Arabian Journal for Science and Engineering

, Volume 44, Issue 3, pp 2033–2044 | Cite as

Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring

  • Mohamed Djerioui
  • Mohamed Bouamar
  • Mohamed Ladjal
  • Azzedine ZerguineEmail author
Research Article - Electrical Engineering


A major problem in water treatment plants is the continuous difficulty faced in online measurement by means of dedicated measuring hardware and laboratory analysis of certain variables related to the composition of water. Actually, for several reasons, such as the high cost of some sensors, their number, the dedicated time to check out the sensors, cleaning operation, calibration routines and sensor replacement, make their proper operation hard to ensure high-quality composition of water. Furthermore, in water quality monitoring, there is a huge number of heterogeneous sensors which may be time-consuming in the measurement and processing stages. Nevertheless, soft sensor approach can provide an effective and economic way to solve this problem for any cases of sensor failure. This work presents a contribution to the study and development of a soft sensor used in water quality monitoring using chlorine. A comparative study between support vector machine (SVM) and extreme learning machine (ELM) techniques in terms of learning time and other parameters for regression and classification is presented. The main objective is to set up a system architecture based on a soft sensor for water quality in order to make an adapted decision to the control and monitoring of water quality issues. ELM is shown to be the most suitable technique to address the previously mentioned problems as it has better characteristics than those of the SVM technique. An example of application is provided to focus on the interest of using a chlorine soft sensor as it is accurate, efficient and less cost-effective tool.


Water quality monitoring Chlorine soft sensor Regression Classification Support vector machine Extreme learning machine 


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Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Mohamed Djerioui
    • 1
  • Mohamed Bouamar
    • 1
  • Mohamed Ladjal
    • 1
  • Azzedine Zerguine
    • 2
    Email author
  1. 1.LASS Laboratory, Department of Electronics, Faculty of TechnologyUniversity of M’silaM’SilaAlgeria
  2. 2.Electrical Engineering DepartmentKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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