International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Post-Chlorination Process Control based on Flow Prediction by Time Series Neural Network in Water Treatment Plant

  • Lee, HoHyun (School of Electrical Engineering and Computer Science, Chungbuk National University) ;
  • Shin, GangWook (K-water Research Institute, Korea Water Resources Corporation) ;
  • Hong, SungTaek (K-water Research Institute, Korea Water Resources Corporation) ;
  • Choi, JongWoong (K-water Research Institute, Korea Water Resources Corporation) ;
  • Chun, MyungGeun (School of Electrical Engineering and Computer Science, Chungbuk National University)
  • Received : 2016.08.16
  • Accepted : 2016.09.13
  • Published : 2016.09.25
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Abstract

It is very important to maintain a constant chlorine concentration in the post chlorination process, which is the final step in the water treatment process (hereafter WTP) before servicing water to citizens. Even though a flow meter between the filtration basin and clear well must be installed for the post chlorination process, it is not easy to install owing to poor installation conditions. In such a case, a raw water flow meter has been used as an alternative and has led to dosage errors due to detention time. Therefore, the inlet flow to the clear well is estimated by a time series neural network for the plant without a measurement value, a new residual chlorine meter is installed in the inlet of the clear well to decrease the control period, and the proposed modeling and controller to analyze the chlorine concentration change in the well is a neuro fuzzy algorithm and cascade method. The proposed algorithm led to post chlorination and chlorination improvements of 1.75 times and 1.96 times respectively when it was applied to an operating WTP. As a result, a hygienically safer drinking water is supplied with preemptive response for the time delay and inherent characteristics of the disinfection process.

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