Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor

Bioresource Technology 293 (2019) 122103

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Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor

T

Yifeng Chena, Genying Yua, Ying Longa, Jiaheng Tenga, Xiujia Youa, Bao-Qiang Liaob, ⁎ Hongjun Lina, a b

College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China Department of Chemical Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, Ontario P7B 5E1, Canada

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Keywords: Membrane bioreactor Membrane fouling Interfacial energy Artificial neural network Wastewater treatment

Efficient quantification of interfacial energy related with membrane fouling represents the primary interest in membrane bioreactors (MBRs) as interfacial energy determines foulant layer formation. In this study, radial basis function (RBF) artificial neural networks (ANNs) with five related factors as input variables were applied to quantify interfacial energy with randomly rough membrane surface. It was found that, RBF ANNs could well capture the complex non-linear relationships between the related factors and interfacial energy. RBF ANN quantification showed high regression coefficient and accuracy, suggesting its high capacity to quantify interfacial energy. Compared to at least one-week time consumption of the advanced extensive Derjaguin-LandauVerwey-Overbeek (XDLVO) approach, quantification by RBF ANNs only took several seconds for a same case, indicating the high efficiency of RBF ANNs. Moreover, the abilities of RBF ANNs can be further improved. The robust RBF ANNs proposed paved a new way to study membrane fouling in MBRs.

1. Introduction Membrane bioreactor (MBR) technology has been more and more used in water treatment owning to its higher treatment efficiency, energy saving and smaller footprint over conventional activated sludge process (Chen et al., 2016; Drews, 2010; Lin et al., 2012; Wang et al., 2014b). However, the problem of membrane fouling has always been the primary issue in application of membrane-based technology (Aslam et al., 2017; Drews, 2010; Yu et al., 2019; Zhang et al., 2018). It is generally believed that membrane fouling in MBRs chiefly stems from

⁎

foulant adhesion/deposition to form a foulant layer on surface of membrane (Chen et al., 2019; Fortunato et al., 2019; Li et al., 2019). Therefore, better understanding of foulant adhesion process is very important for membrane fouling control. In MBRs, two forces were considered to be critically involved into foulant adhesion process: hydrodynamic force provided by stirring, aeration or filtration, and short-range interfacial force/energy provided by thermodynamic interactions between foulants and membrane (Hoek and Agarwal, 2006; Teng et al., 2019; Wang et al., 2013). The former force will carry foulants nearby membrane surface, and the latter

Corresponding author. E-mail address: [email protected] (H. Lin).

https://doi.org/10.1016/j.biortech.2019.122103 Received 4 August 2019; Received in revised form 30 August 2019; Accepted 2 September 2019 Available online 04 September 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.

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2. Material and methods

determines fastening foulants on membrane surface (Hoek and Agarwal, 2006; Teng et al., 2019; Wang et al., 2013). Considering the determining roles of short-range interfacial force in foulant adhesion process, many efforts have been devoted into quantification of the short-range interfacial force in various complicated interaction scenarios (Bhattacharjee et al., 1998; Chen et al., 2017; Hoek and Agarwal, 2006; Li et al., 2019). As a notable headway, triangulation technique and surface element integration (SEI) method were combined with extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory, leading to development of the so-called “advanced XDLVO approach” in recent years (Teng et al., 2018; Teng et al., 2019). This approach treated any rough surface as a sum of infinite differential planar elements, and thus, enabled to quantify the interfacial energy between any two entities with randomly rough surfaces, provided that computation was made. While this approach provides a good opportunity to better understand membrane fouling, it faces grave problem of massive computation complexity (Teng et al., 2018; Teng et al., 2019). For instance, it will take several days to compute the interfacial energy between a foulant and randomly rough membrane surface in full range of separation distance. Moreover, if effects of other parameters, such as sludge properties, the influent water quality, temperature and pH, are considered, the computation complexity and time consumption will be even higher, significantly reducing the feasibility of the advanced XDLVO approach in real application. Therefore, alternative methods which can overcome this drawback are highly desirable. The extremely high computation complexity and time consumption of the advanced XDLVO approach in real application may be not surprise, considering the complex non-linear relationships of interfacial energy with various factors and infinite differential planar elements involved in the computation. Artificial neural networks (ANNs) have been generally deemed as standard non-linear estimators (Ghritlahre and Prasad, 2018; Singh and Gupta, 2012). The distinct advantages of ANNs include the capability to approximate any functions to any accuracy, learning ability, parallel processing ability, and noise resistance ability (Iliyas et al., 2013; You and Nikolaou, 1993). Inspired by this fact, it is envisaged that ANNs may act as an alternative method to quantify interfacial energy in various complicated scenarios. Among various ANNs models, radial basis function (RBF) ANN possesses advantages of simple network structure, fast convergence speed and high ability to approach arbitrary nonlinear function(Jang and Sun, 1993), and has been particularly used in MBR process control, membrane performance and fouling prediction (Al-Abri and Hilal, 2008; Chen and Kim, 2006; Mirbagheri et al., 2015). While interfacial energy is a primary index of foulant adhesion and membrane fouling, there is quite limited study regarding application of ANNs in quantification of interfacial energy in the literature. Only one related study was available, which verified the feasibility of RBF ANN in quantifying interfacial energy with rough surface (Zhao et al., 2019b). However, in that study, only one affecting factor (namely separation distance) was included as an input variable in the quantification, and the time consumption was still relatively high though significantly reduced. Meanwhile, the prediction accuracy should be improved. These drawbacks indicate the necessity to optimize this method, so that it can be efficiently used in real application. Therefore, the purpose of this study is to establish high efficient RBF ANNs for quantification of interfacial energy related with membrane fouling in a MBR. More parameters including contact angles of three probe liquids, zeta potential and separation distance were used as inputs of the RBF ANNs. The training data set and verifying data set were obtained from a case study where interfacial energy data between sludge foulant and membrane in a pilot-scale MBR were calculated by the advanced XDLVO approach. Comparison with the advanced XDLVO approach was conducted to assess the performance of the proposed RBF ANNs. This study will provide a more universal and efficient method for quantifying interfacial energy associated with membrane fouling.

2.1. Advanced XDLVO approach to quantify interfacial energy XDLVO theory proposed by Van Oss (van Oss, 1993) offered series methods to calculate the interfacial force/energy within two ideally planar surfaces in aqueous solution. It is accepted that, three individual components: Lifshitz-van der Waals (LW), acid-base (AB) and electrostatic (EL) interactions, make up the interfacial force/energy. The specific forms of the individual components between two ideally planar surfaces are quantified by the following equations, respectively (Hoek and Agarwal, 2006; van Oss, 1993, 1995):

ΔG LW (h) = ΔG hLW 0

h 02 h2

(1)

h0 − h ⎞ λ ⎠

(2)

2 2 1 ⎞ ⎛ ζm + ζ f ΔG EL (h) = κζm ζ f εr ε0 ⎜ (1 − coth κh) + 2ζm ζ f sinh κh ⎟ ⎠ ⎝

(3)

ΔG AB (h) = ΔG hAB exp ⎛ 0 ⎝

where ΔG LW (h) , ΔG AB (h) and ΔG EL (h) are the individual LW, AB and EL interfacial energy per unit area, respectively. Variable h is the separation distance between the two infinite planar surfaces. Constant h0 means the minimum separation distance that the two planar surfaces can approach to each other, and is usually assumed to be 0.158 nm for the normal conditions (Meinders et al., 1995). Constant λ (=0.6 nm) means the decay length of AB interaction in aqueous solution. Variables ζm and ζf represent the zeta potential of the membrane and foulant, respectively. Variables εr and ε0 mean dielectric constants in water and vacuum conditions, respectively. Variable κ means the reciprocal of , ΔG hAB and ΔG hEL Debye length, as a function of ionic strength. ΔG hLW 0 0 0 mean the individual unit area interfacial energy at h0, which are defined as follows:

ΔG hLW = −2( γmLW − 0

γwLW )( γ fLW −

γwLW )

(4)

ΔG hAB 0 = 2[ γw+ ( γ f− + −

ΔG hEL = 0

γm− −

γw− ) +

γw− ( γf+ +

γm+ −

γw+ ) −

γ f−γm+

γf+γm− ]

(5)

2ξf ξm ε0 εr κ 2 ⎡ ⎤ (ξ f + ξm2 ) ⎢1 − coth(κh 0) + 2 csch(κh 0) ⎥ 2 ξ f + ξm2 ⎣ ⎦

(6)

where subscripts of f, w and m represent foulant, water and membrane, respectively. Variables of γ+, γ− and γLW mean the surface tension of electron acceptor, electron donor and LW, respectively. The values of these surface tension components for a solid substance can be derived by solving a group of Young's equations (Eq. (7)) (Adam, 1957; Brant and Childress, 2004), provided that contact angles (ϕ ) of three probe liquids (ultrapure water, glycerol and diiodomethane in this study) are measured.

(1 + cos ϕ) Tol γl = 2

γlLW γSLW +

γl− γS+ +

γl+ γS−

(7)

where subscripts of l and s refer to liquid (water) and solid, respectively. It was suggested that, rough surface topography like membrane surface could be deemed as sum of infinite differential planar elements (Bhattacharjee and Elimelech, 1997). Inspired by this idea, the individual interfacial energy (U(D)) between a particle and a rough surface can be viewed as summation of differential individual interfacial energy per unit area (ΔG(h)). Such a treatment is generally defined as surface element integration (SEI) method given as: 2

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U (h) =

∬ ΔG (h) dA A

y = rsinθ (8)

Therefore, h can be expressed in the form of variables r and θ. Substitution of Eqs. (1)–(6) and (9)–(13) into Eq. (8) results in a set of equations (Eqs. (14)–(16) to theoretically quantify the individual interfacial energy between a sludge floc and rough membrane surface.

where h and dA represent the vertical distance between two differential planar elements and the projected area of the differential arc on the sphere surface, respectively. For the sphere surface, dA can be further expressed by the following equation (Bhattacharjee et al., 1998). (9)

dA = rdθdr

where r and dr are the radius of circular ring and differential ring, respectively. dθ is the differential angle of the differential arc on sphere surface. In theory, if the topography of two interacting surfaces can be integrated into Eqs. (8) and (9), the individual interfacial energy for the concerned interaction scenario can be quantified. Membranes used in MBRs are randomly rough in nature. Obviously, SEI method requires continuous surfaces. However, atomic force microscopy (AFM) analysis only provides a position matrix of surface topography. Triangulation technique was therefore adopted to reconstruct a continuous membrane surface based on AFM analytical data (Teng et al., 2018). This technique treats a continuous surface as summation of lots of adjacent triangular faces. The height (z) of any point P (x, y, z) in a triangular face can be derived by following formula (Teng et al., 2018), since three vector coordinates P1 (x1, y1, z1), P2 (x2, y1, z2) and P3 (x1, y3, z3) of the triangular face are given by AFM analysis: (10)

where n·P1 P is the vector between points P1 and P. Variable of n (P1, P2, P3) is the normal vector vertical to the triangular facet. Fig. 1 shows the interaction scenario regarding a sludge floc on the top of a rough membrane surface. Obviously, the vertical distance h for any differential area on floc surface (corresponding to point P on the membrane surface) depends on its location. By exploring the spatial relationships presented in Fig. 1b, the following equation can be derived to describe such a dependence.

R2 − r 2 − z + zmax

(11)

where z and zmax are the height of the point P and the maximum height value of the membrane surface, respectively. For point P on the membrane surface, the x and y coordinates should satisfy both of surface topographies of the membrane and the sludge floc. For convenience of calculation, the Cartesian coordinates (x, y) are transformed into circular coordinates (r, θ) by Eqs. (12) and (13).

x = rcosθ

∫0 ∫0

R

AB Ufwm =

∫0 ∫0

2π

R

EL Ufwm =

∫0 ∫0

2π

R

ΔG LW ( R + D −

R2 − r 2 − z + zmax ) rdrdθ

(14)

ΔG AB ( R + D −

R2 − r 2 −z + zmax ) rdrdθ

(15)

ΔG EL ( R + D −

R2 − r 2 − z + zmax ) rdrdθ

(16)

ANN, which is deemed as the most important embranchment of computational intelligence paradigms, is an information processing network simulating the network of neurons that make up human brain (Iliyas et al., 2013; Shetty and Chellam, 2003). Besides advantages of self-learning, fault tolerance and distributed memory, a distinct advantage of ANN is the inherent ability to incorporate non-linear relationships into the model, which avoid the complexity of conceptual models (Moradkhani et al., 2004; Zhao et al., 2019b). The RBF ANN is an efficient feedforward neural network (Iliyas et al., 2013), which simulates the neural network structure of local adjustment and mutual coverage of the receiving domain (or receptive field) in the human brain. Therefore, the RBF ANN is a local approximation network that has been shown to approximate arbitrary continuous functions with arbitrary precision (Huang et al., 2006). As shown in Fig. 2, there are three layers including an input layer, a hidden layer, and an output layer making up a RBF ANN where the number of neurons in hidden layer can be adjusted according to actual needs (Sreekanth et al., 1999). This allows the input vector (not through the weighted connection) to be mapped directly to the hidden layer, showing non-linear property. The distinct advantages of RBF ANN over other ANNs include universal approximation ability, no local minimum problem and faster learning algorithm (Ghritlahre and Prasad, 2018). Gaussian functions (Dreiseitl and Ohno-Machado, 2002) are often used as activation functions for RBF ANN:

⇀

h=R+D−

2π

LW Ufwm =

2.2. RBF ANN model

⇀

n·P1 P = n (P1, P2, P3)·(x1 − x , y1 − y, z1 − z )

(13)

1 Ri (x ) = exp ⎛⎜ ∥x − ci ∥2 ⎞⎟ (i = 1, 2, …, p) 2 ⎝ 2σi ⎠

(17)

where x is the m-dimensional input vector, ci is the center of the i-th basis function, σi is the i-th perceptual variable, and p is the number of

(12)

Fig. 1. Schematic diagram of the interaction scenario regarding a sludge floc on the top of the membrane surface: (a) three-dimensional view, (b) side view. 3

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Fig. 2. Architecture of a typical RBF network model.

2.3. Analytical methods

hidden-layer nodes (sensing unit), ∥x − ci∥ is the norm of vector x-ci. The mapping from the hidden layer to the output layer is linear, which can be expressed as:

In order to quantify the interfacial energy, characterization of the surface topography of the membrane, the surface properties of the membrane and foulants is necessary. MBR systems primarily consist of the sludge suspension and membrane units. The sludge suspension and polyvinylidene fluoride (PVDF) flat sheet membrane used in the MBR system were used as samples for characterization. Surface morphology of the membrane was characterized by AFM instrument (NT-MDT). The derived AFM images were analyzed by Nanoscope Analysis 1.7. Surface zeta potential of the membrane was measured by the Surpass 3 of Anton Paar. Whereas, the surface zeta potential of sludge foulants was determined by the Malvern Zetasizer Nano ZS instrument. Measurements of the contact angle on the samples were carried out according to dripstop method. In order to obtain the individual surface tension components of a solid surface, three typical liquids (ultrapure water, glycerol and diiodomethane) were used as probe liquids for contact angle measurements. The complex double integrals involved in the SEI method can be solved based on the composite Simpson’s rule (Cai et al., 2017; Zhao et al., 2016), and the calculation was executed by a selfprogramming program running in MATLAB 2017b.

p

yk (x ) =

∑ wik Ri (x ) (k = 1,

2, …, q) (18)

i=1

where wik is the output weight of the RBF ANN; q is the number of output nodes (variables). The training process necessary for RBF ANN application is mainly divided into two steps. The first step is to select centers among the data used for training, or to cluster the training data to construct centers. The second step is the weight calculation between the hidden layer and the output layer. Self-organized center selection is the most widely used learning algorithms for RBF ANNs where orthogonal least square (OLS) is the most popular self-organizing center selection strategy (Chen et al., 1991; Fernando and Jayawardena, 1998). This strategy uses the gram-Schmidt algorithm to select and update the center, and uses adaptive gradient descent to adapt the weights (Iliyas et al., 2013). The value of the network parameter can be obtained when the original function is minimized. Q

min J =

∑ (|Ti − ti |2 )

3. Results and discussion (19)

i=1

3.1. A case study of interfacial energy quantification

where Ti and ti are the output values and desire target values of the RBF ANN, respectively. Q is the number of training samples. The accuracy of the RBF ANN predictions is analyzed by relative error (R) as follows:

R = (Yi − yi )/ yi

The three-dimensional surface topography of the PVDF membrane was characterized by AFM, and the results are shown in Supplementary information. It is clear that the natural PVDF membrane surface is randomly rough. Assisted by triangulation technique (Teng et al., 2018), a continuous membrane surface can be constructed based on the height matrix (256 × 256) of the PVDF membrane surface derived from Nanoscope software. The reconstructed membrane surface topography, which is highly similar to the membrane surface morphology characterized by AFM, is also shown in Supplementary information. The surface properties of the PVDF membranes and foulants used for

(20)

where Yi and yi represent the i-th network predicted output value and the target value calculated by the advanced XDLVO method, respectively. In addition, a regression analysis between the network output values and the desire target values and N10 (means percentage of predictions with R of less than 10%) are also used to evaluate the prediction performance of the RBF ANN.

Table 1 Experimentally measured surface properties in terms of contact angle, surface tension and zeta potential of the PVDF membrane and the sludge foulant (5.0 μm radius) used for interfacial energy quantification. Materials

PVDF membrane sludge foulant

Surface tensions (mJ m−2)

Contact angle (°)

Zeta potential (mV)

Ultrapure water

Glycerol

Diiodomethane

γLW

γ+

γ−

γAB

γTol

60.56 ± 0.83 70.49 ± 0.64

56.26 ± 0.96 64.50 ± 0.36

21.35 ± 1.28 33.18 ± 0.52

47.37 46.66

0.03 0.01

17.75 11.98

1.47 0.70

48.84 43.56

4

−23.89 ± 0.21 −25.65 ± 0.74

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Fig. 3. Profiles of interfacial energies between a sludge floc (5.0 μm radius) and the PVDF membrane surface quantified by the advanced XDLVO approach.

quantification of the interfacial energy are listed in Table 1. Based on these input data, the individual interfacial energy profiles with separation distance can be figured out by the advanced XDLVO approach, and the results are shown in Fig. 3. It can be seen that, all the three individual interfacial energies monotonously decrease with separation distance increases from 0.158 nm to 12 nm. The total interfacial energy is negligible when separation distance larger than 7 nm. EL force is continuously repulsive because both of the membrane surface and foulant are negatively charged. In contrast, the AB and LW forces are continuously attractive. It can be seen that, the maximum total interfacial energy is at high level of −1200000 kT in this study, which is much higher than the one (-450 kT) reported by Hoek and Agarwal (2006). This difference is caused by the different size particles used for energy calculation (5 μm in this study v.s. 0.1 μm in Hoek and Agarwal (2006)). Considering that interaction energy is proportionate to particle surface area, these two studies have same level of maximum total interfacial energy. Meanwhile, among the three interactions, AB interaction is the main contributor to the total interaction, indicating that AB interaction plays a decisive role in the foulant adhesion process. These results are consistent with previous studies (Hoek and Agarwal, 2006; Wang et al., 2013; Zhao et al., 2017), indicating the feasibility of the advanced XDLVO approach. However, the process for quantification of individual interfacial energy profiles was rather time-consuming. Although a high-end computer simulation workstation (HP Z240 SFF Workstation) was used for quantification, it took more than one week to figure out these profiles for a given interaction scenario. This is not surprise considering randomly rough surface and the non-linear relationships between surface properties and interfacial energy. Whereas, the sludge properties in an MBR system may significantly change daily, corresponding to countless interaction scenarios. This contrast suggests that the advanced XDLVO approach neither reflect the foulant adhesion process in time, nor be used for on-line system monitoring membrane fouling development, greatly limiting the application of this method. Therefore, it is quite desirable to develop an efficient alternative method. Fig. 4. Comparisons of RBF ANN’ outputs with the calculated interfacial energies by the advanced XDLVO approach: (a) AB interfacial energy, (b) LW interfacial energy and (c) EL interfacial energy.

3.2. Establishment of RBF ANN for interfacial energy quantification Establishment of a robust and reliable ANN model requires including as many affecting factors as possible (as model inputs) and a large number of samples for model training and testing. Interfacial energy quantification depends on characterization of three probe liquid contact angles and zeta potential of both membrane and sludge foulants, and separation distance. For an MBR system, the membrane surface properties are relatively stable, while sludge foulant properties will be apt to change. Therefore, three probe liquid contact angles and zeta potential of sludge foulants and separation distance were

considered as affecting factors (five factors) in this study. Among them, three probe liquid contact angles are interrelated, and not independent factors. According to XDLVO theory, three probe liquid contact angles can be solved to surface tension properties and further converted into two independent factors of AB conversion coefficient (kAB) and LW conversion coefficient (kLW). Meanwhile, zeta potential can be converted into EL conversion coefficient (kEL). Accordingly, two 5

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Fig. 5. (a) The relative errors of RBF ANN quantification, and (b) convergence plot of RBF models (the postfixes of 1, 2 and 3 refer to AB, LW and EL interfacial energies, respectively).

were used for training and verification, respectively. For EL interfacial energy, 117 groups training samples were calculated where 65 groups were used for verification. The original data are provided in the support information to ensure that this work can be repeated by other researchers. The function of newrb in Matlab 2018b was used to yield RBF networks for quantification of three individual interfacial energy. RBF networks with optimum parameter values (mainly spread value and maximum neurons number) can be obtained by minimizing the mean square error of the training samples. Fig. 4 shows comparisons between the network outputs and the calculated individual interfacial energies by the advanced XDLVO approach. It can be seen that, high regression coefficients (around 1.0) and high N10 values (98%, 100% and 100%) are obtained for all the 52 testing data sets in the three individual interfacial energy quantification. These results highly demonstrate the high capacity of the trained RBF networks to involve various factors and treat the complicated non-linear relationships between various factors and interfacial energies.

independent factors (conversion coefficient and separation distance) were actually included as final inputs of an RBF ANN for individual interfacial energy quantification. Data of five factors at different levels and the resulted interfacial energy data (calculated by the advanced XDLVO approach) were used as sample database for RBF ANN model training and testing. Namely, foulant contact angles of ultrapure water, glycerol and diiodomethane were set at intervals of 5° in the range of 50–80°, 40–75° and 10–30°, respectively, and foulant zeta potential and separation distance were set to be in the range of −10 to −50 mV at an interval of −5 mV and in the range of 0.2–12.2 nm at 1.0 nm intervals, respectively. A series of suitable data was selected as a training sample by orthogonal design. In order to make the training network better generalized, some experimentally measured data were also included for the training and verification of network. Different sample number was set for individual interfacial energy quantification due to different factor levels set. For AB and LW interfacial energies, 244 groups of data were obtained by the advanced XDLVO method. Among them, 166 groups and 78 groups 6

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profiles of interfacial energies between a sludge floc and the rough membrane surface quantified by the established RBF ANNs. It is clear that, the profiles are almost identical to those in Fig. 3 although the time consumption of RBF ANN quantification is less than one ten thousandth of that of the advanced XDLVO approach. Fig. 6b further analyzes the relative errors for all the data samples used in the profiles. It can be seen that, almost all of the errors are close to zero except two which are only 2.99% for LW interfacial energy at 0.2 nm and 2.23% for AB interfacial energy at separation distance of 11.2 nm, respectively. The high consistency of interfacial energies quantified by the established RBF networks with those quantified by the advanced XDLVO method, together with the low relative errors, suggest the reliability of RBF ANN approach. Moreover, the quick response nature of RBF ANNs makes the competent enough to serve as on-line system monitoring membrane fouling development. Moreover, the efficiency of RBF ANNs can be further improved by adjusting their topography and further training, showing the robustness of such a method. The robustness of RBF ANNs in quantification of interfacial energy paved a new way to study membrane fouling in MBRs. Considering the great interest of membrane fouling research (Shen et al., 2019; Wang et al., 2014a; Zhang et al., 2017; Zhang et al., 2013; Zhao et al., 2019a), it is argued that the findings obtained by this study are significant. 4. Conclusions This study demonstrated establishment, optimization and application of RBF ANNs in quantification of interfacial energy with randomly rough membrane surface in an MBR. RBF ANNs could well capture the complex non-linear relationships between the related factors and interfacial energy related membrane fouling. Comparison of RBF ANNs with the advanced XDLVO approach showed high regression coefficient, high accuracy and quick response of RBF ANNs for a same case study. The abilities of RBF ANNs can be further improved by adjusting the network topography and further training. The robust RBF ANNs paved a new way to study membrane fouling in MBRs.

Fig. 6. (a) Profiles of interfacial energies between a sludge floc and the rough membrane surface, and (b) the relative errors of the interfacial energies quantified by the established RBF ANNs.

Fig. 5a shows the relative errors of the three individual interfacial energies quantified by the trained RBF ANNs. All the samples except several ones with separation distance larger than 11 nm possess extremely low relative errors. The phenomenon of existing several large relative errors is reasonable because separation distance larger than 11 nm corresponds to rather weak interfacial energies (almost to zero) which even cannot be exactly quantified by the advanced XDLVO approach, let alone the trained RBF ANNs. The overall extremely low relative errors suggest the superior ability of RBF network to quantify interfacial energy related with membrane fouling. Fig. 5b shows the convergence plot of RBF networks trained in this study. It can be seen that, the lowest values of mean square error (MSE) are reached when 160 and 110 neurons (equal to 160 epochs) are included in the RBF networks for AB and EL interfacial energies, respectively. RBF network for LW interfacial energy is even more efficient as quantification goal can be achieved when 8 neurons are included (Fig. 5b2). The process of RBF network generation and quantification with the required epochs could be accomplished in seconds without obviously reduced quantification accuracy. In contrast, the advanced XDLVO approach required at least one week to figure out the profiles of interfacial energies for a given interaction scenario within separation distance of 12 nm. It can be concluded that, RBF network possess extremely higher efficiency over the advanced XDLVO approach for quantification of interfacial energy related with membrane fouling.

Acknowledgement This study was financially supported by National Natural Science Foundation of China (Nos. 51978628, 51578509). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.122103. References Adam, N.K., 1957. Use of the Term ‘Young's Equation’ for Contact Angles. Nature 180, 809. Al-Abri, M., Hilal, N., 2008. Artificial neural network simulation of combined humic substance coagulation and membrane filtration. Chem. Eng. J. 141 (1), 27–34. Aslam, M., Charfi, A., Lesage, G., Heran, M., Kim, J., 2017. Membrane bioreactors for wastewater treatment: a review of mechanical cleaning by scouring agents to control membrane fouling. Chem. Eng. J. 307, 897–913. Bhattacharjee, S., Elimelech, M., 1997. Surface element integration: a novel technique for evaluation of DLVO interaction between a particle and a flat plate. J. Colloid Interf. Sci. 193 (2), 273–285. Bhattacharjee, S., Ko, C.-H., Elimelech, M., 1998. DLVO interaction between rough surfaces. Langmuir 14 (12), 3365–3375. Brant, J.A., Childress, A.E., 2004. Colloidal adhesion to hydrophilic membrane surfaces. J. Membr. Sci. 241 (2), 235–248. Cai, X., Zhang, M., Yang, L., Lin, H., Wu, X., He, Y., Shen, L., 2017. Quantification of interfacial interactions between a rough sludge floc and membrane surface in a membrane bioreactor. J. Colloid Interf. Sci. 490, 710–718. Chen, H., Kim, A.S., 2006. Prediction of permeate flux decline in crossflow membrane filtration of colloidal suspension: a radial basis function neural network approach. Desalination 192 (1), 415–428. Chen, J., Lin, H., Shen, L., He, Y., Zhang, M., Liao, B.-Q., 2017. Realization of quantifying interfacial interactions between a randomly rough membrane surface and a foulant particle. Bioresour. Technol. 226, 220–228.

3.3. Application of RBF ANN in interfacial energy quantification To further verify the feasibility of the established RBF ANN in quantifying the interfacial energy between typical foulants in MBRs and membrane surface, the interaction scenario provided in Section 3.1 was used as a case study of RBF ANN quantification. Fig. 6a shows the 7

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Singh, K.P., Gupta, S., 2012. Artificial intelligence based modeling for predicting the disinfection by-products in water. Chemometr. Intell. Lab. Syst. 114, 122–131. Sreekanth, S., Ramaswamy, H.S., Sablani, S.S., Prasher, S.O., 1999. A neural network approach for evaluation of surface heat transfer coefficient. J. Food Process. Pres. 23 (4), 329–348. Teng, J., Shen, L., He, Y., Liao, B.-Q., Wu, G., Lin, H., 2018. Novel insights into membrane fouling in a membrane bioreactor: elucidating interfacial interactions with real membrane surface. Chemosphere 210, 769–778. Teng, J., Zhang, M., Leung, K.-T., Chen, J., Hong, H., Lin, H., Liao, B.-Q., 2019. A unified thermodynamic mechanism underlying fouling behaviors of soluble microbial products (SMPs) in a membrane bioreactor. Water Res. 149, 477–487. van Oss, C.J., 1993. Acid—base interfacial interactions in aqueous media. Colloids Surf. A 78, 1–49. van Oss, C.J., 1995. Hydrophobicity of biosurfaces — origin, quantitative determination and interaction energies. Colloids Surf. B 5 (3), 91–110. Wang, F., Chen, J., Hong, H., Wang, A., Lin, H., 2014a. Pollutant removal and membrane fouling in an anaerobic submerged membrane bioreactor for real sewage treatment. Water Sci. Technol. 69 (8), 1712–1719. Wang, Q., Wang, Z., Zhu, C., Mei, X., Wu, Z., 2013. Assessment of SMP fouling by foulantmembrane interaction energy analysis. J. Membr. Sci. 446, 154–163. Wang, Z., Ma, J., Tang, C.Y., Kimura, K., Wang, Q., Han, X., 2014b. Membrane cleaning in membrane bioreactors: a review. J. Membr. Sci. 468, 276–307. You, Y., Nikolaou, M., 1993. Dynamic process modeling with recurrent neural networks. AIChE J. 39 (10), 1654–1667. Yu, W., Liu, Y., Xu, Y., Li, R., Chen, J., Liao, B.-Q., Shen, L., Lin, H., 2019. A conductive PVDF-Ni membrane with superior rejection, permeance and antifouling ability via electric assisted in-situ aeration for dye separation. J. Membr. Sci. 581, 401–412. Zhang, M., Hong, H., Lin, H., Shen, L., Yu, H., Ma, G., Chen, J., Liao, B.-Q., 2018. Mechanistic insights into alginate fouling caused by calcium ions based on terahertz time-domain spectra analyses and DFT calculations. Water Res. 129, 337–346. Zhang, M., Lin, H., Shen, L., Liao, B.-Q., Wu, X., Li, R., 2017. Effect of calcium ions on fouling properties of alginate solution and its mechanisms. J. Membr. Sci. 525, 320–329. Zhang, M., Peng, W., Chen, J., He, Y., Ding, L., Wang, A., Lin, H., Hong, H., Zhang, Y., Yu, H., 2013. A new insight into membrane fouling mechanism in submerged membrane bioreactor: osmotic pressure during cake layer filtration. Water Res. 47 (8), 2777–2786. Zhao, L., Wang, F., Weng, X., Li, R., Zhou, X., Lin, H., Yu, H., Liao, B.-Q., 2017. Novel indicators for thermodynamic prediction of interfacial interactions related with adhesive fouling in a membrane bioreactor. J. Colloid Interf. Sci. 487, 320–329. Zhao, L., Zhang, M., He, Y., Chen, J., Hong, H., Liao, B.-Q., Lin, H., 2016. A new method for modeling rough membrane surface and calculation of interfacial interactions. Bioresour. Technol. 200, 451–457. Zhao, Y., Yu, W., Li, R., Xu, Y., Liu, Y., Sun, T., Shen, L., Lin, H., 2019a. Electric field endowing the conductive polyvinylidene fluoride (PVDF)-graphene oxide (GO)-nickel (Ni) membrane with high-efficient performance for dye wastewater treatment. Appl. Surf. Sci. 483, 1006–1016. Zhao, Z., Lou, Y., Chen, Y., Lin, H., Li, R., Yu, G., 2019b. Prediction of interfacial interactions related with membrane fouling in a membrane bioreactor based on radial basis function artificial neural network (ANN). Bioresour. Technol. 282, 262–268.

Chen, J., Zhang, M., Li, F., Qian, L., Lin, H., Yang, L., Wu, X., Zhou, X., He, Y., Liao, B.-Q., 2016. Membrane fouling in a membrane bioreactor: high filtration resistance of gel layer and its underlying mechanism. Water Res. 102, 82–89. Chen, S., Cowan, C.F., Grant, P.M., 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Network 2 (2), 302–309. Chen, Y., Teng, J., Shen, L., Yu, G., Li, R., Xu, Y., Wang, F., Liao, B.-Q., Lin, H., 2019. Novel insights into membrane fouling caused by gel layer in a membrane bioreactor: effects of hydrogen bonding. Bioresour. Technol. 276, 219–225. Dreiseitl, S., Ohno-Machado, L., 2002. Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35 (5–6), 352–359. Drews, A., 2010. Membrane fouling in membrane bioreactors—characterisation, contradictions, cause and cures. J. Membr. Sci. 363 (1), 1–28. Fernando, D.A.K., Jayawardena, A.W., 1998. Runoff forecasting using RBF networks with OLS algorithm. J. Hydrol. Eng. 3 (3), 203–209. Fortunato, L., Li, M., Cheng, T., Rehman, Z.U., Heidrich, W., Leiknes, T., 2019. Cake layer characterization in activated sludge membrane bioreactors: real-time analysis. J. Membr. Sci. 578, 163–171. Ghritlahre, H.K., Prasad, R.K., 2018. Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Therm. Sci. Eng. Prog. 6, 226–235. Hoek, E.M.V., Agarwal, G.K., 2006. Extended DLVO interactions between spherical particles and rough surfaces. J. Colloid Interf. Sci. 298 (1), 50–58. Huang, G.B., Chen, L., Siew, C.K., 2006. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Network 17 (4), 879–892. Iliyas, S.A., Elshafei, M., Habib, M.A., Adeniran, A.A., 2013. RBF neural network inferential sensor for process emission monitoring. Control Eng. Pract. 21 (7), 962–970. Jang, J.R., Sun, C., 1993. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Network 4 (1), 156–159. Li, R., Lou, Y., Xu, Y., Ma, G., Liao, B.-Q., Shen, L., Lin, H., 2019. Effects of surface morphology on alginate adhesion: molecular insights into membrane fouling based on XDLVO and DFT analysis. Chemosphere 233, 373–380. Lin, H., Gao, W., Meng, F., Liao, B.-Q., Leung, K.-T., Zhao, L., Chen, J., Hong, H., 2012. Membrane bioreactors for industrial wastewater treatment: a critical review. Crit. Rev. Environ. Sci. Technol. 42 (7), 677–740. Meinders, J.M., van der Mei, H.C., Busscher, H.J., 1995. Deposition efficiency and reversibility of bacterial adhesion under flow. J. Colloid Interf. Sci. 176 (2), 329–341. Mirbagheri, S.A., Bagheri, M., Bagheri, Z., Kamarkhani, A.M., 2015. Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm. Process Saf. Environ. 96, 111–124. Moradkhani, H., Hsu, K.-L., Gupta, H.V., Sorooshian, S., 2004. Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J. Hydrol. 295 (1), 246–262. Shen, L., Zhang, Y., Yu, W., Li, R., Wang, M., Gao, Q., Li, J., Lin, H., 2019. Fabrication of hydrophilic and antibacterial poly(vinylidene fluoride) based separation membranes by a novel strategy combining radiation grafting of poly(acrylic acid) (PAA) and electroless nickel plating. J. Colloid Interf. Sci. 543, 64–75. Shetty, G.R., Chellam, S., 2003. Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks. J. Membr. Sci. 217 (1), 69–86.

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