected per cycle were chosen as input parameters. From the results it was found that the GEP can consistently emulate actual engine performance and emission characteristics proficiently even under different modes of CRDI operation with EGR with significant accuracy. Moreover, the GEP obtained results were also compared with an ANN model, developed on the same parametric ranges. The comparison of the obtained results showed that the GEP model outperforms the ANN model in predicting the desired response variables.

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KW - Exhaust emissions

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Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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