Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine

Mujahed Al Dhaifallah; K. S. Nisar

doi:10.1109/ICIT.2015.7125158

Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine

Mujahed Al Dhaifallah
, K. S. Nisar

Department of Control and Instrumentation Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Reverse osmosis (RO) desalination is the most common method for purifying brackish water. Due to its sensitivity to quality of the feed and plant operating conditions, RO desalination process needs an efficient and accurate control system to maintain operation at optimum conditions that ensures the least energy utilization and prevent scaling and fouling. Nonlinear systems identification techniques have been used widely to model many chemical processes. Recently, support vector machines (SVMs) and least squares support vector machines(LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In this Paper, an algorithm to identify the Wiener models using least square support vector machine regression is developed and used to identify a Hollow Fiber B-10 Permasep Permeator reverse osmosis (RO) desalination process. The obtained results showed 96 % matching of the model output and actual system output variances.

Original language	English
Title of host publication	2015 IEEE International Conference on Industrial Technology, ICIT 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	559-563
Number of pages	5
Edition	June
ISBN (Electronic)	9781479978007
DOIs	https://doi.org/10.1109/ICIT.2015.7125158
State	Published - 16 Jun 2015

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology
Number	June
Volume	2015-June

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/ICIT.2015.7125158

Cite this

Dhaifallah, M. A., & Nisar, K. S. (2015). Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine. In 2015 IEEE International Conference on Industrial Technology, ICIT 2015 (June ed., pp. 559-563). Article 7125158 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2015-June, No. June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2015.7125158

Dhaifallah, Mujahed Al ; Nisar, K. S. / Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine. 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 559-563 (Proceedings of the IEEE International Conference on Industrial Technology; June).

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title = "Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine",

abstract = "Reverse osmosis (RO) desalination is the most common method for purifying brackish water. Due to its sensitivity to quality of the feed and plant operating conditions, RO desalination process needs an efficient and accurate control system to maintain operation at optimum conditions that ensures the least energy utilization and prevent scaling and fouling. Nonlinear systems identification techniques have been used widely to model many chemical processes. Recently, support vector machines (SVMs) and least squares support vector machines(LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In this Paper, an algorithm to identify the Wiener models using least square support vector machine regression is developed and used to identify a Hollow Fiber B-10 Permasep Permeator reverse osmosis (RO) desalination process. The obtained results showed 96 \% matching of the model output and actual system output variances.",

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Dhaifallah, MA & Nisar, KS 2015, Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine. in 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June edn, 7125158, Proceedings of the IEEE International Conference on Industrial Technology, no. June, vol. 2015-June, Institute of Electrical and Electronics Engineers Inc., pp. 559-563. https://doi.org/10.1109/ICIT.2015.7125158

Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine. / Dhaifallah, Mujahed Al; Nisar, K. S.
2015 IEEE International Conference on Industrial Technology, ICIT 2015. June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 559-563 7125158 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2015-June, No. June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Reverse osmosis (RO) desalination is the most common method for purifying brackish water. Due to its sensitivity to quality of the feed and plant operating conditions, RO desalination process needs an efficient and accurate control system to maintain operation at optimum conditions that ensures the least energy utilization and prevent scaling and fouling. Nonlinear systems identification techniques have been used widely to model many chemical processes. Recently, support vector machines (SVMs) and least squares support vector machines(LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In this Paper, an algorithm to identify the Wiener models using least square support vector machine regression is developed and used to identify a Hollow Fiber B-10 Permasep Permeator reverse osmosis (RO) desalination process. The obtained results showed 96 % matching of the model output and actual system output variances.

AB - Reverse osmosis (RO) desalination is the most common method for purifying brackish water. Due to its sensitivity to quality of the feed and plant operating conditions, RO desalination process needs an efficient and accurate control system to maintain operation at optimum conditions that ensures the least energy utilization and prevent scaling and fouling. Nonlinear systems identification techniques have been used widely to model many chemical processes. Recently, support vector machines (SVMs) and least squares support vector machines(LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In this Paper, an algorithm to identify the Wiener models using least square support vector machine regression is developed and used to identify a Hollow Fiber B-10 Permasep Permeator reverse osmosis (RO) desalination process. The obtained results showed 96 % matching of the model output and actual system output variances.

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Dhaifallah MA, Nisar KS. Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine. In 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 559-563. 7125158. (Proceedings of the IEEE International Conference on Industrial Technology; June). doi: 10.1109/ICIT.2015.7125158

Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine

Abstract

Publication series

Bibliographical note

UN SDGs

ASJC Scopus subject areas

Access to Document

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Cite this