Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network*

Chin.J.Chem.Eng. ›› 2012, Vol. 20 ›› Issue (6): 1184-1190.

• PROCESS ESTIMATION AND SOFT SENSOR • Previous Articles Next Articles

Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*

YAN Xingdi, YANG Wen, MA Hehe, SHI Hongbo

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

Received:2012-06-08 Revised:2012-07-20 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported by the National Natural Science Foundation of China (61074079);Shanghai Leading Academic Discipline Project(B504);Specialized Research Fund for the Doctoral Program of Higher Education of China (20100074120010);the Natural Science Foundation of Shanghai City (11ZR1409700)

Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*

阎兴頔, 杨文, 马贺贺, 侍洪波

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

通讯作者: SHI Hongbo,E-mail:hbshi@ecust.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (61074079);Shanghai Leading Academic Discipline Project(B504);Specialized Research Fund for the Doctoral Program of Higher Education of China (20100074120010);the Natural Science Foundation of Shanghai City (11ZR1409700)

Abstract

Abstract: The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production.The ammonia concentration at the ammonia converter outlet is a significant process variable,which reflects directly the production efficiency.However,it is hard to be measured reliably online in real applications.In this paper,a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration.A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN.GSO-NH is integrated with BPNN to build a soft sensor model.Finally,the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application.Three other modeling methods are applied for comparison with GSO-NH-NN.The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy.Moreover,the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

Key words: ammonia synthesis, ammonia concentration, soft sensor, group search optimization

摘要： The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production.The ammonia concentration at the ammonia converter outlet is a significant process variable,which reflects directly the production efficiency.However,it is hard to be measured reliably online in real applications.In this paper,a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration.A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN.GSO-NH is integrated with BPNN to build a soft sensor model.Finally,the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application.Three other modeling methods are applied for comparison with GSO-NH-NN.The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy.Moreover,the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

关键词: ammonia synthesis, ammonia concentration, soft sensor, group search optimization

YAN Xingdi, YANG Wen, MA Hehe, SHI Hongbo. Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1184-1190.

阎兴頔, 杨文, 马贺贺, 侍洪波. Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1184-1190.

References

1 Huang,D.,Liu,Y.,Li,Y.,"Soft sensor research and its application in wastewater treatment",CIESC Journal,62 (1),1-9 (2011).(in Chinese)
2 Fu,Y.,Su,H.,Zhang,Y.,Chu,J.,"Adaptive soft-sensor modeling algorithm based on FCMISVM and its application in PX adsorption separation process",Chin.J.Chem.Eng.,16 (5),746-751 (2008).
3 Wang,J.,Yu,T.,Jin,C.,"On-line estimation of biomass in fermentation process using support vector machine",Chin.J.Chem.Eng.,14 (3),383-388 (2006).
4 Bowker,M.,Parker,I.B.,Waugh,K.C.,"Extrapolation of the kinetics of model ammonia synthesis catalysts to industrially relevant temperatures and pressures",Appl.Catal.,14 (1-3),101-118 (1985).
5 Abashar,M.,"Application of heat interchange systems to enhance the performance of ammonia reactors",Chem.Eng.J.,78 (1),69-79 (2000).
6 Mancusi,E.,Maffettone,P.L.,Gioia,F.,Crescitelli,S.,"Nonlinear analysis of an industrial ammonia reactor with heterogeneous model",Comput.Aid.Chem.Eng.,9,225-230 (2001).
7 Kasiri,N.,Hosseini,A.R.,Moghadam,M.,"Dynamic simulation of an ammonia synthesis reactor",Comput.Aid.Chem.Eng.,14,695-700 (2003).
8 Rumelhart,D.E.,McClelland,J.L.,"Parallel distributed processing: Explorations in the microstructure of cognition",Comput.Models Cognition Perception,1 (1),318-362 (1986).
9 Geng,Y.,Zheng,J.,Shi,T.,Shi,G.,"Wet gas meter development based on slotted orifice couple and neural network techniques",Comput.Aid.Chem.Eng.,15 (2),281-285 (2007).
10 Du,J.K.,"Prediction on lower flammability limit temperature of organic compounds based on GA-BP neural network",CIESC Journal,61 (2),3067-3071 (2010).(in Chinese)
11 Karimi,H.,Yousefi,F.,"Correlation of vapour liquid equilibria of binary mixtures using artificial neural networks",Comput.Aid.Chem.Eng.,15 (5),765-771 (2007).
12 He,S.,Wu,Q.H.,Saunders,J.R.,"A novel group search optimizer inspired by animal behavioural ecology",In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation,IEEE Press,Vancouver,BC,1272-1278 (2006).
13 He,S.,Wu,Q.H.,Saunders,J.R.,"Group search optimizer: an optimization algorithm inspired by animal searching behavior",IEEE Trans.Evolut.Comput.,13 (5),973-990 (2009).
14 He,S.,Cooper,H.J.,Ward,D.G.,Yao,X.,Heath,J.K.,"Analysis of premalignant pancreatic cancer mass spectrometry data for biomarker selection using a group search optimizer",Trans.Inst.Measur.Control.,34 (6),668-676 (2012).
15 Zhong,G.,Liu,F.,"Optimal design of plate structures with discrete variables by group search optimizer",Adv.Sci.Lett.,4 (3),1057-1061 (2011).
16 Zhang,W.F.,Teng,S.H.,Li,L.J.,"Improved group search optimizer algorithm",Comput.Eng.Appl.,45 (4),48-51 (2009).
17 Ballerini,M.,Cabibbo,N.,Candelier,R.,Cavagna,A.,Cisbani,E.,Giardina,I.,Lecomte,V.,Orlandi,A.,Parisi,G.,Procaccini,A.,"Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study",Proc.Nat.Acad.Sci.,105 (4),1232-1237 (2008).
18 Suganthan,P.N.,"Particle swarm optimiser with neighbourhood operator",In: Proceedings of the IEEE Congress on Evolutionary Computation,IEEE Press,Washington,DC,1958-1962 (1999).
19 Eberhart,R.C.,Shi,Y.,Kennedy,J.,Handbook of Nature-Inspired and Innovative Computing,Springer,US,187-219 (2001).
20 Xu,W.,Zhang,L.,Gu,X.,"Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved particle swarm optimization and BP neural network",Chem.Eng.Res.Des.,89 (10),2102-2109 (2011).

[1]	Yanliang Zhou, Qianjin Sai, Zhenni Tan, Congying Wang, Xiuyun Wang, Bingyu Lin, Jun Ni, Jianxin Lin, Lilong Jiang. Highly efficient subnanometer Ru-based catalyst for ammonia synthesis via an associative mechanism [J]. Chinese Journal of Chemical Engineering, 2022, 43(3): 177-184.
[2]	Kaixun He, Kai Wang, Yayun Yan. Active training sample selection and updating strategy for near-infrared model with an industrial application [J]. Chinese Journal of Chemical Engineering, 2019, 27(11): 2749-2758.
[3]	Honggui Han, Shuguang Zhu, Junfei Qiao, Min Guo. Data-driven intelligent monitoring system for key variables in wastewater treatment process [J]. Chin.J.Chem.Eng., 2018, 26(10): 2093-2101.
[4]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics [J]. Chin.J.Chem.Eng., 2018, 26(1): 137-143.
[5]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor [J]. , 2017, 25(9): 1230-1237.
[6]	Huan Min, Xionglin Luo. Calibration of soft sensor by using Just-in-time modeling and AdaBoost learning method [J]. , 2016, 24(8): 1038-1046.
[7]	Wentao Cang, Huizhong Yang. Estimation of catalytic activity using an unscented Kalman filtering in condensation reaction [J]. Chin.J.Chem.Eng., 2015, 23(12): 1965-1969.
[8]	Weiming Shao, Xuemin Tian, PingWang. Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor [J]. Chin.J.Chem.Eng., 2015, 23(12): 1925-1934.
[9]	Yanlin He, Yuan Xu, Zhiqiang Geng, Qunxiong Zhu . Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network [J]. Chin.J.Chem.Eng., 2015, 23(1): 138-145.
[10]	Ouguan Xu, Yongfeng Fu, Hongye Su, Lijuan Li . A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling [J]. , 2014, 22(7): 799-804.
[11]	CAO Pengfei, LUO Xionglin. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification [J]. Chin.J.Chem.Eng., 2014, 22(5): 538-548.
[12]	LÜ Ye, YANG Huizhong. A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion [J]. Chin.J.Chem.Eng., 2014, 22(2): 146-152.
[13]	XU Feng, WANG Yeye, LUO Xionglin. Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes [J]. Chin.J.Chem.Eng., 2013, 21(9): 1038-1047.
[14]	NIAN Xiaoyu, WANG Zhenlei, QIAN Feng. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace [J]. Chin.J.Chem.Eng., 2013, 21(5): 537-543.
[15]	PAN Chonggen, LI Ying, JIANG Wen, LIU Huazhang Zhejiang. Effects of Reaction Conditions on Performance of Ru Catalyst and Iron Catalyst for Ammonia Synthesis [J]. , 2011, 19(2): 273-277.

Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*

Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments