Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization*

Chin.J.Chem.Eng. ›› 2012, Vol. 20 ›› Issue (6): 1089-1093.

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization^*

LI Shaojun, LI Feng

Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology), Ministry of Education, Shanghai 200237, China

Received:2012-04-17 Revised:2012-07-25 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported by the National Natural Science Foundation of China (20976048;21176072);the Fundamental Research Funds for the Central Universities

Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization^*

李绍军, 李凤

Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology), Ministry of Education, Shanghai 200237, China

通讯作者: LI Shaojun,E-mail:Lishaojun@ecust.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (20976048;21176072);the Fundamental Research Funds for the Central Universities

Abstract

Abstract: Cracking gas compressor is usually a centrifugal compressor.The information on the performance of a centrifugal compressor under all conditions is not available,which restricts the operation optimization for compressor.To solve this problem,two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer.The input data of the model under other conditions should be corrected according to the similarity theory.The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor.The result shows that it is an effective method to optimize the compressor system.

Key words: compressor, characteristic curve, neural-network, modeling

摘要： Cracking gas compressor is usually a centrifugal compressor.The information on the performance of a centrifugal compressor under all conditions is not available,which restricts the operation optimization for compressor.To solve this problem,two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer.The input data of the model under other conditions should be corrected according to the similarity theory.The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor.The result shows that it is an effective method to optimize the compressor system.

关键词: compressor, characteristic curve, neural-network, modeling

LI Shaojun, LI Feng. Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1089-1093.

李绍军, 李凤. Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1089-1093.

References

1 Li,J.H.,"Technical measures to inhibit coking of cracking gas compressor",Journal of Chemical Industry & Engineering,31 (4),46-48 (2010).
2 Sieros,G.,Stamatis,A.,Mathioudakis,K,"Jet engine component maps for performance modeling and diagnosis",Propulsion & Power,13 (5),665-674 (1997).3 Moraal,P.,Kolmanovsky,I.,"Turbo charger modeling for automotive control application",SAE Trans,108,1324-1338 (1999).
4 Ghorbanian,K.,Gholamrezaei,M.,"An artificial neural network approach to compressor performance prediction",Applied Energy,86,1210-1221 (2009).
5 Kong,C.D.,Kho,S.H.,Ki,J.Y.,"Component map generation of a gas turbine using genetic algorithms",Trans ASME J Eng Gas Turbines Power,128 (1),92-96 (2006).
6 Kong,C.D.,Ki,J.Y.,"Components map generation of gas turbine engine using genetic algorithms and engine performance deck data",Trans ASME J Eng Gas Turbines Power,129 (2),312-317 (2007).
7 Yu,Y.H.,Chen,L.G.,Sun,F.R.,Wu,C.H.,"Neural-network based analysis and prediction of compressor’s characteristic performance map",Applied Energy,84,48-55 (2007).
8 Tirnovan,R.,Giurgea,S.,Miraoui,A.,Cirrincione,M.,"Modeling the characteristics of turbo compressors for fuel cell systems using hybrid method based on moving least squares",Applied Energy,86,1283-1289 (2009).
9 Teemu,T.S.,Pekka,R.,Juha,H.,Jari,B.,"Predicting off-design range and performance of refrigeration cycle with two-stage centrifugal compressor and flash intercooler",International Journal of Refrigeration,33 (6),1152-1160 (2010).
10 Lazzaretto,A.,Toffolo,A.,"Analytical and neural-network models for gas-turbine design and off-design simulation",Int.J.Applied Thermodynamics,4 (4),173-182 (2001).
11 Xu,Z.,The Principle of Centrifugal Compressor,Machine Press,Beijing (1990).(in Chinese)
12 Ma,W.T.,Liu,Y.W.,Su,M.,Yu,N.H.,"Multi-stage axial flow compressors characteristics estimation based on system identification",Energy Conversion and Management,49 (2),143-150 (2008).
13 Simon,H.,Neural Networks: A Comprehensive Foundation,2nd edition,Prentice-Hall Inc.,Canada (2001).
14 Ma,P.F.,Wu,C.B.,LI,F.Y.,"Cause analysis of coking of pyrolysis gas compressor and retrofits",Chemical Engineering & Machinery,36 (2),150-153 (2009).(in Chinese)
15 Zhao,J.Y.,Liang,L.W.,Cao,T.T.,Yu,B.W.,"The application of process simulation technology in the ethylene plant",Petrochemical Technology,39,908-909 (2010).(in Chinese)
16 MIT,Aspen-tech,User’s Guide of ASPEN PLUS 10,USA (2001).

[1]	Jindong Dai, Chi Zhai, Jiali Ai, Guangren Yu, Haichao Lv, Wei Sun, Yongzhong Liu. A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 262-274.
[2]	Borui Liu, Tao Zhang, Yi Zheng, Kailong Li, Hui Pan, Hao Ling. A dynamic control structure of liquid-only transfer stream distillation column [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 135-145.
[3]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 244-255.
[4]	Guangyuan Chen, Tong Zhou, Meng Zhang, Zhongxiang Ding, Zhikun Zhou, Yuanhui Ji, Haiying Tang, Changsong Wang. Effects of heavy metal ions Cu²⁺/Pb²⁺/Zn²⁺ on kinetic rate constants of struvite crystallization [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 10-16.
[5]	Bowen Jiang, Jia Liu, Guoqiang Yang, Zhibing Zhang. Efficient conversion of CO₂ into cyclic carbonates under atmospheric by halogen and metal-free poly(ionic liquid)s [J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 202-211.
[6]	Hany M. Abd El-Lateef, Mai M. Khalaf, K. Shalabi, Antar A. Abdelhamid. Multicomponent synthesis and designing of tetrasubstituted imidazole compounds catalyzed via ionic-liquid for acid steel corrosion protection: Experimental exploration and theoretical calculations [J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 304-319.
[7]	Xibao Zhang, Zhenghong Luo. Bubble size modeling approach for the simulation of bubble columns [J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 194-200.
[8]	Zhi-Guo Yuan, Yu-Xia Wang, You-Zhi Liu, Dan Wang, Wei-Zhou Jiao, Peng-Fei Liang. Research and development of advanced structured packing in a rotating packed bed [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 178-186.
[9]	Kangcheng Wang, Jie Zhang, Dexian Huang. Online temperature estimation of Shell coal gasification process based on extended Kalman filter [J]. Chinese Journal of Chemical Engineering, 2022, 47(7): 134-144.
[10]	Xiang Wu, Yuzhou Hou, Kanjian Zhang, Ming Cheng. Dynamic optimization of 1,3-propanediol fermentation process: A switched dynamical system approach [J]. Chinese Journal of Chemical Engineering, 2022, 44(4): 192-204.
[11]	Danlong Li, Yannan Liang, Hainan Wang, Ruoqian Zhou, Xiaokang Yan, Lijun Wang, Haijun Zhang. Investigation on the effects of fluid intensification based preconditioning process on the decarburization enhancement of fly ash [J]. Chinese Journal of Chemical Engineering, 2022, 44(4): 275-283.
[12]	Mohsen Rezaeimanesh, Ali Asghar Ghoreyshi, S.M. Peyghambarzadeh, Seyed Hassan Hashemabadi. A coupled CFD simulation approach for investigating the pyrolysis process in industrial naphtha thermal cracking furnaces [J]. Chinese Journal of Chemical Engineering, 2022, 44(4): 528-542.
[13]	Tao Tian, Yayan Wang, Bing Liu, Zhaoyang Ding, Xinxi Xu, Meisheng Shi, Jun Ma, Yanjun Zhang, Donghui Zhang. Simulation and experiment of six-bed PSA process for air separation with rotating distribution valve [J]. Chinese Journal of Chemical Engineering, 2022, 42(2): 329-337.
[14]	Wenhui Yang, Haoyu Yin, Zhihong Yuan, Bingzhen Chen. Flexibility analysis for continuous ibuprofen manufacturing processes [J]. Chinese Journal of Chemical Engineering, 2022, 51(11): 115-125.
[15]	Min Wu, Juanjuan Ding, Zhaofeng Zhang, Shengping You, Wei Qi, Rongxin Su, Zhimin He. Kinetic modeling of gamma-aminobutyric acid production by Lactobacillus brevis based on pH-dependent model and rolling correction [J]. Chinese Journal of Chemical Engineering, 2022, 50(10): 352-360.

Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization^*

Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization^*

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments