Prediction of gaseous emissions from industrial stacks using an artificial intelligence method
-
C. Anghel
and
A. Ozunu
Abstract
A novel technique based on artificial intelligence methods able to predict pollutant emission concentrations from industrial stacks is presented. This procedure combines regression and classification problems into a unified technique, named minimax decision procedure. The core of this procedure is based on the minimax probability machine regression model. Using experimental databases, the trend of pollutant emissions and the level of pollution for one industrial thermal power station stack were presented. Based on this unified technique, numerical experiments provided the estimates of concentrations of CO, NOx, NO, and SO2 confirming the predictive power of this procedure.
[1] Benvenuto, F. and Marani, A., Global Nest Int. J. 23, 281 (2000). Search in Google Scholar
[2] Maqsood, I., Riaz Khan, M., Huang, G. H., and Abdalla, R., Eng. Appl. Artif. In. 18, 115 (2005). http://dx.doi.org/10.1016/j.engappai.2004.08.01910.1016/j.engappai.2004.08.019Search in Google Scholar
[3] Olcese, L. E. and Toselli, B. M., Chemosphere 57, 691 (2004). http://dx.doi.org/10.1016/j.chemosphere.2004.07.04510.1016/j.chemosphere.2004.07.045Search in Google Scholar PubMed
[4] Lu, W. Z. and Wang, W. J., Chemosphere 59, 693 (2005). http://dx.doi.org/10.1016/j.chemosphere.2004.10.03210.1016/j.chemosphere.2004.10.032Search in Google Scholar PubMed
[5] Anghel, C. I. and Ozunu, A., Chem. Pap. 59, 469 (2005). Search in Google Scholar
[6] Vapnik, V. N., The Nature of Statistical Learning Theory. Springer, New York, 2000. 10.1007/978-1-4757-3264-1Search in Google Scholar
[7] Hastie, T., Tibshirani, R., and Friedman, J. H., The Elements of Statistical Learning. Springer, New York, 2001. 10.1007/978-0-387-21606-5Search in Google Scholar
[8] Jain, A. K., Duin, R. P. W., and Mao, J., IEEE T Pattern. Anal. 22, 4 (2000). http://dx.doi.org/10.1109/34.82481910.1109/34.824819Search in Google Scholar
[9] Smola, A. J., Bartlett, P. L., Scholkopf, B., and Schuurmans, D., Advances in Large Margin Classifiers. MIT Press, Cambridge, 2000. 10.7551/mitpress/1113.001.0001Search in Google Scholar
[10] Vapnik, V. N., Statistical Learning Theory. Wiley, New York, 1998. Search in Google Scholar
[11] Lanckriet, G. R. G., Ghaoui, E. L., Bhattacharyya, C., and Jordan, M. I., Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, 2002. Search in Google Scholar
[12] Lanckriet, G. R. G., Ghaoui, E. L., Bhattacharyya, C., and Jordan, M. I., J. Machine Learning Res. 3, 555 (2002). http://dx.doi.org/10.1162/15324430332189772610.1162/153244303321897726Search in Google Scholar
[13] Strohmann, T. R. and Grudic, G. Z., Advances in Neural Information Processing Systems 15. MIT Press, Cambridge, 2003. Search in Google Scholar
[14] Grudic, G. Z., Probabilistic Regression Using Basis Function Models, Department of Computer Science, Report CU-CS-975-04, University of Colorado, 2004, http://www.cs.colorado.edu/»grudic/software. Search in Google Scholar
[15] Bordes, A., Ertekin, S., Weston, J., and Bottou, L., J. Machine Learning Res. 6, 1579 (2005). Search in Google Scholar
© 2006 Institute of Chemistry, Slovak Academy of Sciences
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Articles in the same Issue
- Coupled membrane process applied to fruit juice concentration
- Residence time distribution study for the catalytic packing MULTIPAK®
- Prediction of gaseous emissions from industrial stacks using an artificial intelligence method
- Production of process water using integrated membrane processes
- Kinetics of pyrolysis and properties of carbon black from a scrap tire
- Extraction of Re(VII) by neutral and basic extractants
- Multiple steady states in a CSTR with total condenser: Comparison of equilibrium and nonequilibrium models
- Influence of biomass on hydrodynamics of an internal loop airlift reactor
- Modelling of enzymatic reaction in an internal loop airlift reactor
- Safety analysis and risk identification for a tubular reactor using the HAZOP methodology
- Soil adsorption defluoridation of drinking water for an Ethiopian rural community
- Isolation and identification of anthraquinones of Caloplaca cerina and Cassia tora
- Selection of carrier for immobilization of fructosyltransferase from Aureobasidium pullulans
