Study on Control of the Second Carbonation in the Clarifying Process of Sugar Cane Juice via Generalized Predictive Control Algorithm

Chun Ning Song; Wen Han Zhong

doi:10.4028/www.scientific.net/AMM.303-306.1257

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306Study on Control of the Second Carbonation in the...

Study on Control of the Second Carbonation in the Clarifying Process of Sugar Cane Juice via Generalized Predictive Control Algorithm

Abstract:

The second carbonation in the clarifying process of sugar cane juice is a dynamic nonlinear system which has the characteristics of strong non-linearity, multi-constraint, large time-delay, multi-input and other characteristics of complex nonlinear systems. In this paper, BP neural network is applied to the model of the second carbonation clarifying process of sugar cane juice. The generalized predictive control algorithm is employed to the optional control of color value in clarifying process of second carbonation. The result of Matlab simulation shows that generalized predictive control algorithm based on BP neural network implement the optimal control of the second carbonation with strong robustness and high control precision.

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Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

1257-1260

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.1257

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Online since:

February 2013

Authors:

Chun Ning Song, Wen Han Zhong

Keywords:

BP Neural Network (BPNN), Clarifying Process, Generalized Predictive Control (GPC), Second Carbonation

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References

[1] Weijun Chen, Sixin Xu. The Principle and Technique of Cane Sugar, Clarification[M]. Beijing: China Light Industry Press，2000, 12.

Google Scholar

[2] Liqun Hang. Artificial Neural Network Theory, Design and Application [M]. Beijin: Chemical Industry Press，(2007).

Google Scholar

[3] Clarke D W, Mohtadi C , Tuffs P S. Generalized Predictive Control: Part 1 and Part 2[J]. Automatica, 1987, 23( 1) : 137～160.

DOI: 10.1016/0005-1098(87)90088-4

Google Scholar

[4] Wei Wang, Jianjun Yang. Generalized Predictive Contol: Theory, Algorithm and Application[J]. Control Theory and Applications, 1997, 14( 6) : 777～786.

Google Scholar

[5] A. Pawlowski J.L. Guzman J.E. Normey,M. Berenguel. A practical approach for Generalized Predictive Control within an event-based framework[J]. Computers & Chemical engineering, 41(2012), 52～66.

DOI: 10.1016/j.compchemeng.2012.03.003

Google Scholar

[6] Ayla Altinten. Generalized predictive control applied to a pH neutralization process[J]. Computers & Chemical engineering, 31(2007), 1199～1204.

DOI: 10.1016/j.compchemeng.2006.10.005

Google Scholar

[7] Yong Lu, Xiangdong Xu. A Neural Net-based General Predictive Control Strategy for Use on Boilers[J]. Journal of Engineering for Thermal Energy and Power, 2001, 61(91): 55～58.

Google Scholar