Deep learning-based production assists water quality warning system for reverse osmosis plants

K Udayakumar,N P Subiramaniyam

doi:10.2166/h2oj.2020.059

Abstract

Abstract Classifying water quality irregularities in reverse osmosis (RO) production plants requires suitable systems to provide intelligent warnings to the operators or supervisors who are engaged in executing corrective procedures applicable to production. The suggested deep learning methods are of utmost importance to identify at once variations in water quality irregularities in plants through competent classification methods, thereby enabling a reduction of burden for operators. In this paper, two types of LSTM-CNN based classification techniques are suggested to classify water quality features temporally into grades based on corrective actions that classify irregularity conditions of water quality on the basis of corrections. Distinct control methods are used for experiments to find water quality irregularities from variables, namely, pH, TDS, ORP, and EC, which aim to assist the production line. This proposed method enables automatic diagnosis and warning systems about water quality in RO plants. For classification, LSTM-CNN was trained with data recorded from eight plants of west and north parts of Chennai region. This research is meant to demonstrate particularly the top-level classification job for quality alerts. The features obtained from 4,096 time series array data using LSTM-CNNs achieved sensitivity to 97% and accuracy to 98%.

Highlights

Controlling the water quality continuously in reverse osmosis (RO) production plants and the necessary timely corrective action to improve or maintain the water quality during processes is a major real-time problem
The outcomes showed that long short-term memory (LSTM)-convolutional neural networks (CNN) could be considered an appropriate choice in order to classify water quality as a quality alert system for RO plant drinking water production
The proposed classifiers have shown some encouraging signs of providing intelligible warning about water quality irregularities which require to be rectified with precise treatment in the necessary circumstances

Summary

Introduction

Controlling the water quality continuously in reverse osmosis (RO) production plants and the necessary timely corrective action to improve or maintain the water quality during processes is a major real-time problem. Many production plants quite commonly use measuring instruments to monitor water quality physicochemical parameters and warn about adverse variations so as to perform instantly corrective actions in real time. Without a proper warning system, the remedial actions may get delayed. Water quality monitoring in real time requires more interpretation instead of just the values of physicochemical parameters like pH, electrical conductivity (EC), oxidation-reduction potential (ORP), and total dissolved solids (TDS)

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