Dynamic Process Monitoring of an Ammonia Synthesis Fixed-Bed Reactor
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Dynamic Process Monitoring of an Ammonia Synthesis Fixed-Bed Reactor

Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

Abstract:

This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: Ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1111691

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1928

References:


[1] C.C. Pantelides, J. G. Renfro," The online use of first-principles models in process operations: Review, current status and future needs" Comput. Chem. Eng. 2013, 51(0),136-148. DOI: 10.1016/j.compchemeng.2012.07.008
[2] P. Geladi, B. Kowalski, "Partial least square regression: A tutorial" Anal. Chim. Acta. 1986, 185(1), 1-17. DOI:10.1016/0003-2670(86)80028-9
[3] A. Höskuldsson,"PLS regression methods" J. Chemom. 1988, 2(3), 211–228. DOI: 10.1002/cem.1180020306
[4] S. Wold, M. Sjöström, L. Eriksson, " PLS-regression: a basic tool of chemometrics " Chemom. Intell. Lab. Syst. 2001, 58(2), 109-130. DOI: 10.1016/S0169-7439(01)00155-1
[5] H. Abdi," Partial least squares regression and projection on latent structure regression (PLS Regression)" Wiley Interdiscip. Rev.: Comput. Stat. 2010, 2(1), 97-106. DOI: 10.1002/wics.51
[6] M. H. Kaspar, W. H. Ray,"Chemometric methods for process monitoring and high-performance controller design" AIChE J. 1992, 38(10), 1593-1608. DOI: 10.1002/aic.690381010
[7] A. Simoglou, E. B. Martin, A. J. Morris, " Multivariate statistical process control of an industrial fluidised-bed reactor "Control Engineering Practice. 2000, 8(8), 893-909. DOI: 10.1016/S0967-0661(00)00015-0
[8] F. Yacoub, J. F. MacGregor," Analysis and optimization of a polyurethane reaction injection molding (RIM) process using multivariate projection methods" Chemom. Intell. Lab. Syst. 2003, 65(1), 17-33. DOI: 10.1016/S0169-7439(02)00088-6
[9] O. Marjanovic, B. Lennox, D. Sandoz, K. Smith, M. Crofts," Real-time monitoring of an industrial batch process" Comput. Chem. Eng.2006, 30(10-12), 1476-1481. DOI: 10.1016/j.compchemeng.2006.05.040
[10] M.H. Kaspar, H. W. Ray," Dynamic PLS modelling for process control" Chem. Eng. Sci. 1993, 48(20), 3447-3461. DOI: 10.1016/0098-1354(93)80079-3
[11] S. Lakshminarayanan, S. L. Shah, K. Nandakumar," Modeling and control of multivariable processes: Dynamic PLS approach" AIChE J. 1997, 43(9), 2307-2322. DOI: 10.1002/aic.690430916
[12] S. J. Qin," Partial least squares regression for recursive system identification" presented at 32nd IEEE Conf. on Decision and Control, Austin, USA, December 1993, DOI: 10.1109/cdc.1993.325671
[13] S. Wold," Exponentially Weighted Moving Principal Component Analysis and Projections to Latent Structuers" Chemom. Intell. Lab. Syst. 1994, 23(1), 149-161. DOI:10.1016/0169-7439(93)E0075-F
[14] S. J. Qin," Recursive PLS algorithms for adaptive data modeling" Comput. Chem. Eng. 1998, 22(4-5), 503-514. DOI:10.1016/S0098-1354(97)00262-7
[15] X. Wang, U. Kruger, B. Lennox," Recursive partial least squares algorithms for monitoring complex industrial processes" Control Engineering Practice. 2003, 11(6), 613-632. DOI: 10.1016/S0967-0661(02)00096-5
[16] L. E. Wangen, B. R. Kowalski," A multiblock partial least squares algorithm for investigating complex chemical systems" J. Chemom. 1989, 3(1), 3-20. DOI: 10.1002/cem.1180030104
[17] J.F. MacGregor, C. Jaeckle, C. Kiparissides, M. Koutoudi," Process monitoring and diagnosis by multiblock PLS methods" AIChE J. 1994, 40(5), 826-838. DOI: 10.1002/aic.690400509
[18] S. Wold, N. Kettaneh, K. Tjessem," Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection" J. Chemom. 1996, 10(5-6), 463-482. DOI: 10.1002/(SICI)1099-128X(199609)10:5/6<463::AID-CEM445>3.0.CO;2-L
[19] J. A. Westerhuis, P. M. J. Coenegracht," Multivariate modelling of the pharmaceutical two-step process of wet granulation and tableting with multiblock partial least squares" J. Chemom. 1997, 11(5), 379-392.
[20] X. Wang, U. Kruger, B. Lennox, P. Goulding," A novel multiblock method using latent variable partial least squares" in Proc. Am. Control Conf., Arlington, VA, June 2001.
[21] D. Zhou, G. Li, S. J. Qin," Total projection to latent structures for process monitoring" AIChE J. 2010, 56(1), 168-178. DOI: 10.1002/aic.11977
[22] N.B. Gallagher, B. M. Wise, S. W. Butler, D. D. White, G.G. Barna," Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness Through Model Updating" in Proc. of ADCHEM 97, Banff, Canada, June 1997.
[23] Helland, K., H. E. Berntsen, O. Borgen, H. Martens,"Recursive algorithm for partial least squares regression" Chemom. Intell. Lab. Syst. 1992, 14(1-3), 129-137. DOI:10.1016/0169-7439(92)80098-O
[24] C. Rosen, J. A. Lennox," Multivariate and multiscale monitoring of wastewater treatment" Water Res. 2001, 35(14), 3402-3410. DOI: 10.1016/S0043-1354(01)00069-0
[25] H. W. Lee, M. W. Lee, J. M. Park," Robust adaptive partial least squares modeling of a full-scale industrial wastewater treatment proces" Ind. Eng. Chem. Res. 2006, 46(3), 955-964. DOI: 10.1021/ie061094+
[26] S. J. Qin, H. H. Yue, " Reconstruction based fault identification using a combined index" Ind. Eng. Chem. Res. 2001, 40(20), 4403. DOI: 10.1021/ie000141+
[27] S. J. Qin," Statistical process monitoring: Basics and beyond" J. Chemom. 2003, 17(8-9), 480-502. DOI: 10.1002/cem.800
[28] D. J. Cummins, C. W. Andrew," Iteratively reweighted partial least squares: A performance analysis by Montecarlo simulation" J. Chemom. 1995, 9(6), 489-507. DOI: 10.1002/cem.1180090607
[29] J. C. Morud, S. Skogestad," Analysis of instability in an industrial ammonia reactor" AIChE J. 1998, 44(4), 888-895. DOI: 10.1002/aic.690440414
[30] E. Holter, M.Sc. Thesis," Feedforward for Stabilization of an Ammonia Synthesis Reactor" Department of Engineering Cybernetics, Norwegian University of Science and Technology, 2010.
[31] N. L. Ricker," The use of biased least-squares estimators for parameters in discrete-time pulse-response models" Ind. Eng. Chem. Res. 1988, 27(2), 343-350.
[32] J. S. Qin, T. J. McAvoy," A data-based process modeling approach and its applications" In Proc.of the 3rd IFAC Conf. 1993.
[33] G. Baffi, E. B. Martin, A. J. Morris," Non-linear dynamic projection to latent structures modelling" Chemom. Intell. Lab. Syst. 2000, 52(1), 5-22. DOI:10.1016/S0169-7439(00)00083-6
[34] D. C. Montgomery, Introduction to statistical quality control. 5th ed., John Wiley& Sons. USA 2005.
[35] H. Akaike," A new look at the statistical model identification" IEEE Trans. Autom. Control.1974, 19(6), 716-723.