Sensor Monitoring of the Concentrations of Different Gases Present in Synthesis of Ammonia Based On Multi-Scale Entropy and Multivariate Statistics

This paper presents powerful techniques for the development of a new monitoring method based on multi-scale entropy (MSE) in order to characterize the behaviour of the concentrations of different gases present in the synthesis of Ammonia and soft-sensor based on Principal Component Analysis (PCA).

Authors:

• S. Aouabdi
• M. Taibi

Keywords:

• Ammonia synthesis
• concentrations of different gases
• soft sensor
• multi-scale entropy
• multivariate statistics.

References:

[1] M. J. Shah, A.J. Weisenfelder “Control and Optimization of a Large
Ammonia Plant with a Digital Computer,” Automatica. P. Press, G.
Britain. vol. 5, pp. 319–333, 1969.
[2] Lee M.J. Yoo C. Choi W.S. Vanrolleghem A.P. Lee.B.I, “Nonlinar
process monitoring using kernel principal component analysis,”
Elsevier, Chemical Engineering Science Vol.59, pp.223-234, 2004.
[3] R. Isermann, “Process Fault Detection Based on Modeling and
Estimation Methods a Survey”.Operation Process, vol. 4, no. 4, 1984.
[4] P. Laššák, J. Labovský, Ľ. Jelemenský, “Influence of parameter
uncertainty on modeling of industrial ammonia reactor for safety and
operability analysis”, Elsevier, Journal of Loss Prevention in the Process
Industries, N.23.pp 208-288,2010.
[5] HE Ren-Chu, Wan Hao, GU Xiao-Jing, “Advanced Process Control
Technology Implementation in Ammonia Plant” 25th Chinese and
Decision Conference (CCDC).2013.
[6] Ali Mesbash, Stefan Streif, Rolf Findein and Richard D. Braatz, “Active
Fault Diagnosis for Nonlinear Systems with Probabilistic Uncertainties”,
19 th IFAC World Congress, Cape Town, South Africa, August24-29,
2014.
[7] A. Cioloca, R Both and R. Festila, “Model validation, simulation and
control for ammonia synthesis column,” 8 th IEEE International
Symposium on Applied Computational Intelligence and Informatics.
Romania.2013.
[8] Kris Villez, “Multivariate and qualitative data analysis for monitoring,
diagnosis and control of sequencing batch reactors for wastewater
treatment. Ph.D. thesis, Ghent University, Gent, Belgium.2007.
[9] A. Maulud, D. Wang, and J. a. Romagnoli, “A multi-scale orthogonal
nonlinear strategy for multi-variate statistical process monitoring,”
Journal of Process Control, vol. 16, no. 7, pp. 671–683, Aug. 2006.
[10] Dunia R. Qin S. J. and Edgar T.F, “Identification of faulty sensors using
principal component analysis,” AIChE Journal, Vol.42, N. 10, pp.2797-
2812, October 1996.
[11] Thomas Darkow, Rainer Dittmar, HelgeTimm, “Real-time application of
multivariate statistical methods for early event detection in an industrial
slurry stripper,” 19th IFAC World Congress, Cape Town, South Africa,
August 24-29, 2014.
[12] Salahshoor K. Kordestani M. KhoshroM.S, “Design of Online Soft
Sensors Based on Combined Adaptive PCA and DMLP Neural
Networks,” IEEE, 978-1-.4244-2723, September 2009.
[13] KBR Advanced Ammonia Process (KAAPTM) Documentation. [14] K. Zhu, X. Song, and D. Xue, “A roller bearing fault diagnosis method
based on hierarchical entropy and support vector machine with particle
swarm optimization algorithm,” Measurement, vol. 47, pp. 669–675,
Jan. 2014.
[15] M.Costa,A.L. Goldberger,C.K.Peng, “Multi-scale entropy analysis of
phsiologic time series,” Physical Review Letters 89.062102.2002.
[16] Mac Gregor .J.F. Kourti.T, “Statistical process control of multivariate
processes,” Elsevier, Pergamon, Control Eng Practice, Vol.3, N. 3,
pp.403-414, 1995.
[17] Qin Joe.S. Dunia Ricardo, “Determing the number of principal
components for best reconstruction,” Elsevier, Journal Process
Control,N.10. pp 245-250,2000.