Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control
- 1 Laboratory of Energetics, Electronics and Processes, University of La Reunion, Saint-Denis, France; 2GEPEA, ONIRIS, Nantes, France.
- 2 Laboratory of Energetics, Electronics and Processes, University of La Reunion, Saint-Denis, France; 2GEPEA, ONIRIS, Nantes, France.
- 3 GEPEA, ONIRIS, Nantes, France.
- 4 Laboratory of Energetics, Electronics and Processes, University of La Reunion, Saint-Denis, France
- 5 Laboratory of Energetics, Electronics and Processes, University of La Reunion, Saint-Denis, France
Abstract
This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach is declined according to two different strategies: first, a setpoint tracking is proposed for the couple crystal mass/concentration, whereas a second way consists in tracking of crystal content and concentration. The controlled variables, unavailable online, are issued from an observer developed in previous works. The performance of these strategies, which application to cane sugar crystallization constitutes a real novelty, are compared with experimental data issued from a PID-controlled industrial plant. The results reveal a significant improvement of energy efficiency, leading to an economy of more than 10% of energy.
- U. Vollmer and J. Raisch, “ -Control of a Continuous Crystallizer,” Control Engineering Practice, Vol. 9, No. 8, 2001, pp. 837-845. doi:10.1016/S0967-0661(01)00048-X
- N. Moldovainyi, B. Lakatos and F. Szeifert, “Model Predictive Control of MSMPR Crystallizers,” Journal of Crystal Growth, Vol. 275, No. 1-2, 2005, pp. 1349-1354. doi:10.1016/j.jcrysgro.2004.11.170
- Q. Hu, S. Rohani, D.X. Wang and A. Jutan, “Optimal Control of Batch Cooling Seeding Crystallizer,” Powder Technology, Vol. 156, No. 2-3, 2005, pp. 170-176. doi:10.1016/j.powtec.2005.04.010
- P. Georgieva and S. Feyo de Azevedo, “Neural Network-Based Control Strategies Applied to a Fed-Batch Crystallization Process,” Computational Intelligence, Vol. 3, 2006, pp. 224-233.
- W. Paengjuntuek, A. Arpornwichanop and P. Kittisupakorn, “Product Quality Improvement of Batch Crystallizer by a Batch-to-Batch Optimization and Nonlinear Control Approach,” Chemical Engineering Journal, Vol. 139, No. 2, 2008, pp. 344-350. doi:10.1016/j.cej.2007.08.010
- M. Sheikhzadeh, M. Trifkovic and S. Rohani, “Real-Time Optimal Control of an Anti-Solvent Isothermal Semi- Batch Crystallization Process,” Chemical Engineering Science, Vol. 63, No. 3, 2008, pp. 829-839. doi:10.1016/j.ces.2007.09.049
- Z. K. Nagy, “Model Based Robust Control Approach for Batch Crystallization Product Design,” Computers & Chemical Engineering, Vol. 33, No. 10, 2009, pp. 1685- 1691. doi:10.1016/j.compchemeng.2009.04.012
- S. Feyo de Azevedo, J. Chor?o, M. J. Gon?alves and L. Bento, “On-Line Monitoring of White Sugar Crystallization through Software Sensors, Part I,” International Sugar Journal, Vol. 95, 1993, pp. 483-488.
- S. Feyo de Azevedo, J. Chor?o, M. J. Gon?alves and L. Bento, “On-Line Monitoring of White Sugar Crystallization through Software Sensors, Part II,” International Sugar Journal, Vol. 96, 1994, pp. 18-26.
- D. Devogelaere, M. Rijckaert, O. G. Leon and G. C. Lemus, “Application of Feed Forward Neural Networks for Soft Sensors in the Sugar Industry,” VIIth Brazilian Symp. on Neural Networks, 2002, pp. 2-6.
- A. Simoglou, P. Georgieva, E. B. Martin, A. J. Morris and S. Feyo de Azevedo, “On-Line Monitoring of a Sugar Crystallization Process,” Computers & Chemical Engineering, Vol. 29, No. 6, 2005, pp. 1411-1422. doi:10.1016/j.compchemeng.2005.02.013
- H. M. Hulburt and S. Katz, “Some Problems in Particle Technology. A Statistical Mechanical Formulation,” Chemical Engineering Science, Vol. 19, No. 8, 1964, pp. 555-574. doi:10.1016/0009-2509(64)85047-8