Multi-units Unified Process Optimization Under Uncertainty Based on Differential Evolution with Hypothesis Test

  • Authors:

  • Wenxiang Lv;Bin Qian;Dexian Huang;Yihui Jin

  • Affiliations:

  • Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China

  • Venue:
  • ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
  • Year:
  • 2009

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Abstract

For large-scale chemical process, which consists of lots of production units, all units have their respective optimization objects which are often conflicting with each other for a series of constraints on material and energy balance. In this paper, the total solution with two layers structure strategy made up of multi-units unified optimization and predictive control of each unit is realized. For the global optimization has high dimension, serious nonlinearity and uncertainty, the optimization algorithm based on differential evolution (DE) is performed, while a hybrid DE approach combining hypothesis test (HT) to compare the optimization objects under uncertainty is proposed. The simulation results of an application example to a 20Mt/a gas separation process show that the proposed total solution with two layers structure strategy is successful and multi-units unified optimization method based on HTDE is effective and robust for solving the optimization problem under uncertainty.