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

Due to the increasing demand for fossil fuels and environmental threat due to pollution a number renewable sources of energy have been studied worldwide. In the present investigation influence of injection timing on the performance and emissions of a single cylinder, four stroke stationary, variable compression ratio, diesel engine was studied using waste cooking oil (WCO) as the biodiesel blended with diesel. The tests were performed at three different injection timings (24°, 27°, 30° CA BTDC) by changing the thickness of the advance shim. The experimental results showed that brake thermal efficiency for the advanced as well as the retarded injection timing was lesser than that for the normal injection timing (27° BTDC) for all sets of compression ratios. Smoke, un-burnt hydrocarbon (UBHC) emissions were reduced for advanced injection timings where as NOx emissions increased. Artificial Neural Networks (ANN) was used to predict the engine performance and emission characteristics of the engine. Separate models were developed for performance parameters as well as emission characteristics. To train the network, compression ratio, injection timing, blend percentage, percentage load, were used as the input parameters where as engine performance parameters like brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature (Texh) were used as the output parameters for the performance model and engine exhaust emissions such as NOx, smoke and (UBHC) values were used as the output parameters for the emission model. ANN results showed that there is a good correlation between the ANN predicted values and the experimental values for various engine performance parameters and exhaust emission characteristics and the relative mean error values (MRE) were within 8%, which is acceptable.

[1]  Deng Yuanwang,et al.  An analysis for effect of cetane number on exhaust emissions from engine with the neural network , 2002 .

[2]  N. S. Rathore,et al.  Experimental investigation of the effect of compression ratio and injection pressure in a direct injection diesel engine running on Jatropha methyl ester , 2010 .

[3]  Vojislav Kecman,et al.  Neural networks—a new approach to model vapour‐compression heat pumps , 2001 .

[4]  G. Devaradjane,et al.  Vegetable Oils And Their Derivatives As Fuels For CI Engines: An Overview , 2003 .

[5]  K. Reddy,et al.  Experimental Investigation of Pongamia, Jatropha and Neem Methyl Esters as Biodiesel on C.I. Engine , 2008 .

[6]  Havva Balat,et al.  Progress in biodiesel processing , 2010 .

[7]  A. Tomašević,et al.  Methanolysis of used frying oil , 2003 .

[8]  Abul Kalam Hossain,et al.  Plant oils as fuels for compression ignition engines: a technical review and life-cycle analysis , 2010 .

[9]  A. I. Ogbonna,et al.  Effect of advanced injection timing on the performance of rapeseed oil in diesel engines , 2000 .

[10]  O. M. I. Nwafor,et al.  Effect of advanced injection timing on emission characteristics of diesel engine running on natural gas , 2007 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  Anthony Paul Roskilly,et al.  Experimental investigation on the performance and emissions of a diesel engine fuelled with ethanol–diesel blends , 2009 .

[13]  M. M Prieto,et al.  Power plant condenser performance forecasting using a non-fully connected artificial neural network , 2001 .

[14]  Hua Chen,et al.  Performance and combustion characteristics of biodiesel-diesel-methanol blend fuelled engine , 2010 .

[15]  Zafer Utlu,et al.  The effect of biodiesel fuel obtained from waste frying oil on direct injection diesel engine performance and exhaust emissions , 2008 .

[16]  M. Canakci,et al.  EVALUATING WASTE COOKING OILS AS ALTERNATIVE DIESEL FUEL , 2005 .

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

[18]  Vicente Hernández,et al.  Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Yaodong Wang,et al.  The performance and the gaseous emissions of two small marine craft diesel engines fuelled with biodiesel , 2008 .

[20]  Richard J. Atkinson,et al.  Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure , 1999 .

[21]  Hüseyin Aydın,et al.  Performance and emission evaluation of a CI engine fueled with preheated raw rapeseed oil (RRO)–diesel blends , 2010 .

[22]  A. Agarwal,et al.  Experimental investigations of performance and emissions of Karanja oil and its blends in a single cylinder agricultural diesel engine , 2009 .

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

[24]  Marc A. Dubé,et al.  Acid-catalyzed production of biodiesel from waste frying oil , 2006 .

[25]  Jose J. Lopez,et al.  Application of Neural Networks for Prediction and Optimization of Exhaust Emissions in a H.D. Diesel Engine , 2002 .

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

[27]  Mustafa Canakci,et al.  Influence of injection timing on the exhaust emissions of a dual-fuel CI engine , 2008 .

[28]  Magali R. G. Meireles,et al.  A comprehensive review for industrial applicability of artificial neural networks , 2003, IEEE Trans. Ind. Electron..

[29]  C. Muraleedharan,et al.  Use of vegetable oils as I.C. engine fuels—A review , 2004 .

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

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