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Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach
American Journal of Chemical Engineering
Volume 5, Issue 6, November 2017, Pages: 122-129
Received: Jul. 23, 2017; Accepted: Aug. 21, 2017; Published: Nov. 5, 2017
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Babak Ghanaati, Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
Mehdi Shahbazian, Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
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An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a Takagi-Sugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.
Inferential Sensor, Distillation Column, Takagi-Sugeno-Kang Fuzzy System, Nearest Neighborhood Clustering, Particle Swarm Optimization, Wavelet, Principal Component Analysis
To cite this article
Babak Ghanaati, Mehdi Shahbazian, Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach, American Journal of Chemical Engineering. Vol. 5, No. 6, 2017, pp. 122-129. doi: 10.11648/j.ajche.20170506.11
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Fortuna, L., Graziani, S., Rizzo, A. and Xibilia, M. G., 2007. Soft sensors for monitoring and control of industrial processes. Springer Science & Business Media.
Lee, P. and Dexter, A., 2005. "A fuzzy sensor for measuring the mixed air temperature in air-handling units". Measurement, 37 (1), pp. 83-93.
Ibargüengoytia, P. H., Delgadillo, M. A., García, U. A. and Reyes, A., 2013. "Viscosity virtual sensor to control combustion in fossil fuel power plants". Engineering Applications of Artificial Intelligence, 26 (9), pp. 2153-2163.
Sun, K., Liu, J., Kang, J.-L., Jang, S.-S., Wong, D. S.-H. and Chen, D.-S., 2014. "Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote". Journal of Process Control, 24 (7), pp. 1068-1075.
Kadlec, P., Gabrys, B. and Strandt, S., 2009. "Data-driven soft sensors in the process industry". Computers & Chemical Engineering, 33 (4), pp. 795-814.
Gholami, A. R. and Shahbazian, M., 2015. "Soft sensor design based on fuzzy C-Means and RFN_SVR for a stripper column". Journal of Natural Gas Science and Engineering, 25, pp. 23-29.
Souza, F. A., Araujo, R. and Mendes, J., 2016. "Review of soft sensor methods for regression applications". Chemometrics and Intelligent Laboratory Systems, 152, pp. 69-79.
Rani, A., Singh, V. and Gupta, J., 2013. "Development of soft sensor for neural network based control of distillation column". ISA transactions, 52 (3), pp. 438-449.
Pan, T.-H., Wong, D. S.-H. and Jang, S.-S., 2010. "Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach". Industrial & Engineering Chemistry Research, 49 (10), pp. 4738-4747.
Kaneko, H., Arakawa, M. and Funatsu, K., 2009. "Development of a new soft sensor method using independent component analysis and partial least squares". AIChE Journal, 55 (1), pp. 87-98.
Jassar, S., Liao, Z. and Zhao, L., 2009. "Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems". Building and environment, 44 (8), pp. 1609-1616.
Zamprogna, E., Barolo, M. and Seborg, D. E., 2005. "Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis". Journal of process control, 15 (1), pp. 39-52.
Jain, P., Rahman, I. and Kulkarni, B., 2007. "Development of a soft sensor for a batch distillation column using support vector regression techniques". Chemical Engineering Research and Design, 85 (2), pp. 283-287.
Yu, J., 2012. "A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses". Computers & Chemical Engineering, 41, pp. 134-144.
Kalos, A., Kordon, A., Smits, G. and Werkmeister, S., 2003. "Hybrid model development methodology for industrial soft sensors". American Control Conference, 2003. Proceedings of the 2003, IEEE, pp. 5417-5422.
Takagi, T. and Sugeno, M., 1985. "Fuzzy identification of systems and its applications to modeling and control". IEEE transactions on systems, man, and cybernetics, (1), pp. 116-132.
Delgado, M. R., Nagai, E. Y. and de Arruda, L. V. R., 2009. "A neuro-coevolutionary genetic fuzzy system to design soft sensors". Soft computing, 13 (5), pp. 481-495.
Mendes, J., Souza, F., Araújo, R. and Gonçalves, N., 2012. "Genetic fuzzy system for data-driven soft sensors design". Applied Soft Computing, 12 (10), pp. 3237-3245.
Zheng, Y., Fang, H. and Wang, H. O., 2006. "Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36 (4), pp. 924-929.
Ma, X.-J. and Sun, Z.-Q., 2000. "Output tracking and regulation of nonlinear system based on Takagi-Sugeno fuzzy model". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 30 (1), pp. 47-59.
Rezaee, B. and Zarandi, M. F., 2010. "Data-driven fuzzy modeling for Takagi–Sugeno–Kang fuzzy system". Information Sciences, 180 (2), pp. 241-255.
Juang, C.-F. and Lo, C., 2008. "Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm". Fuzzy Sets and Systems, 159 (21), pp. 2910-2926.
Ren, Q., Balazinski, M., Baron, L. and Jemielniak, K., 2008. "Tool condition monitoring using the TSK fuzzy approach based on subtractive clustering method". International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, pp. 52-61.
Wang, N. and Yang, Y., 2009. "A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model". Expert Systems with Applications, 36 (10), pp. 12375-12382.
Wang, L.-X., 1999. A course in fuzzy systems. Prentice-Hall press, USA.
Zadeh, L. A., 1965. "Fuzzy sets". Information and control, 8 (3), pp. 338-353.
Jolliffe, I., 2002. Principal component analysis. Wiley Online Library.
Smith, L. I., 2002. "A tutorial on principal components analysis". Cornell University, USA, 51 (52), p. 65.
Shi, Y. and Eberhart, R. C., 1999. "Empirical study of particle swarm optimization". Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, IEEE, pp. 1945-1950.
Shahbazian, M., Jazayerirad, H. and Ebnali, M., 2014. "ANFIS based identification and control of distillation process". Journal of Automation and Control, 2 (2), pp. 49-56.
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