Abstract
To diagnose the status of aluminum production cell, we set up a diagnosis system. This system was based on BP neural network with 10 inputs and 3 outputs. It used frequency energy of cell resistance as characteristic vectors. Also genetic algorithm was used to optimize the initial weights and threshold value of BP network. After designed and tested this software system, three kinds of status were successfully diagnosed, which are anode lesion, liquid aluminum fluctuation and normal condition. As a result, by sampling data on site, the status diagnosis accuracy is larger than 83%.
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
Liu, Y., Li, J.: Modern Aluminum Production. The Metallurgical Industry Press, Beijing (2008)
Zeng, S., Li, J.: Model Predictive Control of Superheat for Prebake Aluminum Production Cells. Light Metals (2008), TMS Annual Meeting (March 2008)
Zeng, S., Li, J.: The Fuzzy Predictive Control of the Aluminum Electrolytic Cell Electrolyte Molecular Ratio. In: The seventh Global Intelligence Control and Automation Conference, pp. 1229–1233 (June 2008)
Banta, L., Dai, C., Biedler, P.: Noise Classification in the Aluminum Reduction Process, Light Metals, pp. 431–435 (2003)
Li, H., Zhi, M., Tang, Q., et al.: The Aluminum Production Cell Status Diagnosis on the Fuzzy Neural Network. Journal of System Stimulation 18(2), 482–484 (2006)
Ding, L.: 350kA Aluminum Production Cell Fault Diagnosis System Research (Master degree thesis). North China University of Technology, Beijing (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, S., Li, J., Cui, L. (2011). Cell Status Diagnosis for the Aluminum Production on BP Neural Network with Genetic Algorithm. In: Lin, S., Huang, X. (eds) Advanced Research on Computer Education, Simulation and Modeling. CESM 2011. Communications in Computer and Information Science, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21783-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-21783-8_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21782-1
Online ISBN: 978-3-642-21783-8
eBook Packages: Computer ScienceComputer Science (R0)