Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network

The present study explores the potential of artificial neural network to predict the performance and exhaust emissions of an existing single cylinder four-stroke CRDI engine under varying EGR strategies. Based on the experimental data an ANN model is developed to predict BSFC, BTE, CO2, NOx and PM with load, fuel injection pressure, EGR and fuel injected per cycle as input parameters for the network. The study was carried out with 70% of total experimental data selected for training the neural network, 15% for the network’s cross-validation and remaining 15% data has been used for testing the performance of the trained network. The developed ANN model was capable of predicting the performance and emissions of the experimental engine with excellent agreement as observed from correlation coefficients within the range of 0.987–0.999, mean absolute percentage error in the range of 1.1–4.57% with noticeably low root mean square errors. In addition to common correlation coefficients, the present study incorporated special statistical error and performance metrics such as mean square relative error, forecasting uncertainty Theil U2, Nash–Sutcliffe Coefficient of Efficiency and Kling–Gupta Efficiency. Low values of MSRE and Theil U2 combined with commendable indices of NSE and KGE proved beyond doubt the robustness and applicability of the model so developed. Furthermore, the developed ANN model was capable of mapping the PM–NOx–BSFC trade-off potential of the CRDI operation under EGR for all cases of actual observations with significant accuracy.

[1]  Paul J. Shayler,et al.  The exploitation of neural networks in automotive engine management systems , 2000 .

[2]  I. Dimopoulos,et al.  Artificial neural networks as a classification method in the behavioural sciences , 1997, Behavioural Processes.

[3]  Terukazu Nishimura,et al.  Dual Mode Combustion Concept With Premixed Diesel Combustion by Direct Injection Near Top Dead Center , 2003 .

[4]  Dennis L. Siebers,et al.  Non-Sooting, Low Flame Temperature Mixing-Controlled DI Diesel Combustion , 2003 .

[5]  Robert J. Abrahart,et al.  HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts , 2007, Environ. Model. Softw..

[6]  Johan Lennblad,et al.  Neural Network Based Fast-Running Engine Models for Control-Oriented Applications , 2005 .

[7]  Gholamhassan Najafi,et al.  Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network , 2009 .

[8]  Soteris A. Kalogirou,et al.  Artificial intelligence for the modeling and control of combustion processes: a review , 2003 .

[9]  Matteo De Cesare,et al.  Neural Network Based Models for Virtual NO x Sensing of Compression Ignition Engines , 2011 .

[10]  Mustafa Canakci,et al.  Performance and exhaust emissions of a gasoline engine using artificial neural network , 2007 .

[11]  Akihiko Minato,et al.  Investigation of premixed lean diesel combustion with ultra high pressure injection , 2005 .

[12]  T. Balusamy,et al.  Effect of Injection Time and Injection Pressure on CI Engine Fuelled with Methyl Ester of Thevetia Peruviana Seed Oil , 2010 .

[13]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[14]  O. Cortés,et al.  Optimization of operating conditions for compressor performance by means of neural network inverse , 2009 .

[15]  Michael R. Grimes,et al.  A Neural Network Based Methodology for Virtual Sensor Development , 2005 .

[16]  Hyun Kyu Suh,et al.  Investigations of multiple injection strategies for the improvement of combustion and exhaust emissions characteristics in a low compression ratio (CR) engine , 2011 .

[17]  T. Johnson,et al.  Diesel Emissions in Review , 2011 .

[18]  Adem Çiçek,et al.  Prediction of engine performance for an alternative fuel using artificial neural network , 2012 .

[19]  Sedat Bayseç,et al.  Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks , 2010 .

[20]  T. Johnson.  Review of Diesel Emissions and Control , 2010 .

[21]  B. F. Yousif,et al.  CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network , 2010 .

[22]  Ismail Saritas,et al.  Prediction of diesel engine performance using biofuels with artificial neural network , 2010, Expert Syst. Appl..

[23]  Mustafa Canakci,et al.  Performance and exhaust emissions of a biodiesel engine , 2006 .

[24]  Rolf Isermann,et al.  Modeling and Real-Time Simulation of Diesel Engines for Control Design , 1998 .

[25]  Philip Gerald Blakeman,et al.  On The Road to 2010 Emissions: Field Test Results and Analysis with DPF-SCR System and Ultra Low Sulfur Diesel Fuel , 2005 .

[26]  Rolf D. Reitz,et al.  Controlling D.I. Diesel engine emissions using multiple injections and EGR , 1998 .

[27]  Sébastien Candel,et al.  Combustion control and sensors: a review , 2002 .

[28]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[29]  J. Desantes,et al.  The modification of the fuel injection rate in heavy-duty diesel engines: Part 2: Effects on combustion , 2004 .

[30]  D N Assanis,et al.  Development of a neural network model of an advanced, turbocharged diesel engine for use in vehicle-level optimization studies , 2004 .

[31]  R. P. Sharma,et al.  Use of HOT EGR for NOx control in a compression ignition engine fuelled with bio-diesel from Jatropha oil , 2007 .

[32]  Magín Lapuerta,et al.  Modeling diesel particulate emissions with neural networks , 2001 .

[33]  Prakash Gajendran,et al.  A predictive tool for emissions from heavy-duty diesel vehicles. , 2003, Environmental science & technology.

[34]  Dimitrios C. Rakopoulos,et al.  Investigating the emissions during acceleration of a turbocharged diesel engine operating with bio-d , 2010 .

[35]  Erol Arcaklioğlu,et al.  A diesel engine's performance and exhaust emissions , 2005 .

[36]  Yusuf Çay,et al.  Prediction of a gasoline engine performance with artificial neural network , 2013 .

[37]  R. Hentschel,et al.  In-Car Modelling of Emissions with Dynamic Artificial Neural Networks , 2001 .

[38]  Jeffrey M. Pfeiffer,et al.  Replacing Volumetric Efficiency Calibration Look-up Tables with Artificial Neural Network-based Algorithm for Variable Valve Actuation , 2010 .

[39]  Ahmed Rachid,et al.  Diesel Engine Exhaust Emissions Modelling Using Artificial Neural Networks , 1999 .

[40]  Yasushi Tanaka,et al.  Technologies of DENSO Common Rail for Diesel Engine and Consumer Values , 2004 .

[41]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[42]  C. B. Tilanus,et al.  Applied Economic Forecasting , 1966 .

[43]  Yakup Sekmen,et al.  Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine , 2005 .

[44]  Adnan Sözen,et al.  Solar potential in Turkey , 2005 .

[45]  G. Nagarajan,et al.  Influence of injection timing on performance, emission and combustion characteristics of a DI diesel engine running on waste plastic oil , 2009 .

[46]  Stephan M. Winkler,et al.  Virtual Sensor Design of Particulate and Nitric Oxide Emissions in a DI Diesel Engine , 2005 .

[47]  G. R. Kannan,et al.  Experimental investigation on diesel engine with diestrolwater micro emulsions , 2011 .

[48]  A. Maiboom,et al.  Experimental study of various effects of exhaust gas recirculation (EGR) on combustion and emissions of an automotive direct injection diesel engine , 2008 .

[49]  Ulrich Lenz,et al.  Artificial Intelligence for Combustion Engine Control , 1996 .

[50]  Marco Badami,et al.  Influence of Injection Pressure on the Performance of a DI Diesel Engine with a Common Rail Fuel Injection System , 1999 .

[51]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[52]  Gholamhassan Najafi,et al.  Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network , 2009 .

[53]  Adnan Parlak,et al.  Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine , 2006 .

[54]  G. C. Mavropoulos,et al.  Effect of exhaust gas recirculation (EGR) temperature for various EGR rates on heavy duty DI diesel engine performance and emissions , 2008 .

[55]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[56]  Hoon Kiat Ng,et al.  Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends , 2012 .

[57]  F. Bliemel.  Theil's Forecast Accuracy Coefficient: A Clarification , 1973 .

[58]  Jesús Benajes,et al.  Combustion and Exhaust Emissions in a Heavy-Duty Diesel Engine with Increased Premixed Combustion Phase by Means of Injection Retarding , 2006 .

[59]  N Ladommatos,et al.  Control of oxides of nitrogen from diesel engines using diluents while minimising the impact on particulate pollutants , 1998 .

[60]  Ugur Kesgin,et al.  Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission , 2004 .

[61]  R. Reitz,et al.  Effects of Injection Pressure and Nozzle Geometry on D.I. Diesel Emissions and Performance , 1995 .

[62]  N. V. Mahalakshmi,et al.  Performance, emission and combustion characteristics of poon oil and its diesel blends in a DI diesel engine , 2009 .

[63]  Erol Arcaklioğlu,et al.  Performance maps of a diesel engine , 2005 .

[64]  Hua Zhao.  Advanced Direct Injection Combustion Engine Technologies and Development , 2010 .

[65]  Christopher M. Atkinson,et al.  Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines , 2005 .

[66]  P. Srinivasa Pai,et al.  Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings , 2011 .

[67]  T. Johnson.  Diesel Emission Control in Review , 2001 .