SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 2, Pages 953-978, DOI: https://doi.org/10.21307/ijssis-2017-903
License : (CC BY-NC-ND 4.0)
Received Date : 17-December-2015 / Accepted: 28-March-2016 / Published Online: 11-June-2016
The nature of optimization for intermittent sugar cane crystallization process is to obtain ideal crystals. One typical difficulty in crystallization optimization refers to the simultaneous effects of both seeding characters and process variables on the final crystal size distribution (CSD) parameters, including mean size (MA) and coefficient of variation (CV). And the application of traditional multi-objective evolutionary algorithm in crystallization process could not optimize all of them. Therefore, this paper puts forward a different multi-objective framework, and correspondingly, an improved optimization algorithm is applied to intermittent sugar cane crystallization. This method combines the elitist non-dominated sorting genetic algorithm (NSGA-II) with technique for order preference which is similar to an ideal solution (TOPSIS), and it provides a quantitative way to analyze the effect of both seed characteristics and process variables on the trade-off between MA and CV. Furthermore, the proposed algorithm has been adapted here to be compared with the NSGA-II, and the comparing results demonstrate better Pareto-optimal solutions of the novel approach.
 S.M. Miller, J. B. Rawlings, “Model identification and control strategies for batch cooling crystallizers”, A. I. Ch. E. Journal, Vol. 40, 1994, pp. 1312-1327.
 Y.D. Lang, A. M. Cervantes, L. T. Biegler, “Dynamic optimization of a batch cooling crystallization process”, Industrial and Engineering Chemistry Research, Vol. 38, 1999, pp. 1469-1477.
 V.Gamez-Garci, H.F. Flores-Mejia，J. Ramirez-Muñz，H. Puebla, “Dynamic optimization and robust control of batch crystallization”, Procedia Engineering, Vol. 42, 2012, pp.471-481.
 A. Fiordalis, C. Georgakis, “Data-driven, using design of dynamic experiments, versus model-driven optimization of batch crystallization processes”, Journal of Process Control, Vol.23, No. 2, 2013, pp. 179-188.
 H. Seki, N. Furuya, S. Hoshino, “Evaluation of controlled cooling for seeded batch crystallization incorporating dissolution”, Chemical Engineering Science, Vol. 77, No. 30, 2012, pp. 10-17.
 A. Gharsallaoui, B. Roge, M. Mathlouthi, “Study of batch maltitol (4-O-α-D-glucopyranosyl-D-glucitol) crystallization by cooling and water evaporation”, Journal of Crystal Growth, Vol. 312, No. 21, 2010, pp. 3183-3190.
 S.M. Lee, K.Y.Kim, S.W. Kim, “Multi-objective optimization of a double-faced type printed circuit heat exchanger”, Applied Thermal Engineering, Vol. 60, No. 1-2, 2013, pp. 44-50.
 A. Suharjono, Wirawan，G. Hendrantoro, “A new unequal clustering algorithm using energy-balanced area partitioning for wireless sensor networks”, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 5, 2013, pp. 1808-1829.
 M. Barrett, M. McNamara, H. X. Hao, P. Barrett, B. Glennon, “Supersaturation tracking for the development, optimization and control of crystallization processes”, Chemical Engineering Research and Design, Vol. 88, No. 8, 2010, pp. 1108-1119.
 L. A. Paz Suárez, P. Georgieva, S. F. de Azevedo, “ Nonlinear MPC for fed-batch multiple stages sugar crystallization”, Chemical Engineering Research and Design, Vol. 89, No. 6, 2011, pp.753-767.
 H. Takiyama, “Supersaturation operation for quality control of crystalline particles in solution crystallization”, Advanced Powder Technology, Vol. 23, No. 3, 2012, pp. 273-278.
 Y. Meng，X. Yu，H. He, et al, “Knowledge-based modeling for predicting cane sugar crystallization state”, International Journal on Smart Sensing and Intelligent Systems,Vol.7, 2014, 942-965.
 D.J. Widenski, A. Abbas, J.A. Romagnoli, “A model-based nucleation study of the combined effect of seed properties and cooling rate in cooling crystallization”, Computers and Chemical Engineering, Vol. 35, No. 12, 2011, pp. 2696-2705.
 Y. Meng, K. Zheng, W. Li, et al, “Design and implementation of intelligent integrated measuring and controlling system for sugar cane crystallization process”, International Journal on Smart Sensing and Intelligent Systems, Vol. 8, No.3, 2015, pp.1687-1705.
 Z. Y. Zhang, K. Hidajat, A. K. Ray, M. Morbidelli, “Multi-objective optimization of SMB and varicol process for chiral separation”, A. I. Ch. E. Journal, Vol.48, No.12, 2002, pp. 2800-2816.
 K. L. Choong, R. Smith, “Optimization of batch cooling crystallization”, Chemical Engineering Science, Vol.59, No.12, 2004, pp. 313-327.
 K. L. Choong, R. Smith, “Novel strategies for optimization of batch, semi-batch and heating/cooling evaporative crystallization”, Chemical Engineering Science, Vol.59, No.12, 2004, pp. 329-343.
 J. Wu, J. Wang, T. Yu, et al, “An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process”, Chinese Journal of Chemical Engineering, Vol. 22, No.10, 2014, pp.1131-1140.
 S. Fazlollahi, S. L. Bungener, G. Becker, et al, “Multi-objective, multi-period optimization of renewable technologies and storage system Using evolutionary algorithms and mixed integer linear programming (MILP) ”, Computer Aided Chemical Engineering, Vol. 31, No.10, 2012, pp. 890-894.
 J. Wang, M. Wang, M. Li, et al, “Multi-objective optimization design of condenser in an organic Rankine cycle for low grade waste heat recovery using evolutionary algorithm”, International Communications in Heat and Mass Transfer, Vol. 45, No.10, 2013, pp.47-54.
 K. Deb, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol.6, No. 2, 2002, pp. 182-197.
 P. Georgieva, M.J. Meireles, SF. de Azevedo, “Knowledge-based hybrid modelling of a batch crystallisation when accounting for nucleation, growth and agglomeration phenomena”, Chemical Engineering Science, Vol.58, No. 16, 2003, pp. 3699-3713.
 H.M. Hulburt, S. Katz, “Some problems in particle technology: A statistical mechanical formulation”, Chemical Engineering Science, Vol.19, No. 8, 1964, pp. 555-574.
 A. D. Randoph, M. A. Larson, “Theory of particulate processes, analysis and techniques of continuous crystallization”, Academic Press Publisher, New York, America, 1971.
 D. Sarkar, S. Rohani, A. Jutan, “Multi-objective optimization of seeded batch crystallization processes”, Chemical Engineering Science Vol.61, No. 16, 2006, pp. 5282-5295.
 N. Srinivas, K. Deb, “Muiltiobjective optimization using nondominated sorting in genetic algorithms”, Evolutionary Computation, Vol.2, No. 3, 1994, pp. 221-248.
 K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol.6, No. 2, 2002, pp. 182-197.  A. Zhou, B. Y. Qu, H. Li, et al, “Multiobjective evolutionary algorithms: A survey of the state of the art,” Swarm and Evolutionary Computation, Vol.1, No.1, 2011, pp.32-49.
 C. E. Shannon, W. Weaver, “The Mathematical Theory of Communication”, University of Illinois Press Publisher, America, 1949.