Abstract
This study first created a model to predict the content of free calcium oxide (fCaO) of the calcined clinker in the rotary kiln by adopting the technologies of rough sets, neural networks and data fusion. And then it was used to predict the quality of the calcined clinker in the rotary kiln and pleasant simulation results were obtained, indicating that the model is valid and has attained the goal of increasing the training speed and precision. Besides, it has solved many problems in the course of cement production, such as big inertia, lagging, time variation, serious nonlinearity, multiple parameters, serious coupling, and difficulty in creating systematic models.
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References
Chen, T., Sun, J.: Aeroengine Gas Path Fault Diagnosis Using Rough Sets and Neural Networks. Journal of Aerospace Power 1, 207–211 (2006)
Wang, H., Zhang, X., Yu, J.: Fault Diagnosis Based on Support Vector Machine. Journal of East China University of Science and Technology 2, 179–182 (2004)
Liao, R., Liao, Y., Yang, L., et al.: Study on Synthetic Diagnosis Method of Transformer Fault Using Multi-neural Network and Evidence Theory. Proceedings of the CSEE 3, 119–124 (2006)
Li, Y., Jiang, J., Yang, F.: NN-Based D-S Evidence Theory Applied to Multisensor Target Identification. Chinese Journal of Scientific Instrument 6, 652–655 (2001)
Chen, T., Sun, J., Hao, Y.: Neural Network and Dempster-Shafter Theory Based Fault Diagnosis for Aeroengine Gas Path. Acta Aeronautica et Astronautica Sinica 6, 1014–1017 (2006)
Pawlak, Z.: Rough Set-Theoretical Aspects of Reasoning about Data, pp. 9–30. Kluwer Academic Publishers, Dordrecht (1991)
Hashemi, R.R., Le Blanc, L.A., Rucks, C.T., et al.: A hybrid intelligent system for predicting bank holding structures. European Journal of Operational Research, 390–402 (1998)
Li, M., Zhang, H.: Reserch on the method of neural network modeling based on rough sets theory. ACTA Automatica Sinica 1, 27–33 (2002)
He, M., Feng, B., Ma, Z., et al.: Approach to Construct a Rough Neural Networks Based on Rough Set. Journal of Xi’An Jiaotong University 12, 1240–1242 (2004)
Yang, L.: Computer Simulation for Calcinations Process of the Precalciner. Wuhan University of Technology (2004)
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© 2007 Springer-Verlag Berlin Heidelberg
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Shu, Y., Yun, S., Ge, B. (2007). Predicting the Free Calcium Oxide Content on the Basis of Rough Sets, Neural Networks and Data Fusion. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_37
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DOI: https://doi.org/10.1007/978-3-540-74771-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74770-3
Online ISBN: 978-3-540-74771-0
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