Estimation of gross calorific value of coals using artificial neural networks

Fuel

Volumn 86, Issue 3, 2007, Pages 334-344

  Patel, Shagufta U ^a   Jeevan Kumar, B ^a   Badhe, Yogesh P ^a   Sharma, B K ^b   Saha, Sujan ^b   Biswas, Subhasish ^b   Chaudhury, Asim ^b   Tambe, Sanjeev S ^a   Kulkarni, Bhaskar D ^a  

^a NATIONAL CHEMICAL LABORATORY   (India)

^b CSIR CENTRAL INSTITUTE OF MINING AND FUEL RESEARCH   (India)

Author keywords

Artificial neural network; Gross calorific value (GCV); Proximate and ultimate analyses

Indexed keywords

CORRELATION METHODS; LINEAR PROGRAMMING; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

ARTIFICIAL NEURAL NETWORK MODELS; GROSS CALORIFIC VALUE (GCV); NONLINEAR MODELS; PROXIMATE AND ULTIMATE ANALYSES;

COAL;

EID: 33750515303     PISSN: 00162361     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fuel.2006.07.036     Document Type: Article

References (25)

1. Parikh J., Channiwala S.A., and Ghosal G.K. A correlation for calculating HHV from proximate analysis of solid fuels. Fuel 84 (2005) 487-494
2. Goutal M. C.R. Acad Sci. Paris 135 (1902) 477-479
3. Schuster V.F. Über die Berechnung des Heizwertes von Kohlen aus der Immediatzusammensetzung. Brennstoff - Chemie 32 (1951) 19-20
4. Spooner C.E. Swelling power of coal. Fuel 30 (1951) 193-202
5. Mazumdar B.K. Coal systematics: deductions from proximate analysis of coal Part I. J Sci Ind Res 13B 12 (1954) 857-863
6. Mazumder B.K. Theoretical oxygen requirement for coal combustion: relationship with its calorific value. Fuel 79 (2000) 1413-1419
7. Choudhary A, Biswas S. CFRI internal report titled "Development of equivalency chart between UHV and GCV", Report No. TR/CFRI/3.02/2002-03.
8. Channiwala S.A., and Parikh P.P. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel 81 (2002) 1051-1063
9. Kucukbayrak S., Durus B., Mericboyu A.E., and Kadioglu E. Estimation of calorific values of Turkish lignites. Fuel 70 (1991) 979-981
10. Cordero T., Marquez F., Rodriquez-Mirasol J., and Rodriguez J.J. Predicting heating values of lignocellulosic and carbonaceous materials from proximate analysis. Fuel 80 (2001) 1567-1571
11. Fernandez P., Diaz R.M., and Xiberta J. Correlations of properties of Spanish coals with their natural radionuclides contents. Fuel 76 10 (1997) 951-955
12. Demirbas A. Calculation of higher heating values of biomass fuels. Fuel 76 5 (1997) 431-434
13. Jimenez L., and Gonzalez F. Study of the physical and chemical properties of lignocellulosic residues with a view to the production of fuels. Fuel 70 (1991) 947-950
14. Raveendran K., and Ganesh A. Heating value of biomass and biomass pyrolysis products. Fuel 75 15 (1996) 1715-1720
15. Marquardt D.W. An algorithm for least squares estimation of nonlinear parameters. J Soc Ind Appl Math 11 (1963) 431-441
16. Tambe SS, Kulkarni BD Deshpande PB. Elements of artificial neural networks with selected applications in chemical engineering, and chemical & biological sciences, simulation & advanced controls, Louisville, KY, 1996.
17. Rumelhart D., Hinton G., and Williams R. Learning representations by backpropagating error. Nature 323 (1986) 533-536
18. Freeman J.A., and Skapura D.M. Neural networks: algorithms, applications and programming techniques (1991), Addision-Wesley, Reading, MA
19. Bishop C.M. Neural networks and their applications. Rev Sci Instrum 65 (1994) 1803
20. Nandi S., Ghosh S., Tambe S., and Kulkarni B. Artificial Neural-Network-Assist Stochast Process Optim Strat AICHE 47 1 (2001) 126-141
21. Sung A. Ranking importance of input parameters of neural networks. Expert Syst Appl 15 (1998) 405-411
22. Lin Y., and Cunningham G. Building a fuzzy system from input-output data. J Intell Fuzzy Syst 2 (1994) 243-250
23. Lin Y., and Cunningham G. A new approach to fuzzy-neural system modeling. IEEE Trans Fuzzy Syst 3 (1995) 190-198
24. Engelbrecht A.P., Cloete I., and Zurada J.M. Determining the significance of input parameters using sensitivity analysis, from natural to artificial neural computation: proceedings of international workshop on artificial neural networks. Malaga-Torremolinos (1995), Springer, Spain pp. 382-388
25. Zurada J.M., Malinowski A., and Cloete I. Sensitivity analysis for minimization of input data dimension for feed forward neural network. Proceedings of IEEE international symposium on circuits and systems (1994), IEEE Press, London pp. 447-450