SCI和EI收录∣中国化工学会会刊

›› 2010, Vol. 18 ›› Issue (4): 703-710.

• • 上一篇    

Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System

S. M. Khazraee, A. H. Jahanmiri   

  1. School of Chemical and Petroleum Engineering, Shiraz University, Shiraz 71345, Iran
  • 收稿日期:2009-12-14 出版日期:2010-08-28 发布日期:2010-08-28
  • 通讯作者: A. H. Jahanmiri, E-mail:jahanmir@shirazu.ac.ir

Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System

S. M. Khazraee, A. H. Jahanmiri   

  1. School of Chemical and Petroleum Engineering, Shiraz University, Shiraz 71345, Iran
  • Received:2009-12-14 Online:2010-08-28 Published:2010-08-28

摘要: Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS),which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.

关键词: reactive batch distillation, multicomponent, pilot plant, adaptive neuro-fuzzy inference system, state estimation

Abstract: Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS),which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.

Key words: reactive batch distillation, multicomponent, pilot plant, adaptive neuro-fuzzy inference system, state estimation