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Research Papers

A Comparative Study of Artificial Intelligence Based Models to Predict Performance and Emission Characteristics of a Single Cylinder Diesel Engine Fueled With Diesosenol

[+] Author and Article Information
Subrata Bhowmik, Subrata Kumar Ghosh

Department of Mechanical Engineering,
IIT (ISM),
Dhanbad, Jharkhand 826004, India

Rajsekhar Panua

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

Durbadal Debroy, Abhishek Paul

Department of Mechanical Engineering,
NIT,
Agartala, Tripura 799046, India

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received August 24, 2017; final manuscript received October 11, 2017; published online March 30, 2018. Assoc. Editor: Matthew Oehlschlaeger.

J. Thermal Sci. Eng. Appl 10(4), 041004 (Mar 30, 2018) (11 pages) Paper No: TSEA-17-1315; doi: 10.1115/1.4038709 History: Received August 24, 2017; Revised October 11, 2017

This study investigates the potential of oxygenated additive (ethanol) on adulterated diesel fuel on the performance and exhaust emission characteristics of a single cylinder diesel engine. Based on the engine experimental data, two artificial intelligence (AI) models, viz., artificial neural network (ANN) and adaptive-neuro fuzzy inference system (ANFIS), have been modeled for predicting brake thermal efficiency (Bth), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbon (UBHC) and carbon monoxide (CO) with engine load (%), kerosene (vol %), and ethanol (vol %) as input parameters. Both the proposed AI models have the capacity for predicting input–output paradigms of diesel–kerosene–ethanol (diesosenol) blends with high accuracy. A (3–9–5) topology with Levenberg–Marquardt feed forward back propagation (trainlm) learning algorithm has been observed to be the ideal model for ANN. The comparative study of the two AI models demonstrated that ANFIS predicted results have higher accuracy than the ANN with a maximum RANFIS/RANN value of 1.000534.

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Figures

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Fig. 1

Schematic of IDI engine setup

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Fig. 2

Variation of MSE with respect to number of neurons

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Fig. 12

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

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Fig. 3

Overall R values of ANN 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 CO with experimentally measured CO

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Fig. 9

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

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Fig. 10

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

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Fig. 11

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

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Fig. 13

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

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Fig. 14

Comparison of model error matrices

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Fig. 15

Comparison of model correlation coefficient matrices

Tables

Errata

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