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

›› 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

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

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/Abstract

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

S. M. Khazraee, A. H. Jahanmiri. Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System[J]. , 2010, 18(4): 703-710.

参考文献

1 Oisiovici,R.M.,Cruz,S. L.,“State estimation of batch distillation columns using an extended Kalman filter”,Chem.Eng.Sci.,55, 4667-4680(2000).
2 Osiovici,R.M.,Cruz,S. L.,“Inferential control of high-purity multicomponent batch distillation columns using an extended Kalman filter”,Ind.Eng.Chem.Res.,40,2628-2639(2001).
3 Venkateswarlu,C.,Avantika,S.,“Optimal state estimation of multicomponent batch distillation”,Chem.Eng.Sci.,56,5771–5786(2001).
4 Olanrewaju,M. J.,Al-Arfaj,M. A.,“Estimator-based control of reactive distillation system:Application of an extended Kalman filtering”,Chem.Eng.Sci.,61,3386-3399(2006).
5 Venkateswarlu,C.,Kumar,B. J.,“Composition estimation of multicomponent reactive batch distillation with optimal sensor configuration”,Chem. Eng. Sci.,61,5560-5574(2006).
6 Zamprogna,E.,Baroloa,M.,Seborg,D.E.,“Estimating product composition profiles in batch distillation via partial least squares regression”,Control Eng.Practice,12,917-929(2004).
7 Bahar,A.,?zgen,C.,“State estimation for a reactive batch distillation column”,In:Proceedings of the 17th World Congress The International Federation of Automatic Control,Seoul,Korea,July 6-11(2008).
8 Fernandez de Canete,J.,Saz-Orozco,P. D.,Gonzalez-Perez,S., “Application of adaptive neurofuzzy control using soft sensors to continuous distillation”,In:18th European Symposium on Computer Applied Process Engineering-ESCAP18,Elsevier,Lyon,France(2008).
9 Khazraee,S.M.,“Composition estimation of a laboratory reactive batch distillation column”,M.S.Thesis,School of Chemical and Petroleum Engineering,Shiraz University,Shiraz,Iran(2009).
10 Yu,C.,Luyben,W.L.,“Control of multicomponent distillation columns using rigorous composition estimators”,I.Chem.E.Symposium Series,104,A29-A49(1987).
11 Quintero-Marmol,E.,Luyben,W.L.,Georgakis,C.,“Application of an extended luenberger observer to the control of multicomponent batch distillation”,Ind.Chem.Eng.Res.,30,1870-1880(1991).
12 Yildiz,U.,Gürkan,U.A.,?zgen,C.,Leblebicio?lu,K.,“State estimator design for multicomponent batch distillation columns”,Chem. Eng. Res. Des.,83,1-12(2005).
13 Jang,J. S. R.,“ANFIS:Adaptive network-based fuzzy inference systems”,IEEE Trans.on Systems,Man,and Cybernetics,23(3),665-685 (1993).
14 Jang,J. S. R.,Sun,C.T.,Mizutani,E.,Neuro-Fuzzy and Soft Computing:A Computational Approach to Learning and Machine Intelligence,Prentice
Hall,Upper Saddle River,NJ,199-223 (1997).
15 Jang,R.,Sun,C.,“Neuro-fuzzy modeling and control”,Proceedings of the IEEE,83(3),378-405(1995).
16 Luyben,W. L.,Process Modeling,Simulation,and Control for Chemical Engineering,2nd edition,McGraw-Hill,New York,137 (1990).
17 Lee,H.Y.,Huang,H. P.,Chien,I. L.,“Control of reactive distillation process for production of ethyl acetate”,J. Proc. Cont.,17,363-377 (2007).

[1]	Chun Deng, Xuantong Lu, Qixin Zhang, Jian Liu, Jui-Yuan Lee, Xiao Feng. Fuzzy optimization design of multicomponent refinery hydrogen network[J]. 中国化学工程学报, 2022, 48(8): 125-139.
[2]	Beverly S. Chittoo, Clint Sutherland. Column breakthrough studies for the removal and recovery of phosphate by lime-iron sludge: Modeling and optimization using artificial neural network and adaptive neuro-fuzzy inference system[J]. 中国化学工程学报, 2020, 28(7): 1847-1859.
[3]	Hao Tang, Yuan Zong, Ling Zhao. Numerical simulation of micromixing effect on the reactive flow in a co-rotating twin screw extruder[J]. , 2016, 24(9): 1135-1146.
[4]	Liqiang Zhao, Jianlin Wang, Tao Yu, Kunyun Chen, Tangjiang Liu. Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements[J]. Chinese Journal of Chemical Engineering, 2015, 23(11): 1801-1810.
[5]	杨国军, 李秀喜, 钱宇. A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation[J]. Chinese Journal of Chemical Engineering, 2014, 22(3): 318-329.
[6]	胡玉峰. Pathwardhan-Kumer密度规则及基于线性等压关系的密度规则可适性的研究[J]. , 2001, 9(3): 319-321.
[7]	葛庆峰, 李绍芬, 廖晖. DIFFUSION AND REACTION OF BENZENE HYDROGENATION OVER POROUS Ni/γ-Al_2O_3 CATALYST[J]. , 1993, 1(4): 213-222.

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

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价