Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner

This study deals with ANN (artificial neural network) modeling of a swirl burner. The model was used to predict the flame temperature and pollutant emissions (CO (carbon monoxide) and NOx (nitrogen oxide)) from combustion of LPG (liquefied petroleum gas) in the swirl burner. The data for the training and testing of the proposed ANN was obtained by combusting LPG at various equivalent ratios (LPG/air ratios) and swirler's vane angles in a low swirl burner. Vane angles of 35–60° in steps of 5° and equivalent ratios of 0.94, 0.90, 0.85, 0.80, 0.75, 0.71, 0.66 and 0.61 were considered. An ANN model based on standard back-propagation algorithms for the swirl burner was developed using some of the experimental data for training and validation. The performance of the ANN was tested by comparing the predicted outputs with the experimental values that were not used in training the network. R values of 0.94 were obtained for CO and NOx and 0.99 for flame temperature. These results show that very strong correlation exists between the ANN predicted values and the experimental results. Therefore, this study demonstrates that the performance and emissions of swirl burner can be accurately predicted using ANN approach.

[1]  Pedro Pérez-Higueras,et al.  Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks , 2011 .

[2]  Charles Baukal,et al.  Industrial Burners Handbook , 2003 .

[3]  Miroslaw L. Wyszynski,et al.  Characteristics of LPG-diesel dual fuelled engine operated with rapeseed methyl ester and gas-to-liquid diesel fuels , 2012 .

[4]  Derek Dunn-Rankin,et al.  Lean Combustion Technology and Control , 2011 .

[5]  Mohamed Pourkashanian,et al.  The reduction of NOx formation in natural gas burner flames , 1993 .

[6]  Engin Gedik,et al.  Investigation on thermal performance calculation of two type solar air collectors using artificial neural network , 2011, Expert Syst. Appl..

[7]  P. Koutmos,et al.  Fluid dynamics modeling of a stratified disk burner in swirl co-flow , 2012 .

[8]  Carl G. Looney,et al.  Pattern recognition using neural networks: theory and algorithms for engineers and scientists , 1997 .

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

[10]  Gholamhassan Najafi,et al.  Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends , 2010 .

[11]  Bangzhu Zhu,et al.  Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China , 2013 .

[12]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[13]  V. Yang,et al.  Dynamics and stability of lean-premixed swirl-stabilized combustion , 2009 .

[14]  Tugce Bekat,et al.  PREDICTION OF THE BOTTOM ASH FORMED IN A COAL-FIRED POWER PLANT USING ARTIFICIAL NEURAL NETWORKS , 2012 .

[15]  U.S.P. Shet,et al.  Effect of swirl on lean flame limits of pilot-stabilized open premixed turbulent flames , 2007 .

[16]  Mukesh Khare,et al.  A Review of Deterministic, Stochastic and Hybrid Vehicular Exhaust Emission Models , 2004 .

[17]  G. S. Samuelsen,et al.  Scaling and development of low-swirl burners for low-emission furnaces and boilers , 2000 .

[18]  Martin T. Hagan,et al.  Neural network design , 1995 .

[19]  Mustafa Inalli,et al.  Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system , 2008 .

[20]  Mahmoud Omid,et al.  Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production , 2012 .

[21]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[22]  Eduardo F. Fernández,et al.  Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network , 2013 .

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

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

[25]  Mustafa Inalli,et al.  Modelling of a vertical ground coupled heat pump system by using artificial neural networks , 2009, Expert Syst. Appl..

[26]  M. Abdul Mujeebu,et al.  Applications of porous media combustion technology - A review , 2009 .