TITLE:
Performance Prediction of a Reverse Osmosis Desalination System Using Machine Learning
AUTHORS:
Divas Karimanzira, Thomas Rauschenbach
KEYWORDS:
Reverse Osmosis, Membrane Fouling, Fouling Indices, Predicting Models, Machine Learning, Multivariate Temporal Convolutional Neural Networks
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.9 No.7,
July
21,
2021
ABSTRACT: One of the major challenges that membrane
manufacturers, commercial enterprises and the scientific community in the field
of membrane-based filtration or reverse osmosis (RO) desalination have to deal
with is system performance retardation due to membrane fouling. In this
respect, the prediction of fouling or system performance in membrane-based
systems is the key to determining the mid and long-term plant operating
conditions and costs. Despite major research efforts in the field, effective
methods for the estimation of fouling in RO desalination plants are still in
infancy, for example, most of the existing methods, neither consider the
characteristics of the membranes such as the spacer geometry, nor the
efficiency and the day to day chemical cleanings. Furthermore, most studies
focus on predicting a single fouling indicator, e.g., flux decline. Faced with
the limits of mathematical or numerical approach, in this paper, machine
learning methods based on Multivariate Temporal Convolutional Neural networks
(MTCN), which take into account the membrane characteristics, feed water
quality, RO operation data and management practice such as Cleaning In Place
(CIP) will be considered to predict membrane fouling using measurable multiple
indicators. The temporal convolution model offers one the capability to explore
the temporal dependencies among a remarkably long historical period and has
potential use for operational diagnostics, early warning and system optimal
control. Data collected from a Desalination RO plant will be used to demonstrate the capabilities of the prediction
system. The method achieves remarkable predictive accuracy (root mean square
error) of 0.023, 0.012 and 0.007 for the relative differential pressure and
permeates Total Dissolved solids (TDS) and the feed pressure,
respectively.