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Neural Networks and Regression Modeling of Eco-friendly Melting Furnace Parameters using Bio-fuels

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 43 - Number 1
Year of Publication: 2012
Purshottam Kumar
Ranjit Singh

Purshottam Kumar and Ranjit Singh. Article: Neural Networks and Regression Modeling of Eco-friendly Melting Furnace Parameters using Bio-fuels. International Journal of Computer Applications 43(1):10-15, April 2012. Full text available. BibTeX

	author = {Purshottam Kumar and Ranjit Singh},
	title = {Article: Neural Networks and Regression Modeling of Eco-friendly Melting Furnace Parameters using Bio-fuels},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {43},
	number = {1},
	pages = {10-15},
	month = {April},
	note = {Full text available}


Rotary furnaces apart from being pollution efficient can maintain the quality standards set by the present methods of casting. The rising demand for high quality castings necessitates that vast amount of manufacturing knowledge be incorporated in manufacturing systems. Rotary furnace involves several critical parameters like flame temperature, preheat air temperature, revolutions per minute of the furnace, excess air percentage, melting time, fuel consumption and melting rate of the molten metal which should be controlled throughout the melting process. A complex relationship exists between these manufacturing parameters and hence there is a need to develop models which can capture this complex interrelationship and enable fast computation. In this paper the applicability and the relative effectiveness of the artificial neural networks as function approximators for rotary furnace have been investigated. The results obtained by these models are found to correlate well with the experimental data. Results obtained by the regression modeling are also found correlating well with the experimental data. This indicates that NN models and regression models can very well be used to model this complex relationship amongst various parameters in an eco-friendly melting furnace.


  • Singh R. , Patvardhan C. , Jain R. K. and A. Kumar, (2000). "Economic Justification of Coke-less Cupola for Pollution Free Casting in Indian Environment with Special Reference to Agra" Indian Foundry Journal, Vol. 46, No. 8, pp. 18-27.
  • Singh R. , Radha Krishna M. , Patvardhan C. and Rana G. P. , (2006). "Rotary Furnace: Effect of Rotational Speed on Rate of Melting, Fuel Consumption and Pollution", Indian foundry Journal, Vol. 52, No. 2, pp. 38-40.
  • Levi W. W. , (1947). "Variables affecting Carbon Control in Cupola Operation", Transactions of APS, Vol. 55, pp. 626-632.
  • Davis F. and M. Decrop, (1958). "Influence of Blast Input, Coke Size and Melting Coke Ratios on Cupola Performance", Foundry Trade Journal, pp. 319-325.
  • Pehle R. D. , (1963). "Thermo-chemical Model of Computer Prediction of Cupola Performance", AFS Transactions, Vol. 71, pp. 580-587.
  • Karunakar, D. B. and G. L. Datta, (2002). "Modeling of cupola furnace parameters using Artificial Neural Networks", Indian Foundry Journal, Vol. 48, pp. 29-39.
  • Andrew Gelman, Matt Stevens, and Valerie Chan, 2003 "Regression Modeling and Meta-Analysis for Decision Making: A Cost-Bene?t Analysis of Incentives in Telephone Surveys," 1, pp. 1-13.
  • Vasin Kiattikomo, Arun Chatterjee, Joseph E. Hummer, and Mary Sue Younger, 2008 "Planning Level Regression Models for Crash Prediction on Interchange and Non-Interchange Segments of Urban Freeways," Journal of Transportation Engineering, 134 (3), pp.
  • Singh R. , Patvardhan C. , Jain R. K. and A. Kumar, (2000). "Effect of Air - Preheating and Excess Air on the Performance of LDO Fired Rotary Furnace" Indian Foundry Journal, Vol. 46, No. 11, pp. 26-32.
  • Hans Raj K. , Sharma R. S. , Setia R. , Upadhyay V. and Alok K. Verma, (2006). "Modeling of Micro End Milling Operations with Artificial Neural Network Models", International Journal of Agile Manufacturing, Vol. 6, pp. 99-103.
  • Hans Raj K. , Sharma R. S. , Srivastava S. and C. Patvardhan, (2000). "Modeling of Manufacturing Processes with ANN for Intelligent Manufacturing", International Journal of Machine Tools & Manufacture, Vol. 40, pp. 851-868.
  • Fausett L. , (1994). "Fundamentals of Neural Networks", Prentice Hall, Eaglewood Cliffs, NJ.
  • More, J. J. , (1977). "The Levenberg - Maquardt Algorithm: Implementation and theory, Numerical Analysis", G. A. Watson (Ed. ), Lecture Notes in Mathematics, Springer Verlag, Vol. 630, pp. 105-116.