Abstract
The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensor model of the key technology indicators (grinding granularity and mill discharge rate) based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA), the optimized set-point model utilizing case-based reasoning and the self-tuning PID decoupling controller. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process.
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
Wu, X.G., Yuan, M.Z., Yu, H.B.: Product Flow Rate Control in Ball Mill Grinding Process Using Fuzzy Logic Controller. In: 8th IEEE International Conference on Machine Learning and Cybernetics, pp. 761–764. IEEE Press, New York (2009)
Yu, J.Q., Xi, A.M., Fu, J.H.: The Application of Fuzzy Adaptive Learning Control (FALCON) in Milling-classification Operation System. Journal of Xi’an University of Architecture & Technology 2, 175–178 (2000) (in Chinese)
Zhou, P., Yue, H., Zheng, X.P.: Multivariable Fuzzy Supervisory Control for Mineral Grinding Process. Control and Decision 6, 685–688 (2008) (in Chinese)
Tie, M., Yue, H., Chai, T.Y.: Hybrid Intelligent Modeling and Simulation for Ore Grinding and Classification Process. Journal of Northeastern University (Natural Science) 5, 609–612 (2007) (in Chinese)
Zhang, X.D., Wang, W., Wang, X.G.: Beneficiation Process of Neural Networks Granularity of Soft Measurement Method. Control Theory and Application 1, 85–88 (2002) (in Chinese)
Ding, J.L., Yue, H., Qi, Y.T.: NN Soft-sensor for Particle Size of Grinding Circuit Based GA. Chinese Journal of Scientific Instrument 9, 981–984 (2006) (in Chinese)
He, G.C., Mao, Y.P., Ni, W.: Grinding size soft sensor model based on neural network. Metal Mines 2, 47–49 (2005) (in Chinese)
Zhou, P., Yue, H., Zhao, D.Y.: Soft-sensor Approach with Case-based Reasoning and Its Application in Grinding Process. Control and Decision 6, 646–650 (2006) (in Chinese)
Susan, C., Ray, C.R.: Learning Aaptation Knowledge to Improve Case-base Reasoning. Artificial Intelligence 7, 1175–1192 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J., Gao, X., Sun, S. (2012). Data-Driven Integrated Modeling and Intelligent Control Methods of Grinding Process. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-31362-2_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
eBook Packages: Computer ScienceComputer Science (R0)