Fuzzy Adaptive Control of a Chinese Medicine Sugar Precipitation Process

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This paper illustrates the benefits of a fuzzy adaptive control approach applied to a Chinese medicine sugar precipitation process. A model dedicated to Chinese medicine sugar precipitation was designed, without consideration of crystal size distribution. Fuzzy adaptive robust control algorithm was proposed for the uncertain nonlinear systems. The on-line calculation amount of fuzzy logic system is relatively less, the convergence rate and accuracy are better, and the output of system tracks the setpoints well, even in presence of disturbances and modeling error. The algorithm was applied to the precipitation control of sucrose-glucose mixed solution. Simulation results supported the validity of the proposed algorithm.

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435-440

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] W. Paengiuntuek, P. Kittisupakon, A. Arpornwichanop, Optimization and nonlinear control of a batch crystallization process, Journal of the Chinese Institute of Chemical Engineers, vol. 39, 2008, pp.249-256.

DOI: 10.1016/j.jcice.2007.12.017

Google Scholar

[2] Z. K. Nagy, J. W. Chew, and M. Fujiwara, Comparative performance of concentration and temperature controlled batch crystallizations, Journal of Process Control, vol. 18, 2008, pp.399-407.

DOI: 10.1016/j.jprocont.2007.10.006

Google Scholar

[3] P. Georgieva, S. F. D. Azevedo, Neural network-based control strategies applied to a fed-batch crystallization process, International Journal of Information and Mathematical Sciences, vol. 33, 2007, pp.224-233.

Google Scholar

[4] Z. K. Nagy, Model based robust control approach for batch crystallization product design, Computers and Chemical Engineering, vol. 33, 2009, pp.1685-1691.

DOI: 10.1016/j.compchemeng.2009.04.012

Google Scholar

[5] D. Bonvin, Optimal operation of batch reactors: A personal view, Journal of Process Control, vol. 8, 1998, pp.355-368.

DOI: 10.1016/s0959-1524(98)00010-9

Google Scholar

[6] Z. G. Chen, C. Xu, and H. H. Shao, Batch processes optimization and advanced control—A survey , Control and Instruments in Chemical Industry, vol. 30, 2003, pp.1-6.

Google Scholar

[7] Z. H. Xiong, J. Zhang, Neural network model-based on-line re-optimization control of fed-batch processes using a modified iterative dynamic programming algorithm, Chemical Engineering and Processing, vol. 44, 2005, pp.477-484.

DOI: 10.1016/s0255-2701(04)00155-2

Google Scholar

[8] Z. H. Xiong, J. Zhang, and J. Dong, Optimal iterative learning control for batch processes based on linear time-varying perturbation model, Chinese Journal of Chemical Engineering, vol. 16, 2008, pp.235-240.

DOI: 10.1016/s1004-9541(08)60069-5

Google Scholar