Abstract
Blending is an important unit operation in process industry. As a nonlinear optimization problem with constraints, it is difficult to obtain optimal solution for blending scheduling, especially under uncertainty. As a novel evolutionary computing technique, particle swarm optimization (PSO) has powerful ability to solve nonlinear optimization problems with both continuous and discrete variables. In this paper, the performance of PSO under uncertainty for blending scheduling problem is investigated, and a new hybrid approach (namely PSOHT) that combines PSO and hypothesis test (HT) is proposed. The simulation results based on an example of gasoline blending problem show that the proposed PSOHT algorithm is valid and effective for solving problem under uncertainty.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, D.M., Yu, C.C., Chien, I.L.: Coordinated Control of Blending Systems. IEEE Trans. Contr. Sys. Tech. 6(4), 495–506 (1998)
Zhang, Y., Nadler, D., Forbes, J.F.: Results Analysis for Trust Constrained Real-time Optimization. J. Process Contr. 11, 329–341 (2001)
Litvinenko, V.I., Burgher, J.A., Vyshemirskij, V.S., Sokolova, N.A.: Application of Genetic Algorithm for Optimization Gasoline Fractions Blending Compounding. In: Proceedings of The IEEE International Conference on Artificial Intelligences Systems (ICAIS 2002), Divnomorskoe, Russia (2002)
Zhao, X.Q., Rong, G.: Blending Scheduling under Uncertainty Based on Particle Swarm Optimization Algorithm. Chinese J. Chem. Eng. 13(4), 535–541 (2005)
Liu, B., Wang, L., Jin, Y.H., Huang, D.X.: Advances in Particle Swarm Optimization Algorithm. Control Instrum. Chem. Ind. 32(3), 1–6 (2005)
Liu, B., Wang, L., Jin, Y.H., Huang, D.X.: Designing Neural Networks Using Hybrid Particle Swarm Optimization. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 391–397. Springer, Heidelberg (2005)
Pugachev, V.S.: Probability Theory and Mathematical Statistics for Engineers. Pergamon Press, NY (1984)
Wang, L., Zhang, L., Zheng, D.Z.: A Class of Hypothesis-test Based Genetic Algorithm for Flow Shop Scheduling with Stochastic Processing Time. International Journal of Advanced Manufacturing Technology 25(11-12), 1157–1163 (2005)
Glismann, K., Gruhn, G.: Short-term Scheduling And Recipe Optimization of Blending Process. Comput. Chem. Eng. 25, 627–634 (2001)
Singh, A., Forbes, J.F., Vermeer, P.J., Woo, S.S.: Model-based Real-time Optimization of Automotive Gasoline Blending Operations. J. Process Contr. 10, 43–58 (2000)
Zhang, Y., Monder, D., Forbes, J.F.: Real-time Optimization under Parametric Uncertainty: A Probability Constrained Approach. J. Process Contr. 12, 373–389 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pan, H., Wang, L. (2006). Blending Scheduling Under Uncertainty Based on Particle Swarm Optimization with Hypothesis Test. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_12
Download citation
DOI: https://doi.org/10.1007/11816102_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
Online ISBN: 978-3-540-37282-0
eBook Packages: Computer ScienceComputer Science (R0)