CIESC Journal ›› 2018, Vol. 69 ›› Issue (11): 4814-4822.doi: 10.11949/j.issn.0438-1157.20180534

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Self-adaptive iterative hybrid modeling and its application in acetylene hydrogenation process

GUO Jingjing, XU Jinjin, DU Wenli, YE Zhencheng   

  1. State Key Laboratory of Chemical Engineering, Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2018-05-22 Revised:2018-07-03 Online:2018-11-05 Published:2018-07-10
  • Supported by:

    supported by the Major Program of National Natural Science Foundation of China (61590923), the Key Program of National Natural Science Foundation of China (61333010) and the National Natural Science Foundation for Distinguished Young Scholars(61725301).

Abstract:

The reaction mechanism of chemical process is complex. There is a modeling error between the mechanism model and the actual reaction system. At the same time, there are complex slow time-varying features, such as catalyst deactivation, fuel coking, etc. Thus there will be a mismatch between the process model and the actual process system. A self-adaptive iterative hybrid model (SAIHM) is established to reflect the dynamic characteristics of the process accurately over a long period. The mechanism model and the data-driven model are effectively combined to improve the prediction accuracy of the model; the data-driven model uses the deep recurrent neural network (DRNN) to fully exploit the timing relationship between adjacent conditions; the data-driven model is automatically updated based on the evaluation indicators to resolve the contradiction between accuracy and efficiency. The simulation and comparison results of the self-adaptive iterative hybrid model and the existing mechanism model established based on the historical operation data of an acetylene hydrogenation adiabatic reactor show that the self-adaptive iterative hybrid model can more effectively track the actual system.

Key words: self-adaptive, iterative, hybrid model, dynamic, acetylene hydrogenation

CLC Number: 

  • TQ221.242

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