한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제10권5호
  • /
  • Pages.449-458
  • /
  • 2000
  • /
  • 1976-9172(pISSN)
  • /
  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지

신경회로망을 이용한 원유정제공정에서의 조성식별방법에 관한 연구

A Study on a Neural Network-Based Feed Identification Method in Crude Distillation Unit

  • 이인수 (상주대학교 전자전기공학부) ;
  • 이현철 (서울대학교 제어계측기술연구센터) ;
  • 박상진 (동국대학교 화학공학과) ;
  • 이의수 (동국대학교 화학공학과)
  • 발행 : 2000.10.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 원유정제공정에서의 조성을 효율적으로 예측하기 이한 신경회로망을 이용한 조성식별방법을 제시한다. 제시한 신경회로망을 이용한 조성식별기(FINN)는 학습모드와 예측모드로 구성된다. 또한 Borland C++(3.0)빌드로 신경회로망 원료자동분석 소프트센서 시스템을 구현하였다. 그리고 시뮬레이션을 통해 제안한 신경회로망을 이용한 조성식별방밥의 유용성을 확인하였다.

In this paper, we propose a feed identification method using neural network to predict feed in crude distillation unit. The proposed FINN(feed identifier by neural network) is functionally composed of two modes-training mode and prediction mode. Also, we implement a neural network-based soft sensor system using Borland C++(3.0) Builder. The effectiveness of the proposed neural network-based feed identification method is shown by simulation results.

키워드

참고문헌

  1. Industrial and Engineering Chemistry Research v.30 no.12 Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression T. Mejdell;S. Skogestad
  2. Industrial and Engineering Chemistry Research v.30 no.12 Composition estimator in a pilot-plant distillation column using multiple temperatures T. Mejdell;S. Skogestad
  3. AIChE Journal v.39 no.10 Output estimation using multiple secondary measurements : high-purity distillation T. Mejdell;S. Skogestad
  4. Computers & Journal v.18 no.7 Development of inferential process models using PLS J.V. Kresta;T.E. Marlin;J.F. Macgregor
  5. Computer & Chemical Engineering v.21 no.8 Nonlinear partail least squares E.C. Malthouse;A.C. Tamhane;R.S.H. Mah
  6. Automatica v.28 no.6 Artificial neural networks in process estimation and control M.J. Willis;G.A. Montague;C.D. Massimo;M.T. Tham;A.J. Morris
  7. IEEE Trans. Neural Networks v.4 no.5 Single layer neural networks for linear system identification using gradient descent technique S. Bhama;H. Singh
  8. IEEE Trans. Syst., Man and Cybern. v.21 no.4 Neural networks in process fault diagnosis T. Sorsa;H.N. Koivoand;H. Koivisto
  9. Computer & Chemical Engineering v.12 no.9 Artificial neural network models of knowledge representation in chemical engineering J.C. Hoskins;D.M. Himmelblau
  10. Computer & Chemical Engineering v.14 no.7 process fault detection and diagnosis using neural networks-steady state processes V. Venkatasubramanian;R. Vaidyanathan;Y. Yamamoto
  11. AIChE Journal v.39 no.1 An approach to fault diagnosis of chemical processes via neural networks J.Y. Fan;M. Nikolaou;R.E. White
  12. Parallel distributed processing : explorations inthe microstructure of cognition v.1 D.E. Rumelhart;J.L. McClelland