Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration
Introduction
Membrane filtration is widely used for drinking water and wastewater treatment processes [1]. Four different types of membrane filtration techniques such as microfiltration, ultrafiltration, nanofiltration, and reverse osmosis are applied to remove salts or organic matter depending on the need [2]. However, fouling is regarded as one of the critical issues in the membrane filtration process because it accelerates the deterioration of water quality, thereby increasing the water production cost [3].
Numerous studies have been conducted to understand and control fouling on membrane surfaces by considering various parameters such as channel flow velocity, organic concentration, and ion strength [4,5]. Besides experimental studies, membrane models (i.e., mathematical or data-driven models) have been developed to obtain a better understanding of membrane fouling and flux decline so that the optimum maintenance conditions can be found [6]. Mathematical models, however, were not very successful in accurately simulating the membrane fouling behaviors due to its complexities (i.e., heterogeneity of feed water quality, unexpected bacterial growth/death, and complex flow pattern, etc.) [7].
Therefore, a data-driven model would be an alternative way to overcome these complexities and achieve higher predictive accuracy for membrane fouling and flux decline [8]. This is because the model is optimally trained to achieve a good-fit on the actual monitoring data using nonlinear and kernel functions [9]. In particular, artificial neural networks (ANNs) have been used as a modeling tool for membrane filtration systems [10]. One of the advantages of applying ANNs is that it does not require the solving of complex partial differential equations, and requires only available explanatory and target variables in a membrane process [11].
Deep neural networks (DNNs), which are considered as an advanced version of ANN, have been proposed as a promising state-of-the-art technique in many different fields, including speech recognition, image recognition, remote sensing, and climate forecasting [[12], [13], [14]]. The most distinguishable feature of DNN from ANN is that it automatically extracts representations from the given big data using multiple inter-connected layers [15]. Due to this feature, current environmental studies utilize DNN in order to optimize the performance of treatment plants [9,16]; however, the membrane science field still lacks the knowledge to apply DNN in studies such as flux decline and fouling behaviors.
Currently, optical coherence tomography (OCT), a non-destructive and high-speed scanning technology, is used as a fouling monitoring device [17,18]. OCT provides extremely fine-scale data on the membrane surface (<7 μm) and enables real-time direct observation of fouling on a membrane by providing numerous images of the membrane surface within a short time (120 frames/s). However, OCT methodology appears to be difficult to be utilized for continuous monitoring in the field conditions [19]. In addition, the monitoring ability has a limitation in improving operating performance since it can represent only current state of fouling, but does not provide a predictive information for the future. Predicting membrane fouling should be very useful for optimizing the operation of water treatment processes.
With this background, this study attempts to apply DNN to model membrane fouling for the first time. This study aims 1) to develop a DNN model for predicting organic fouling growth and flux decline of NF/RO membranes using high-resolution fouling layer images, and 2) to evaluate the performance of the model with OCT images. In addition, we produced a 3-dimensional video clip on organic fouling growth (available online). We hope this study will provide further insights into the membrane science with a DNN approach.
Section snippets
Experimental setup and data acquisition
The experimental data was taken from our previous study [19]. In this study, membrane fouling was performed using an NF membrane (NE 90, Toray, Japan) and an RO membrane (RE SHF, Toray) with 10 mg C/L of humic acid (Sigma-Aldrich, USA) and 10 mM of calcium ions (Sigma-Aldrich, USA). The effect of calcium ions on fouling layer growth was monitored by using a surface scanning device OCT (SD-OCT, Oz-tec, Korea). The feed water temperature was maintained around 20 °C. The scan area was a region of
The effect of deep learning on flux decline estimation
Fig. 3 compares the observed and simulated flux variations of each OCT image, depending on the membrane types. Model calibration was performed to estimate the model accuracy. In the calibration set, the R2 and RMSE values were 0.98 and 1.44 Lm−2h−1, respectively. The model was validated by comparing the simulated and observed data, except for the images used for training the model. In the validation set, the R2 and RMSE values were 0.98 and 1.52 Lm−2h−1, respectively. In the test set, the R2
Conclusions
The fouling model presented and discussed in this study provides a more precise and remarkable performance for estimating both flux decline and fouling layer growth, simultaneously. The conclusions we obtained are summarized as follows:
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Deep learning was able to utilize substantial amount of image data using convolutional neural network for developing fouling model that estimates flux decline and fouling layer growth.
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The fouling model successfully estimated the flux decline of two different
Acknowledgements
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Industrial Facilities & Infrastructure Research Program, funded by Korea Ministry of Environment (MOE) (RE201901123), and the National Research Foundation of Korea Grant funded by the Korean Government (MSIP) (No. NRF2015R1A5A7037825).
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Sanghun Park and Sang-Soo Baek are Co-first authors.