Advertisement

Blending Scheduling Under Uncertainty Based on Particle Swarm Optimization with Hypothesis Test

  • Hui Pan
  • Ling Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

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.

Keywords

Particle Swarm Optimization Penalty Function Particle Swarm Optimization Algorithm Octane Number Nonlinear Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, D.M., Yu, C.C., Chien, I.L.: Coordinated Control of Blending Systems. IEEE Trans. Contr. Sys. Tech. 6(4), 495–506 (1998)CrossRefGoogle Scholar
  2. 2.
    Zhang, Y., Nadler, D., Forbes, J.F.: Results Analysis for Trust Constrained Real-time Optimization. J. Process Contr. 11, 329–341 (2001)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Zhao, X.Q., Rong, G.: Blending Scheduling under Uncertainty Based on Particle Swarm Optimization Algorithm. Chinese J. Chem. Eng. 13(4), 535–541 (2005)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Pugachev, V.S.: Probability Theory and Mathematical Statistics for Engineers. Pergamon Press, NY (1984)MATHGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Glismann, K., Gruhn, G.: Short-term Scheduling And Recipe Optimization of Blending Process. Comput. Chem. Eng. 25, 627–634 (2001)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Zhang, Y., Monder, D., Forbes, J.F.: Real-time Optimization under Parametric Uncertainty: A Probability Constrained Approach. J. Process Contr. 12, 373–389 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui Pan
    • 1
  • Ling Wang
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations