Research Papers: Fuel Combustion

Artificial Neural Network-Based Prediction of Performances-Exhaust Emissions of Diesohol Piloted Dual Fuel Diesel Engine Under Varying Compressed Natural Gas Flowrates

[+] Author and Article Information
Abhishek Paul, Durbadal Debroy

Department of Mechanical Engineering,
Agartala 799046, Tripura, India

Subrata Bhowmik

Department of Mechanical Engineering,
Dhanbad 826004, Jharkhand, India

Rajsekhar Panua

Department of Mechanical Engineering,
Agartala 799046, Tripura, India
e-mail: rajsekhar_panua@yahoo.co.in

1Corresponding author.

Contributed by the Internal Combustion Engine Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received December 12, 2017; final manuscript received May 17, 2018; published online June 12, 2018. Assoc. Editor: Stephen A. Ciatti.

J. Energy Resour. Technol 140(11), 112201 (Jun 12, 2018) (9 pages) Paper No: JERT-17-1705; doi: 10.1115/1.4040380 History: Received December 12, 2017; Revised May 17, 2018

The present study surveys the effects on performance and emission parameters of a partially modified single cylinder direct injection (DI) diesel engine fueled with diesohol blends under varying compressed natural gas (CNG) flowrates in dual fuel mode. Based on experimental data, an artificial intelligence (AI) specialized artificial neural network (ANN) model have been developed for predicting the output parameters, viz. brake thermal efficiency (Bth), brake-specific energy consumption (BSEC) along with emission characteristics such as oxides of nitrogen (NOx), unburned hydrocarbon (UBHC), carbon dioxide (CO2), and carbon monoxide (CO) emissions. Engine load, Ethanol share, and CNG strategies have been used as input parameters for the model. Among the tested models, the Levenberg–Marquardt feed-forward back propagation with three input neurons or nodes, two hidden layers with ten neurons in each layer and six output neurons, and tansig-purelin activation function have been found to the optimal model topology for the diesohol–CNG platforms. The statistical results acquired from the optimal network topology such as correlation coefficient (0.992–0.999), mean square error (MSE) (0.0001–0.0009), and mean absolute percentage error (MAPE) (0.09–2.41%) along with Nash–Sutcliffe coefficient of efficiency (NSE), Kling–Gupta efficiency (KGE), mean square relative error, and model uncertainty established itself as a real-time robust type machine learning tool under diesohol–CNG paradigms. The study also incorporated a special type of measure, namely Pearson's Chi-square test or goodness of fit, which brings up the model validation to a higher level.

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Fig. 2

Flowchart of CNG injection methodology

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Fig. 1

Schematic of DI engine setup

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Fig. 3

Overall correlation coefficient of developed model

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Fig. 4

((a) and (b)) Comparison of ANN predicted Bth with experimentally measured Bth

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Fig. 5

((a) and (b)) Comparison of ANN predicted BSEC with experimentally measured BSEC

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Fig. 6

((a) and (b)) Comparison of ANN predicted NOx with experimentally measured NOx

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Fig. 7

((a) and (b)) Comparison of ANN predicted UBHC with experimentally measured UBHC

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Fig. 8

((a) and (b)) Comparison of ANN predicted CO2 with experimentally measured CO2

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Fig. 9

((a) and (b)) Comparison of ANN predicted CO with experimentally measured CO

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Fig. 10

Comparison model error matrices

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Fig. 11

Comparison model correlation coefficient matrices



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