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Model based Soft-Sensor for Industrial Crystallization: On-line Mass of Crystals and Solubility Measurement

Authors: Cédric Damour, Michel Benne, Brigitte Grondin-Perez, Jean-Pierre Chabriat


Monitoring and control of cane sugar crystallization processes depend on the stability of the supersaturation (σ ) state. The most widely used information to represent σ is the electrical conductivity κ of the solutions. Nevertheless, previous studies point out the shortcomings of this approach: κ may be regarded as inappropriate to guarantee an accurate estimation of σ in impure solutions. To improve the process control efficiency, additional information is necessary. The mass of crystals in the solution ( c m ) and the solubility (mass ratio of sugar to water / s w m m ) are relevant to complete information. Indeed, c m inherently contains information about the mass balance and / s w m m contains information about the supersaturation state of the solution. The main problem is that c m and / s w m m are not available on-line. In this paper, a model based soft-sensor is presented for a final crystallization stage (C sugar). Simulation results obtained on industrial data show the reliability of this approach, c m and the crystal content ( cc ) being estimated with a sufficient accuracy for achieving on-line monitoring in industry

Keywords: On-Line Monitoring, Soft-sensor, cane sugarcrystallization

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[1] Grondin-Perez B., M. Benne, C. Bonnecaze & J.-P. Chabriat, 2005, Industrial multi-step forward predictor of mother liquor purity of the final stage of a cane sugar crystallization plant, J. of Food Engineering, 66, pp. 361-367
[2] Grondin-Perez B., M. Benne & J.-P. Chabriat, 2006, Supervision of C crystallization in Bois Rouge sugar mill using on-line crystal content estimation using synchronous microwave and refractometric Brix measurements, J. of Food Engineering, 76, pp. 639-645
[3] Radford D. J., 1985, EP0162580: massecuite supersaturation monitor, TONGAAT HULETT GROUP LIMITED (ZA), Nov. 1985,
[4] Devogelaere D., M. Rijckaert, O.G. Leon & G.C. Lemus, 2002, Application of feed forward neural networks for soft sensors in the sugar industry, VIIth Brazilian Symp. on Neural Networks, pp. 2-6
[5] Bakir T., S. Othman, F. Puel & H. Hammouri, 2005, Continuousdiscrete observer for crystal size distribution of batch crystallization process, 44th IEEE Conf. on Decision and Control 2005 & 2005 European Control Conf.), 12-15 Dec. 2005, pp. 6240-6244
[6] Simoglou A., P. Georgieva, E. B. Martin, A. J. Morris & S. Feyo de Azevedo, 2005, On-line monitoring of a sugar crystallization process, Computers & Chemical Engineering, Vol. 29, Issue 6, pp. 1411-1422
[7] Pautrat C., J. Génotelle & M. Mathlouthi, 1997, Sucrose crystal growth: effect of supersaturation, size and macromolecular impurities, in Sucrose crystallization, science and technology, VanHook et al., Bartens, pp. 57-70
[8] Barth S., 2006, Utilization of FBRM in the Control of CSD in a Batch Cooled Crystallizer, PhD thesis, Georgia Institute of Technology, 121 p.
[9] Semlali Aouragh Hassani N., K. Saidi & T. Bounahmidi, 2001, Steady state modeling and simulation of an industrial sugar continuous crystallizer, Computers & Chemical Engineering, Vol. 25, Issues 9-10, pp. 1351-1370
[10] Feyo de Azevedo S., J. Chorão, M. J. Gonçalves & L. Bento, 1993, Online monitoring of white sugar crystallization through software sensors. Part I, International Sugar J., 95, pp. 483-488
[11] Feyo de Azevedo S., J. Chorão, M. J. Gonçalves & L. Bento, 1994, Online monitoring of white sugar crystallization through software sensors. Part II, International Sugar J., 96, pp. 18-26
[12] Hulburt H. M. & S. Katz, 1964, Some problems in particle technology. A statistical mechanical formulation, Chemical Engineering Science, Vol. 19, pp. 555-574
[13] Ramkrishna D., 1985, The status of population balances, Reviews in chemical engineering, 3 (1), pp. 49-95
[14] Benne M., B. Grondin-Perez & J.-P. Chabriat, 2008, Estimation of two parameters to fit a tendency model for dynamic simulation of an industrial crystallization process, FoodSim 08, 26-28 June 2008, University College Dublin, Ireland
[15] Beyou S., 2008, Proposition d-un algorithme PID ├á paramètres variables pour l-amélioration de la conduite d-un procédé de cristallisation industriel, PhD thesis, University of La Réunion (EA 4079), 181 p.