Abstract
Heat value is the key factor whether municipal solid waste (MSW) can be treated by incineration. In this paper, two back-propagation neural network models, based on the measured data of physical compositions and elemental compositions respectively, are founded to predict the heat value of Chongqing MSW. The prediction results show that the model based on the elemental compositions offers a more precise prediction than the model based on the physical compositions, and the maximum relative error is below 10%. Thus the artificial neural network method can be used to predict the heat value of Chongqing MSW quickly, and it is very important to select incinerators, design the combustion chamber and the afterburner, and operate incinerators properly in Chongqing in future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kathirvale, S., Yunus, M.N.M., Sopian, K., Samsuddin, A.H.: Energy potential from municipal solid waste in Malaysia. Renewable Energy 29, 559–567 (2003)
Colin, P., Tim, R.: Energy from waste: an evaluation of conversion technologies. Elsevier Applied Science, New York (1985)
Cooper, C.D., Kim, B., Mac Donald, J.: Estimating the lower heating values of hazardous and solid wastes. Journal of the Air & Waste Management Association 49, 471–476 (1999)
Chang, M.B., Huang, C.K.: Characteristics of energy flow in a municipal solid waste incinerator. Journal of Environmental Engineering ASCE 127(1), 78–81 (2001)
Chang, N.B., Chang, Y.H., Chen, W.C.: Evaluation of heat value and its prediction for refuse-derived fuel. The Science of the Total Environment 197, 139–148 (1997)
Li, X.L., Hu, M.L.: Effective approach to improving heat value of domestic waste: primary probe into heat value of municipal domestic waste in Xiamen city. Environmental Sanitation Engineering 15(5), 46–48 (2007)
Sun, P.F., Li, X.D., Chi, Y., Yan, J.H.: The study on prediction of lower heat value of MSW. Energy Engineering 5, 39–42 (2006)
Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems 39, 43–62 (1997)
Hecht-Nielsen, R.: Theory of the back propagation neural network. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 121–125 (1989)
Math Works Inc., Neural network toolbox user’s guide. Math Works, Natick (2001)
Thipse, S.S., Sheng, C., Booty, M.R., Magee, R.S., Bozzelli, J.W.: Chemical makeup and physical characterization of a synthetic fuel and methods of heat content evaluation for studies on MSW incineration. Fuel 81, 211–217 (2002)
Shu, H.Y., Lu, H.C., Fan, H.J., Chang, M.C., Chen, J.C.: Prediction for energy content of Taiwan municipal solid waste using multilayer perceptron neural networks. Journal of the Air & Waste Management Association 56, 852–858 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, Sh., Chen, Xl., Zhu, Xc., Ding, Yq., Wang, K. (2009). Prediction of Heat Value of Chongqing Municipal Solid Waste Using Artificial Neural Networks. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_161
Download citation
DOI: https://doi.org/10.1007/978-3-642-03664-4_161
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03663-7
Online ISBN: 978-3-642-03664-4
eBook Packages: EngineeringEngineering (R0)