Abstract
Fuel oil viscosity is an important parameter in the control of combustion in power plants. If the viscosity is optimal at the entrance of the boiler, then the combustion is optimal causing a minimum of contamination and a maximum of efficiency. Hardware viscosimeters are expensive and difficult to operate. Laboratory analyses calculate the viscosity based on chemical analysis but not in real time. This paper describes the development of a virtual sensor that utilizes artificial intelligence (AI) techniques for the construction of models. The models are used to estimate the viscosity based on related measurements concerning the combustion in a power plant. A probabilistic model is constructed using automatic learning algorithms and an analytical model is defined using physical principles and chemical analysis. Sensor fusion is applied to estimate the on-line value of the fuel viscosity. The virtual sensor is being installed in the Tuxpan power plant in Veracruz, Mexico.
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.
References
Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine Learning 9(4), 309–348 (1992)
Comision Federal de Electricidad, Generación termoeléctrica (2008), http://www.cfe.gob.mx/es/LaEmpresa/generacionelectricidad/termoelectrica
Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G.: Soft Sensors for Monitoring and Control of Industrial Processes. In: Advances in Industrial Control. Springer, Glasgow (2007)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11(1) (2009)
Ibargüengoytia, P.H., Reyes, A., Huerta, M., Hermosillo, J.: Probabilistic virtual sensor for on–line viscosity estimation. In: Gelbukh, A., Morales, E. (eds.) Seventh Mexican International Conference on Artificial Intelligence, Piscataway, N.J., U.S.A., pp. 377–382. IEEE Computer Society, Los Alamitos (2000)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco (1988)
Thomas, P.: Simulation of Industrial Processes for Control Enginners. Butterworth Heinemann, Butterworths (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ibargüengoytia, P.H., Delgadillo, M.A. (2010). On-Line Viscosity Virtual Sensor for Optimizing the Combustion in Power Plants. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-16952-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16951-9
Online ISBN: 978-3-642-16952-6
eBook Packages: Computer ScienceComputer Science (R0)