Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: http://www.sciepub.com/journal/automation Editor-in-chief: Santosh Nanda
Open Access
Journal Browser
Go
Journal of Automation and Control. 2015, 3(1), 10-17
DOI: 10.12691/automation-3-1-2
Open AccessArticle

Robust Identification of Hydrocarbon Debutanizer Unit using Radial Basis Function Neural Networks (RBFNNs)

Masih Vafaee Ayouri1, , Mehdi Shahbazian1, Bahman Moslemi2 and Mahboobeh Taheri3

1Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran

2Department of Basic Science, Petroleum University of Technology, Ahwaz, Iran

3Senior expert in R&D, Sarkhon & Qeshm Gas Company, Bandar Abbas, Iran

Pub. Date: January 13, 2015

Cite this paper:
Masih Vafaee Ayouri, Mehdi Shahbazian, Bahman Moslemi and Mahboobeh Taheri. Robust Identification of Hydrocarbon Debutanizer Unit using Radial Basis Function Neural Networks (RBFNNs). Journal of Automation and Control. 2015; 3(1):10-17. doi: 10.12691/automation-3-1-2

Abstract

Radial Basis Function Neural Network (RBFNN) is considered as a good applicant for the prediction problems due to it’s fast convergence speed and rapid capacity of learning, therefore, has been applied successfully to nonlinear system identification. The traditional RBF networks have two primary problems. The first one is that the network performance is very likely to be affected by noise and outliers. The second problem is about the determination of the parameters of hidden nodes. In this paper, a novel method for robust nonlinear system identification is constructed to overcome the problems of traditional RBFNNs. This method based on using Support Vector Regression (SVR) approach as a robust procedure for determining the initial structure of RBF Neural Network. Using Genetic Algorithm (GA) for training SVR and select the best parameters as an initialization of RBFNNs. In the training stage an Annealing Robust Learning Algorithm (ARLA) has been used for make the networks robust against noise and outliers. The next step is the implementation of the proposed method on the Hydrocarbon Debutanizer unit for prediction of n-butane (C4) content. The performance of the proposed method (ARLA-RBFNNs) has been compared with the conventional RBF Neural Network approach. The simulation results show the superiority of ARLA-RBFNNs for process identification with uncertainty.

Keywords:
robust system identification RBF Neural Networks hydrocarbon debutanizer unit support vector regression genetic algorithm annealing robust learning algorithm

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  G. Apostolikas and S. Tzafestas, “On-line RBFNN based identification of rapidly time-varying nonlinear systems with optimal structure-adaptation,” Mathematics and Computers in Simulation, vol. 63, pp. 1-13, 2003.
 
[2]  L. Ljung, “Perspectives on system identification,” Annual Reviews in Control, vol. 34, pp. 1-12, 2010.
 
[3]  C.-N. Ko, “Identification of nonlinear systems with outliers using wavelet neural networks based on annealing dynamical learning algorithm,” Engineering Applications of Artificial Intelligence, vol. 25, pp. 533-543, 2012.
 
[4]  C.-C. Chuang, J.-T. Jeng, and P.-T. Lin, “Annealing robust radial basis function networks for function approximation with outliers,” Neurocomputing, vol. 56, pp. 123-139, 2004.
 
[5]  A. Sánchez and V. David, “Robustization of a learning method for RBF networks,” Neurocomputing, vol. 9, pp. 85-94, 1995.
 
[6]  C.-C. Lee, Y.-C. Chiang, C.-Y. Shih, and C.-L. Tsai, “Noisy time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques,” Expert Systems with Applications, vol. 36, pp. 4717-4724, 2009.
 
[7]  D. S. Broomhead and D. Lowe, “Radial basis functions, multi-variable functional interpolation and adaptive networks,” DTIC Document 1988.
 
[8]  A. D. Niros and G. E. Tsekouras, “A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach,” Fuzzy Sets and Systems, vol. 193, pp. 62-84, 2012.
 
[9]  K.-L. Du and M. N. Swamy, Neural networks in a softcomputing framework: Springer, 2006.
 
[10]  S. Chen, C. F. Cowan, and P. M. Grant, “Orthogonal least squares learning algorithm for radial basis function networks,” Neural Networks, IEEE Transactions on, vol. 2, pp. 302-309, 1991.
 
[11]  N. B. Karayiannis, “Gradient descent learning of radial basis neural networks,” in Neural Networks, 1997., International Conference on, 1997, pp. 1815-1820.
 
[12]  D. Simon, “Training radial basis neural networks with the extended Kalman filter,” Neurocomputing, vol. 48, pp. 455-475, 2002.
 
[13]  C.-C. Lee, P.-C. Chung, J.-R. Tsai, and C.-I. Chang, “Robust radial basis function neural networks,” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 29, pp. 674-685, 1999.
 
[14]  C.-C. Chuang, S.-F. Su, and C.-C. Hsiao, “The annealing robust backpropagation (ARBP) learning algorithm,” Neural Networks, IEEE Transactions on, vol. 11, pp. 1067-1077, 2000.
 
[15]  P. J. Huber, Robust statistics: Springer, 2011.
 
[16]  Y.-Y. Fu, C.-J. Wu, C.-N. Ko, and J.-T. Jeng, “Radial basis function networks with hybrid learning for system identification with outliers,” Applied Soft Computing, vol. 11, pp. 3083-3092, 2011.
 
[17]  V. N. Vapnik and V. Vapnik, Statistical learning theory vol. 2: Wiley New York, 1998.
 
[18]  A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and computing, vol. 14, pp. 199-222, 2004.
 
[19]  J. H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence: U Michigan Press, 1975.
 
[20]  A. Konak, D. W. Coit, and A. E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial,” Reliability Engineering & System Safety, vol. 91, pp. 992-1007, 2006.
 
[21]  B. Üstün, W. Melssen, M. Oudenhuijzen, and L. Buydens, “Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization,” Analytica Chimica Acta, vol. 544, pp. 292-305, 2005.
 
[22]  A. K. Jana, A. N. Samanta, and S. Ganguly, “Nonlinear state estimation and control of a refinery debutanizer column,” Computers & Chemical Engineering, vol. 33, pp. 1484-1490, 2009.
 
[23]  N. Mohamed Ramli, M. Hussain, B. Mohamed Jan, and B. Abdullah, “Composition Prediction of a Debutanizer Column using Equation Based Artificial Neural Network Model,” Neurocomputing, vol. 131, pp. 59-76, 2014.
 
[24]  M. Behnasr and H. Jazayeri-Rad, “Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm,” Journal of Natural Gas Science and Engineering, vol. 22, pp. 35-41, 2015.
 
[25]  S. Ferrer-Nadal, I. Yélamos-Ruiz, M. Graells, and L. Puigjaner, “On-line fault diagnosis support for real time evolution applied to multi-component distillation,” Computer Aided Chemical Engineering, vol. 20, pp. 961-966, 2005.