PREDICTION OF THE BOTTOM ASH FORMED IN A COAL-FIRED POWER PLANT USING ARTIFICIAL NEURAL NETWORKS

The amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.

[1]  A. Murat Ozbayoglu,et al.  Estimation of Hardgrove grindability index of Turkish coals by neural networks , 2008 .

[2]  Zhou Hao,et al.  Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion , 2001 .

[3]  Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames , 2003 .

[4]  S. Chehreh Chelgani,et al.  Estimation of gross calorific value based on coal analysis using regression and artificial neural networks , 2009 .

[5]  Hao Zhou,et al.  Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks , 2004 .

[6]  E. Tuncali.  Chemical and technological properties of Turkish tertiary coals , 2002 .

[7]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[8]  D. Durgun,et al.  Effects of coal properties on the production rate of combustion solid residue , 2009 .

[9]  L. Reijnders.  Disposal, uses and treatments of combustion ashes: a review , 2005 .

[10]  Zhou Hao,et al.  Optimizing pulverized coal combustion performance based on ANN and GA , 2004 .

[11]  Inmaculada Arauzo,et al.  Monitoring and prediction of fouling in coal-fired utility boilers using neural networks , 2005 .

[12]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[13]  Mika Liukkonen,et al.  Artificial neural networks for analysis of process states in fluidized bed combustion , 2011 .

[14]  X. Querol,et al.  Contents of major and trace elements in feed coals from Turkish coal-fired power plants , 2000 .

[15]  Thomas Palmé,et al.  Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant , 2010 .

[16]  A. Z. S. Chong,et al.  Prediction of gaseous emissions from a chain grate stoker boiler using neural networks of ARX structure , 2001 .

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

[18]  M. Omid,et al.  Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran , 2014 .

[19]  S. Toprak.  Petrographic properties of major coal seams in Turkey and their formation , 2009 .

[20]  T. Coburn,et al.  Characterization of Turkish coals: a nationwide perspective , 2004 .

[21]  S. Chehreh Chelgani,et al.  Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network , 2010 .

[22]  Mohsen Assadi,et al.  Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden , 2007 .

[23]  W. T. Illingworth,et al.  Practical guide to neural nets , 1991 .

[24]  Alan Williams,et al.  The predictions of coal/char combustion rate using an artificial neural network approach , 1999 .

[25]  B. Z. Adewole,et al.  Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner , 2013 .

[26]  F. Inal,et al.  Artificial neural network predictions of polycyclic aromatic hydrocarbon formation in premixed n-heptane flames , 2006 .

[27]  Simon Haykin,et al.  Neural Networks: A Guided Tour , 2012, SOCO 2012.

[28]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.