0
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,
NIT
Agartala 799046, Tripura, India

Subrata Bhowmik

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

Rajsekhar Panua

Department of Mechanical Engineering,
NIT
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Bhowmik, S. , Panua, R. S. , Ghosh, S. K. , Debroy, D. , and Paul, A. , 2017, “A Comparative Study of Artificial Intelligence Based Models to Predict Performance and Emission Characteristics of a Single Cylinder Diesel Engine Fueled With Diesosenol,” ASME J. Therm. Sci. Eng. Appl., 10(4), p. 041004. [CrossRef]
Bhowmik, S. , Panua, R. S. , Debroy, D. , and Paul, A. , 2017, “Artificial Neural Network Prediction of Diesel Engine Performance and Emission Fueled With Diesel-Kerosene-Ethanol Blends: A Fuzzy Based Optimization,” ASME J. Energy Resour. Technol., 139(4), p. 042201. [CrossRef]
Paul, A. , Panua, R. S. , Debroy, D. , and Bose, P. K. , 2015, “An Experimental Study of the Performance, Combustion and Emission Characteristics of a CI Engine Under Dual Fuel Mode Using CNG and Oxygenated Pilot Fuel Blends,” Energy, 86, pp. 560–573. [CrossRef]
Paul, A. , Bose, P. K. , Panua, R. S. , and Banerjee, R. , 2013, “An Experimental Investigation of Performance-Emission Trade Off of a CI Engine Fueled by Diesel–Compressed Natural Gas (CNG) Combination and Diesel–Ethanol Blends With CNG Enrichment,” Energy, 55, pp. 787–802. [CrossRef]
Paul, A. , Panua, R. S. , Debroy, D. , and Bose, P. K. , 2014, “Effect of Diethyl Ether and Ethanol on Performance, Combustion, and Emission of Single-Cylinder Compression Ignition Engine,” Int. J. Ambient Energy, 38(1), pp. 2–13.
Paul, A. , Bose, P. K. , Panua, R. S. , and Debroy, D. , 2015, “Study of Performance and Emission Characteristics of a Single Cylinder CI Engine Using Diethyl Ether and Ethanol Blends,” J. Energy Inst., 88(1), pp. 1–10. [CrossRef]
Park, S. H. , Cha, J. , Kim, H. J. , and Lee, C. S. , 2012, “Effect of Early Injection Strategy on Spray Atomization and Emission Reduction Characteristics in Bioethanol Blended Diesel Fueled Engine,” Energy, 39(1), pp. 375–87. [CrossRef]
Li, D. G. , Zhen, H. , Xingcai, L. , Wu-gao, Z. , and Yang, J. G. , 2005, “Physico-Chemical Properties of Ethanol-Diesel Blend Fuel and Its Effect on Performance and Emissions of Diesel Engines,” Renewable Energy, 30(6), pp. 967–76. [CrossRef]
Bhowmik S. , Paul A. , Panua R. S. , Ghosh S. K. , and Debroy D. , 2018, “Performance-Exhaust Emission Prediction of Diesosenol Fueled Diesel Engine: An ANN Coupled MORSM Based Optimization,” Energy, 153, pp. 212–222.
Shenghua, L. , Longbao, Z. , Ziyan, W. , and Jiang, R. , 2003, “Combustion Characteristics of Compressed Natural Gas/Diesel Dual-Fuel Turbocharged Compressed Ignition Engine,” J. Automob. Eng., 217(9), p. 833. [CrossRef]
Paul, A. , Panua, R. S. , Debroy, D. , and Bose, P. K. , 2015, “Effect of Diesel–Ethanol–PPME (Pongamia Pinata Methyl Ester) Blends as Pilot Fuel on CNG Dual-Fuel Operation of a CI Engine: A Performance-Emission Trade-Off Study,” Energy Fuels, 29(4), pp. 2394–2407. [CrossRef]
Paul, A. , Panua, R. S. , Debroy, D. , and Bose, P. K. , 2016, “A Performance Emission Trade Off Study of a CI Engine Fueled by Compressed Natural Gas (CNG)/Diesel–Ethanol-PPME Blend Combination,” Environ. Prog. Sustainable Energy, 35(2), pp. 517–530. [CrossRef]
Huang, J. , Wang, Y. , Li, S. , Roskilly, A. P. , Yu, H. , and Li, H. , 2009, “Experimental Investigation on the Performance and Emissions of a Diesel Engine Fuelled With Ethanol–Diesel Blends,” Appl. Therm. Eng., 29(11–12), pp. 2484–2490. [CrossRef]
Hardenberg, H. , and Schaefer, A. , 1981, “The Use of Ethanol as a Fuel for Compression Ignition Engines,” SAE Paper No. 811211.
Hansen, A. C. , Lyne P. W. L. , and Zhang, Q. , 2001, “Ethanol-Diesel Blends a Step Towards a Bio-Based Fuel for Diesel Engines,” ASAE Annual Meeting, Sacramento, CA, July 29–Aug. 1, ASAE Paper No. 2001—01e6048. https://www.researchgate.net/publication/255580785_ETHANOL-DIESEL_BLENDS_A_STEP_TOWARDS_A_BIO-BASED_FUEL_FOR_DIESEL_ENGINES
Lapuerta, M. , Armas, O. , and Herreros, J. M. , 2008, “Emissions From a Diesel Bioethanol Blend in an Automotive Diesel Engine,” Fuel, 87(1), pp. 25–31. [CrossRef]
Paul, A. , Panua, R. S. , and Debroy, D. , 2017, “An Experimental Study of Combustion, Performance, Exergy and Emission Characteristics of a CI Engine Fueled by Diesel-Ethanol-Biodiesel Blends,” Energy, 141, pp. 839–852. [CrossRef]
He, B. , Wang, J. , Yan, X. , Tian, X. , and Chen, H. , 2003, “Study on Combustion and Emission Characteristics of Diesel Engines Using Ethanol Blended Diesel Fuels,” SAE Paper No. 2003-01-0762.
Paul, A. , Panua, R. S. , Bose, P. K. , and Banerjee, R. , 2013, “An Experimental Study of Performance and Emission Parameters of a Compression Ignition Engine Fueled by Different Blends of Diesel-Ethanol-Biodiesel,” International Conference on Energy Efficient Technologies for Sustainability, Nagercoil, India, Apr. 10–12, pp. 786–791.
Kusaka, J. , Okamoto, T. , Daisho, Y. , Kihara, R. , and Saito, T. , 2000, “Combustion and Exhaust Gas Emission Characteristics of a Diesel Engine Dual- Fueled With Natural Gas,” JSAE Rev., 21(4), pp. 489–96. [CrossRef]
Papagiannakis, R. G. , Rakopoulos, C. D. , Hountalas, D. T. , and Rakopoulos, D. C. , 2010, “Emission Characteristics of High Speed, Dual Fuel, Compression Ignition Engine Operating in a Wide Range of Natural Gas/Diesel Fuel Proportions,” Fuel, 89(7), pp. 1397–406. [CrossRef]
Oguz, H. , Sarıtas, I. , and Baydan, H. E. , 2014, “Prediction of Diesel Engine Performance Using Biofuels With Artificial Neural Network,” Expert Syst. Appl., 37(9), pp. 6579–6586. [CrossRef]
Rezaei, J. , Shahbakhti, M. , Bahri, B. , and Aziz, A. A. , 2015, “Performance Prediction of HCCI Engines With Oxygenated Fuels Using Artificial Neural Networks,” Appl. Energy, 138, pp. 460–473. [CrossRef]
Ismail, H. M. , Ng, H. K. , Queck, C. W. , and Gan, S. , 2012, “Artificial Neural Networks Modelling of Engine-out Responses for a Light-Duty Diesel Engine Fuelled With Biodiesel Blends,” Appl. Energy, 92(0306–2619), pp. 769–777. [CrossRef]
Yusaf, T. F. , Buttsworth, D. R. , Saleh, K. H. , and Yousif, B. F. , 2010, “CNG Diesel Engine Performance and Exhaust Emission Analysis With the Aid of Artificial Neural Network,” Appl. Energy, 87(5), pp. 1661–1669. [CrossRef]
Najafi, G. , Ghobadian, B. , Tavakoli, T. , Buttsworth, D. R. , Yusaf, T. F. , and Faizollahnejad, M. , 2009, “Performance and Exhaust Emissions of a Gasoline Engine With Ethanol Blended Gasoline Fuels Using Artificial Neural Network,” Appl. Energy, 86(5), pp. 630–639. [CrossRef]
Channapattana, S. V. , Pawar, A. A. , and Kamble, P. G. , 2017, “Optimisation of Operating Parameters of DI-CI Engine Fueled With Second Generation Bio-Fuel and Development of ANN Based Prediction Model,” Appl. Energy, 187, pp. 84–95. [CrossRef]
Javed, S. , Murthy, Y. V. V. S. , Baig, R. U. , and Rao, D. P. , 2015, “Development of ANN Model for Prediction of Performance and Emission Characteristics of Hydrogen Dual Fueled Diesel Engine With Jatropha Methyl Ester Biodiesel Blends,” J. Nat. Gas Sci. Eng., 26, pp. 549–557. [CrossRef]
Ghobadian, B. , Rahimi, H. , Nikbakht, A. M. , Najafi, G. , and Yusaf, T. F. , 2009, “Diesel Engine Performance and Exhaust Emission Analysis Using Waste Cooking Biodiesel Fuel With an Artificial Neural Network,” Renewable Energy, 34(4), pp. 976–982. [CrossRef]
Dawson, C. W. , Abrahart, R. J. , and See, L. M. , 2007, “HydroTest: A Web-Based Toolbox of Evaluation Metrics for the Standardised Assessment of Hydrological Forecasts,” Environ. Modell. Software, 22(7), pp. 1034–1052. [CrossRef]
Bliemel, F. , 1973, “Theil's Forecast Accuracy Coefficient: A Clarification,” J. Mark. Res., 10(4), pp. 444–446. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Schematic of DI engine setup

Grahic Jump Location
Fig. 2

Flowchart of CNG injection methodology

Grahic Jump Location
Fig. 3

Overall correlation coefficient of developed model

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
Fig. 5

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

Grahic Jump Location
Fig. 6

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

Grahic Jump Location
Fig. 7

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

Grahic Jump Location
Fig. 8

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

Grahic Jump Location
Fig. 9

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

Grahic Jump Location
Fig. 10

Comparison model error matrices

Grahic Jump Location
Fig. 11

Comparison model correlation coefficient matrices

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In