Development of Genetically tuned Fuzzy dynamic model for nonlinear dynamical systems: Application on reaction section of Tennessee Eastman process

Document Type : Article

Authors

1 Petroleum Refining Technology Development Division, Research Institute of Petroleum Industry (RIPI)-West side of Azadi Complex-Tehran-Iran.

2 Petroleum Refining Technology Development Division, Research Institute of Petroleum Industry (RIPI), West side of Azadi Complex, Tehran, Iran.

3 Dept. of Chemical and Petroleum Eng. Sharif University of Technology Tehran, Iran

Abstract

This work presents a new GA-Fuzzy method to model dynamic behavior of a process, based on Recurrent Fuzzy modeling through Mamdani approach whose inference system is optimized by Genetic Algorithms. By using the Mamdani approach, the proposed method surmounts the need to solve various types of mathematical equations governing the dynamic behavior of the process.
The proposed method consists of two steps; i) constructing a startup version of the model, ii) optimizing the shape of membership functions of the fuzzy sets corresponding to the variables exist in the fuzzy model, along with the production rules constituting the inference such that the obtained fuzzy model can predict the dynamic behavior of the process fairly accurately.
The proposed method is used to predict the dynamic behavior of the reaction section of the Tennessee Eastman (TE) benchmark. The overall accuracy of the obtained results compared to their corresponding counterparts in TE benchmark. The mean absolute percentage error (MAPE) of the key process variables which are temperature, pressure, and level of the reactor, and the reactor cooling water outlet temperature were calculated as 1.17%, 0.38%, 1.5%, and 1.57%, respectively, showing high prediction capability of the proposed method.

Keywords

Main Subjects

References

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Scientia Iranica
Volume 25, Issue 6
Transactions on Chemistry and Chemical Engineering (C)
November and December 2018
Pages 3381-3390

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History

  • Receive Date: 08 October 2017
  • Revise Date: 19 March 2018
  • Accept Date: 18 June 2018

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