Farzin Piltan; Shahnaz TayebiHaghighi; Somayeh Jowkar; Hossein Rashidi Bod; Amirzubir Sahamijoo; Jeong-Seok Heo

A Novel Intelligent ARX-Laguerre Distillation Column Estimation Technique

Full Text (PDF, 748KB), PP.52-60

Views: 0 Downloads: 0

Author(s)

Farzin Piltan ¹ Shahnaz TayebiHaghighi ² Somayeh Jowkar ^2,3 Hossein Rashidi Bod ² Amirzubir Sahamijoo ² Jeong-Seok Heo ^1,*

1. School of IT Convergence, University of Ulsan, Ulsan, South Korea

2. Control and Robotic Lab, IRAN SSP Research and Development Center, Shiraz, Iran

3. Department of Information Technology, Faculty of Computer Engineering, Ateneo De Manila University, Manila, Philippines

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2019.04.05

Received: 10 Sep. 2018 / Revised: 28 Dec. 2018 / Accepted: 5 Jan. 2019 / Published: 8 Apr. 2019

Index Terms

Distillation column, system identification, ARX modeling, intelligent nonlinear-ARX-Laguerre, ARX-Laguerre modeling

Abstract

In practical applications, modeling of real systems with unknown parameters such as distillation columns are typically complex. To address issues with distillation column estimation, the system is identified by a proposed intelligent, auto-regressive, exogenous-Laguerre (AI-ARX-Laguerre) technique. In this method, an intelligent technique is introduced for data-driven identiﬁcation of the distillation column. The Laguerre method is used for the removal of input/output noise and decreases the system complexity. The fuzzy logic method is proposed to reduce the system’s estimation error and to accurately optimize the ARX-Laguerre parameters. The proposed method outperforms the ARX and ARX-Laguerre technique by achieving average estimation accuracy improvements of 16% and 9%, respectively.

Cite This Paper

Farzin Piltan, Shahnaz TayebiHaghighi, Somayeh Jowkar, Hossein Rashidi Bod, Amirzubir Sahamijoo, Jeong-Seok Heo, "A Novel Intelligent ARX-Laguerre Distillation Column Estimation Technique", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.4, pp.52-60, 2019. DOI:10.5815/ijisa.2019.04.05

Reference

[1]Bîldea, Costin S., Cătălin Pătruţ, Sten Bay Jørgensen, Jens Abildskov, and Anton A. Kiss. "Cyclic distillation technology–a mini‐review." Journal of Chemical Technology & Biotechnology 91, no. 5 (2016): 1215-1223.
[2]Yang, Ruey-Jen, Chan-Chiung Liu, Yao-Nan Wang, Hui-Hsiung Hou, and Lung-Ming Fu. "A comprehensive review of micro-distillation methods." Chemical Engineering Journal 313 (2017): 1509-1520.
[3]Acharya, Pratima, Geetanjali Dumpa, and Tarun Kumar Dan. "Modelling and control of distillation column." In Computation of Power, Energy Information and Commuincation (ICCPEIC), 2016 International Conference on, pp. 123-128. IEEE, 2016.
[4]Nowak, Robert D. "Nonlinear system identification." Circuits, Systems and Signal Processing 21, no. 1 (2002): 109-122.
[5]Distefano, G. P. "Mathematical modeling and numerical integration of multicomponent batch distillation equations." AIChE Journal 14, no. 1 (1968): 190-199.
[6]Najeh, Tawfik, et al. "New methods of Laguerre pole optimization for the ARX model expansion on Laguerre bases." ISA transactions 70 (2017): 93-103.
[7]Vidal, René, Yi Ma, and S. Shankar Sastry. "Hybrid System Identification." In Generalized Principal Component Analysis, pp. 431-451. Springer, New York, NY, 2016.
[8]Najeh, Tawfik, Chakib Ben Njima, Tarek Garna, and José Ragot. "Input fault detection and estimation using PI observer based on the ARX-Laguerre model." The International Journal of Advanced Manufacturing Technology 90, no. 5-8 (2017): 1317-1336.
[9]Bouzrara, Kais, Tarek Garna, José Ragot, and Hassani Messaoud. "Decomposition of an ARX model on Laguerre orthonormal bases." ISA transactions 51, no. 6 (2012): 848-860.
[10]Abdelwahed, Imen Ben, Abdelkader Mbarek, and Kais Bouzrara. "Adaptive MPC based on MIMO ARX-Laguerre model." ISA transactions 67 (2017): 330-347.
[11]Mbarek, Abdelkader, et al. "Laguerre-based modelling and predictive control of multi-input multi-output systems applied to a communicating two-tank system (CTTS)." Transactions of the Institute of Measurement and Control 39.5 (2017): 611-624.
[12]Leontaritis, I. J., and Stephen A. Billings. "Input-output parametric models for non-linear systems part I: deterministic non-linear systems." International journal of control 41, no. 2 (1985): 303-328.
[13]Xia, Xin, Jianzhong Zhou, Jian Xiao, and Han Xiao. "A novel identification method of Volterra series in rotor-bearing system for fault diagnosis." Mechanical systems and signal processing 66 (2016): 557-567.
[14]Harnischmacher, Gerrit, and Wolfgang Marquardt. "Nonlinear model predictive control of multivariable processes using block-structured models." Control Engineering Practice 15, no. 10 (2007): 1238-1256.
[15]Arnaiz-González, Álvar, Asier Fernández-Valdivielso, Andres Bustillo, and Luis Norberto López de Lacalle. "Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling." The International Journal of Advanced Manufacturing Technology 83, no. 5-8 (2016): 847-859.
[16]Peng, Z. K., Z. Q. Lang, C. Wolters, S. A. Billings, and K. Worden. "Feasibility study of structural damage detection using NARMAX modelling and Nonlinear Output Frequency Response Function based analysis." Mechanical Systems and Signal Processing 25, no. 3 (2011): 1045-1061.
[17]Liu, Xiong, Cheng Lu, Shi Liang, Ajit Godbole, and Yan Chen. "Vibration-induced aerodynamic loads on large horizontal axis wind turbine blades." Applied Energy 185 (2017): 1109-1119.
[18]Tang, Hao, Y. H. Liao, J. Y. Cao, and Hang Xie. "Fault diagnosis approach based on Volterra models." Mechanical Systems and Signal Processing 24, no. 4 (2010): 1099-1113.
[19]JIANG, Jing, Zhinong LI, and Gaosong TANG. "Fault Diagnosis Method Based on Volterra Kernel Identification for Rotor Crack." Machine Tool & Hydraulics 23 (2010): 040.
[20]Benabdelwahed, Imen, et al. "Nonlinear system modelling based on NARX model expansion on Laguerre orthonormal bases." IET Signal Processing 12.2 (2017): 228-241.
[21]Bachnas AA, Tth R, Ludlage JHA, Mesbah A A review on data-driven linear parameter-varying modeling approaches: a high-purity distillation column case study. J Process Control, 24 (2014): 272–285.
[22]Meidanshahi, Vida, et al. "Subspace model identification and model predictive control based cost analysis of a semicontinuous distillation process." Computers & Chemical Engineering 103 (2017): 39-57.
[23]Li X, Lu WF, Zhai L et al Remaining life prediction of cores based on data-driven and physical modeling methods. In: Nee AYC (ed) Handbook of manufacturing engineering and technology. Springer, London, (2015): 3239–3264.
[24]Mavrovouniotis M, Yang S Training neural networks with ant colony optimization algorithms for pattern classification. Soft Comput 19 (2015):1511–1522.
[25]Jaleel, E. Abdul, and K. Aparna. "Identification of realistic distillation column using hybrid particle swarm optimization and NARX based artificial neural network." Evolving Systems (2018): 1-18.
[26]Taqvi, Syed A., et al. "Fault detection in distillation column using NARX neural network." Neural Computing and Applications (2018): 1-17.
[27]Wei, Hua-Liang, and Stephen A. Billings. "Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information." International Journal of Modelling, Identification and Control 3, no. 4 (2008): 341-356.

International Journal of Intelligent Systems and Applications (IJISA)