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Thermal Properties Reduced Models by ANN in Process Simulation

  • Xia Yang
  • Rongshan Bi
  • Yugang Li
  • Shiqing Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

It’s very time-consuming to evaluate thermal properties by rigorous methods in process real-time simulation, especially when the simulated project relates to multi-units and multi-components, which takes about 70 to 80 percent of the total simulation time. We developed a new reduced method for thermal properties evaluation based on the artificial neural net(ANN), in which we established several reduced evaluation models using ANN, such as models of vapor-liquid equilibrium, models of vapor-liquid enthalpy and models of temperature calculated from given enthalpy. We used the reduced models in a dynamic distillation simulation. Compared with rigorous thermal properties models, the ANN-reduced models could save 10 to 20 times simulation time with a satisfied accuracy. The results show it’s an efficient and effective method.

Keywords

Thermal Property Hide Layer Output Layer Learning Rate Training Time 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xia Yang
    • 1
  • Rongshan Bi
    • 1
  • Yugang Li
    • 1
  • Shiqing Zheng
    • 1
  1. 1.Qingdao University of Science and TechnologyQingdao

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