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Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG

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Abstract

This experimental work highlights the inherent capability of an adaptive-neuro fuzzy inference system (ANFIS) based model to act as a robust system identification tool (SIT) in prognosticating the performance and emission parameters of an existing diesel engine running of diesel-LPG dual fuel mode. The developed model proved its adeptness by successfully harnessing the effects of the input parameters of load, injection duration and LPG energy share on output parameters of BSFCEQ, BTE, NOX, SOOT, CO and HC. Successive evaluation of the ANFIS model, revealed high levels of resemblance with the already forecasted ANN results for the same input parameters and it was evident that similar to ANN, ANFIS also has the innate ability to act as a robust SIT. The ANFIS predicted data harmonized the experimental data with high overall accuracy. The correlation coefficient (R) values are stretched in between 0.99207 to 0.999988. The mean absolute percentage error (MAPE) tallies were recorded in the range of 0.02–0.173% with the root mean square errors (RMSE) in acceptable margins. Hence the developed model is capable of emulating the actual engine parameters with commendable ranges of accuracy, which in turn would act as a robust prediction platform in the future domains of optimization.

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Abbreviations

ANFIS:

Adaptive-Neuro Fuzzy Inference System

ANN:

Artificial Neural Network

BDO:

Baseline Diesel Operation

BSFCEQ :

Equivalent Brake Specific Fuel Consumption

BTE:

Brake Thermal Efficiency

CI:

Compression Ignition

LPG:

Liquefied Petroleum Gas

DI:

Direct Injection

HC:

Hydrocarbon

IC:

Internal Combustion

MAPE:

Mean Absolute Percentage Error

NOx:

Oxides of Nitrogen

CO:

Carbon Monoxide

ppm:

Parts Per Million

R:

Correlation Coefficient

RMSE:

Root Mean Square Error

MSRE:

Mean Squared Relative Error

KGE:

Kling-Gupta Efficiency

NSCE:

Nash-Sutcliffe Coefficient of Efficiency

SIT:

System Identification Tool

References

  1. Jothi NM, Nagarajan G, Renganarayanan S (2007) Experimental studies on homogeneous charge CI engine fueled with LPG using DEE as an ignition enhancer. Renewable Energy 32(9):1581–1593

    Article  Google Scholar 

  2. Li GX, Yao BF (2008) Nonlinear dynamics of cycle-to-cycle combustion variations in a lean-burn natural gas engine. Applied Thermal Engineering 28(5):611–620

    Article  MathSciNet  Google Scholar 

  3. Poonia M, Ramesh A, Gaur R (1999) Experimental investigation of the factors affecting the performance of a LPG-diesel dual fuel engine. 0148-7191, SAE Technical Paper

  4. Selim MY, Radwan M, Saleh H (2008) Improving the performance of dual fuel engines running on natural gas/LPG by using pilot fuel derived from jojoba seeds. Renewable energy 33(6):1173–1185

    Article  Google Scholar 

  5. Beroun S, Martins J (2001) The development of gas (CNG, LPG and H2) engines for buses and trucks and their emission and cycle variability characteristics. 0148-7191, SAE Technical Paper

  6. Johnson E (2003) LPG: a secure, cleaner transport fuel? A policy recommendation for Europe. Energy Policy 31(15):1573–1577

    Article  Google Scholar 

  7. Yusaf TF, Buttsworth D, Saleh KH, Yousif B (2010) CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Applied Energy 87(5):1661–1669

    Article  Google Scholar 

  8. Ayhan V, Parlak A, Cesur I, Boru B, Kolip A (2011) Performance and exhaust emission characteristics of a diesel engine running with LPG. International Journal of Physical Sciences 6(8):1905–1914

    Google Scholar 

  9. Barata JM (1995) Performance and emissions of a dual fueled DI diesel engine. 0148-7191, SAE Technical Paper

  10. Chen Z, Konno M, Goto S (2001) Study on homogenous premixed charge CI engine fueled with LPG. JSAE review 22(3):265–270

    Article  Google Scholar 

  11. Liu Z, Karim G (1997) An examination of the exhaust emissions of gas fueled diesel engines. PennWell Conferences and Exhibitions, Houston

  12. Chakraborty A, Roy S, Banerjee R (2016) An experimental based ANN approach in mapping performance-emission characteristics of a diesel engine operating in dual-fuel mode with LPG. Journal of Natural Gas Science and Engineering 28:15–30

    Article  Google Scholar 

  13. Nekooei J, Jaswar AP (2014) Review on combustion control of marine engine by fuzzy logic control concerning the air to fuel ratio. Jurnal Teknologi 66(2):103–106

    Google Scholar 

  14. Alla GA, Soliman H, Badr O, Rabbo MA (2000) Effect of pilot fuel quantity on the performance of a dual fuel engine. Energy Conversion and Management 41(6):559–572

    Article  Google Scholar 

  15. Lounici MS et al (2014) Towards improvement of natural gas-diesel dual fuel mode: An experimental investigation on performance and exhaust emissions. Energy 64:200–211

    Article  Google Scholar 

  16. Papagiannakis R, Hountalas D (2003) Experimental investigation concerning the effect of natural gas percentage on performance and emissions of a DI dual fuel diesel engine. Applied Thermal Engineering 23(3):353–365

    Article  Google Scholar 

  17. Papagiannakis R, Hountalas D (2004) Combustion and exhaust emission characteristics of a dual fuel compression ignition engine operated with pilot diesel fuel and natural gas. Energy conversion and management 45(18-19):2971–2987

    Article  Google Scholar 

  18. Roy MM, Tomita E, Kawahara N, Harada Y, Sakane A (2010) An experimental investigation on engine performance and emissions of a supercharged H2-diesel dual-fuel engine. International Journal of Hydrogen Energy 35(2):844–853

    Article  Google Scholar 

  19. Sahoo B, Sahoo N, Saha U (2009) Effect of engine parameters and type of gaseous fuel on the performance of dual-fuel gas diesel engines—A critical review. Renewable and Sustainable Energy Reviews 13(6-7):1151–1184

    Article  Google Scholar 

  20. Saleh H (2008) Effect of variation in LPG composition on emissions and performance in a dual fuel diesel engine. Fuel 87(13):3031–3039

    Article  Google Scholar 

  21. Sayin C, Ertunc HM, Hosoz M, Kilicaslan I, Canakci M (2007) Performance and exhaust emissions of a gasoline engine using artificial neural network. Applied thermal engineering 27(1):46–54

    Article  Google Scholar 

  22. Vijayabalan P, Nagarajan G (2009) Performance, emission and combustion of LPG diesel dual fuel engine using glow plug. JJMIE 2:105–110

    Google Scholar 

  23. Weaver CS, Turner SH (1994) Dual fuel natural gas/diesel engines: technology, performance, and emissions. 0148-7191, SAE Technical Paper

  24. Yoon SH, Lee CS (2011) Experimental investigation on the combustion and exhaust emission characteristics of biogas–biodiesel dual-fuel combustion in a CI engine. Fuel processing technology 92(5):992–1000

    Article  Google Scholar 

  25. Li G, Sapsford SM, Morgan RE (2000) CFD Simulation of DI Diesel Truck Engine Combustion Using VECTIS. 0148-7191, SAE Technical Paper

  26. KangaraniFarahani M, Mehralian S (2013) Comparison between Artificial Neural Network and neuro-fuzzy for gold price prediction, Fuzzy Systems (IFSC), 2013 13th Iranian Conference on. IEEE, pp. 1-5

  27. Zadeh LA (1996) Fuzzy sets. In: Zadeh LA (ed) Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers, pp 394–432

  28. García-Nieto S, Salcedo J, Martínez M, Laurí D (2009) Air management in a diesel engine using fuzzy control techniques. Inf Sci 179(19):3392–3409

    Article  MATH  Google Scholar 

  29. Ghaffari A, Shamekhi AH, Saki A, Kamrani E (2008) Adaptive fuzzy control for air-fuel ratio of automobile spark ignition engine. World Acad Sci Eng Technol 48:284–292

    Google Scholar 

  30. Radziszewski L, Kekez M (2010) Application of a genetic-fuzzy system to diesel engine pressure modeling. Int J Adv Manuf Technol 46(1–4):1–9

    Article  Google Scholar 

  31. Piltan F, Sulaiman N, Talooki IA, Ferdosali P (2011) Control of IC engine: design a novel MIMO fuzzy backstepping adaptive based fuzzy estimator variable structure control. Int J Robot Autom 2(5):360–380

    Google Scholar 

  32. Sakthivel G, Snehitkumar B, Ilangkumaran M (2016) Application of fuzzy logic in internal combustion engines to predict the engine performance. Int J Ambient Energy 37(3):273–283

    Article  Google Scholar 

  33. Sona N (2013) Fuzzy logic controller for the speed control of an IC engine using Matlab\Simulink

  34. Wu JD, Wang YH, Bai MR (2007) Development of an expert system for fault diagnosis in scooter engine platform using fuzzy-logic inference. Expert Systems with Applications, 33(4):1063–1075

  35. Kasabov NK (1996) Foundations of neural networks, fuzzy systems, and knowledge engineering. Marcel Alencar

  36. Mayilvaganan MK et al (2011) Comparative Study of ANN and ANFIS for the Prediction of Groundwater Level of a Watershed. Global Journal of Mathematical Science: Theory and Practical 3(4):299–306

    Google Scholar 

  37. Roy S, Banerjee R, Bose PK (2014a) Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network. Applied Energy 119:330–340

    Article  Google Scholar 

  38. Shayler PJ, Goodman M, Ma T (2000) The exploitation of neural networks in automotive engine management systems. Eng Appl Artif Intell 13(2):147–157

    Article  Google Scholar 

  39. Canakci M, Erdil A, Arcaklioğlu E (2006) Performance and exhaust emissions of a biodiesel engine. Applied energy 83(6):594–605

    Article  Google Scholar 

  40. Çelik V, Arcaklioğlu E (2005) Performance maps of a diesel engine. Applied Energy 81(3):247–259

    Article  Google Scholar 

  41. Ghobadian B, Rahimi H, Nikbakht A, Najafi G, Yusaf T (2009) Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy 34(4):976–982

    Article  Google Scholar 

  42. Oğuz H, Sarıtas I, Baydan HE (2010) Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications 37(9):6579–6586

    Article  Google Scholar 

  43. Parlak A, Islamoglu Y, Yasar H, Egrisogut A (2006) Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Applied Thermal Engineering 26(8):824–828

    Article  Google Scholar 

  44. Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics 23(3):665–685

    Article  Google Scholar 

  45. Lee SH, Howlett RJ, Crua C, Walters SD (2007) Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration: A comparative study. Journal of Intelligent & Fuzzy Systems 18(1):43–56

    Google Scholar 

  46. Tutuncu K, Allahverdi N (2007) Reverse modeling of a diesel engine performance by FCM and ANFIS, Proceedings of the 2007 international conference on Computer systems and technologies. ACM, pp. 32

  47. Taghavifar H, Khalilarya S, Jafarmadar S (2015) Adaptive neuro-fuzzy system (ANFIS) based appraisal of accumulated heat from hydrogen-fueled engine. International Journal of Hydrogen Energy 40(25):8206–8218

    Article  Google Scholar 

  48. Al-Hmouz A, Shen J, Al-Hmouz R, Yan J (2012) Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Transactions on Learning Technologies 5(3):226–237

    Article  Google Scholar 

  49. Jang J-S, Sun C-T (1995) Neuro-fuzzy modeling and control. Proceedings of the IEEE 83(3):378–406

    Article  Google Scholar 

  50. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence

  51. Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions, Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis. sn, pp. 55-60

  52. Koivo H (2000) ANFIS (Adaptive Neuro-Fuzzy Inference System), European symposium on intelligent technology, Aachen, Germany

  53. Tanikic D, Manic M, Devedzic G, Cojbasic Z (2010) Modelling of the temperature in the chip-forming zone using artificial intelligence techniques. Neural Network World 20(2):171

    Google Scholar 

  54. Jovanovic BB, Reljin IS, Reljin BD (2004) Modified ANFIS architecture-improving efficiency of ANFIS technique, Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004 7th Seminar on. IEEE, pp. 215-220

  55. Werbos P (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences

  56. Cus F, Zuperl U, Milfelner M, Mursec B (2006) An adaptive neuro-fuzzy inference system for modeling of end-milling. Production Engineering Institute, Mechanical Engineering University of Maribor, Maribor, Slovenia

  57. Abdi H (2007) The Kendall rank correlation coefficient. Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks, pp 508–510

    Google Scholar 

  58. Arcaklioğlu E, Çelıkten İ (2005) A diesel engine's performance and exhaust emissions. Applied Energy 80(1):11–22

    Article  Google Scholar 

  59. Fisher RA (1921) On the probable error of a coefficient of correlation deduced from a small sample. Metron 1:3–32

    MathSciNet  Google Scholar 

  60. Gölcü M, Sekmen Y, Erduranlı P, Salman MS (2005) Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine. Applied Energy 81(2):187–197

    Article  Google Scholar 

  61. Gumbel EJ (1960) Bivariate exponential distributions. Journal of the American Statistical Association 55(292):698–707

    Article  MathSciNet  MATH  Google Scholar 

  62. Hashmi MZ, Shamseldin AY, Melville BW (2011) Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP). Environmental modelling & software 26(12):1639–1646

    Article  Google Scholar 

  63. Roy S, Banerjee R, Das AK, Bose PK (2014b) Development of an ANN based system identification tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine. Journal of Natural Gas Science and Engineering 21:147–158

    Article  Google Scholar 

  64. Roy S et al (2015a) Adaptive-neuro fuzzy inference system (ANFIS) based prediction of performance and emission parameters of a CRDI assisted diesel engine under CNG dual-fuel operation. Journal of Natural Gas Science and Engineering 27:274–283

    Article  Google Scholar 

  65. Roy S, Ghosh A, Das AK, Banerjee R (2014c) A comparative study of GEP and an ANN strategy to model engine performance and emission characteristics of a CRDI assisted single cylinder diesel engine under CNG dual-fuel operation. Journal of Natural Gas Science and Engineering 21:814–828

    Article  Google Scholar 

  66. Roy S, Ghosh A, Das AK, Banerjee R (2015b) 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. Applied Energy 140:52–64

    Article  Google Scholar 

  67. Borges CF (2015) On polynomial function approximation with minimum mean squared relative error and a problem of Tchebychef. Applied Mathematics and Computation 258(Supplement C):22–28

    Article  MathSciNet  MATH  Google Scholar 

  68. Dawson CW, Abrahart RJ, See LM (2007) HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling & Software 22(7):1034–1052

    Article  Google Scholar 

  69. Guo M, Ghosh M (2012) Mean squared error of James–Stein estimators for measurement error models. Statistics & Probability Letters 82(11):2033–2043

    Article  MathSciNet  MATH  Google Scholar 

  70. Taraji, M. et al., 2017. Error measures in quantitative structure-retention relationships studies. Journal of Chromatography A, 1524(Supplement C): 298-302.

  71. Krause P, Boyle D, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5:89–97

    Article  Google Scholar 

  72. Birkel C, Soulsby C, Tetzlaff D (2015) Conceptual modelling to assess how the interplay of hydrological connectivity, catchment storage and tracer dynamics controls nonstationary water age estimates. Hydrological Processes 29(13):2956–2969

    Article  Google Scholar 

  73. Gao H et al (2014) Climate controls how ecosystems size the root zone storage capacity at catchment scale. Geophysical Research Letters 41(22):7916–7923

    Article  Google Scholar 

  74. Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology 377(1):80–91

    Article  Google Scholar 

  75. Pechlivanidis I, Jackson B, McMillan H (2010) The use of entropy as a model diagnostic in rainfall-runoff modelling

  76. Banerjee R, Bose P (2012) Development of a Neuro Genetic Algorithm Based Virtual Sensing Platform for the Simultaneous Prediction of NOx, Opacity and BSFC in a Diesel Engine Operated in Dual Fuel Mode with Hydrogen under Varying EGR Conditions. SAE International Journal of Engines 5(2):119–140

    Article  Google Scholar 

  77. Basseur M, Zitzler E (2006) Handling uncertainty in indicator-based multiobjective optimization. International Journal of Computational Intelligence Research 2(3):255–272

    Article  MathSciNet  Google Scholar 

  78. Fieldsend JE, Everson RM (2005) Multi-objective optimisation in the presence of uncertainty, Evolutionary Computation, 2005. The 2005 I.E. Congress on. IEEE, pp. 243-250

  79. Santiago JFN, Petkovic SG, Teixeira RN, Noatsch U, Thiele-Krivoj B (2003) Comparison of Fixed Point Realisations between Inmetro and PTB. AIP Conference Proceedings 684(1):849–854

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, NIT Agartala, Jirania, Tripura, 799046, India

    Amitav Chakraborty & Rahul Banerjee

  2. Department of Mechanical Engineering, BML Munjal University, Gurgaon, 122413, India

    Sumit Roy

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  1. Amitav Chakraborty
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Correspondence to Sumit Roy.

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Chakraborty, A., Roy, S. & Banerjee, R. Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG. Heat Mass Transfer 54, 2725–2742 (2018). https://doi.org/10.1007/s00231-018-2312-8

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