Earticle

초록

영어

The Tennessee Eastman chemical process is a well-defined simulation of a chemical process that has been commonly used in process control research. As chemical process plants are getting more complex, the pressure on chemical engineers to develop accurate models for monitoring and control purposes is increased. In this paper, we explore the idea of using Genetic Programming (GP) technique to model the Tennessee Eastman (TE) Chemical Process Reactor. The process is decomposed to four subsystems. They are reactor level, reactor pressure, reactor cooling water temperature, and reactor temperature subsystems. GP found to have many advantages over other techniques in developing an automated process for industrial system modeling. A comparison between the applications of GP in modeling the TE chemical reactors subsystems with respect to other soft computing techniques such as Artificial Neural Networks (ANN), fuzzy Logic (FL) and Neuro-Gas and Neuro-PSO is provided.

목차

Abstract
 1. Introduction
 2. Tennessee Eastman Process Description
 3. System Identification Procedure
 4. Why Genetic Programming?
 5. How Genetic Programming Works?
 6. Experimental Data
 7. GP Experimental Setup
 8. Experimental Results
 9. Conclusions
 References

키워드

저자

• Hossam Faris [ Business Information Technology Department, The University of Jordan ]
• Alaa F. Sheta [ Computer Science Department, Taif University, Saudi Arabia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605