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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Authors
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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Abstract
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.
Keywords
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
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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