Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: Experimental and artificial neural network modeling

Journal of Water Process Engineering 33 (2020) 101046

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Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: Experimental and artificial neural network modeling

T

Muhammad Yaqub, Seung Hwan Lee* School of Civil and Environmental Engineering, Kumoh National Institute of Technology, 1 Yangho–dong, Gumi, Gyeongbuk, 730-701, Republic of Korea

A R T I C LE I N FO

A B S T R A C T

Keywords: Activated carbon fiber Artificial neural network Mercury Micellar enhanced ultrafiltration Sodium dodecyl sulfate

MEUF was applied efficiently to remove mercury (Hg) from simulated wastewater by using polyacrylonitrile membrane and sodium dodecyl sulfate (SDS) as surfactant. In this process leakage of surfactant monomer to permeate water causing a secondary pollution that was addressed by using MEUF followed by activated carbon fiber (MEUF-ACF). The effect of operating parameters including, concentration of Hg and pH of feed solution, molar ratio of SDS to Hg, and retentate pressure was investigated to optimize MEUF process. Moreover, artificial neural network (ANN) model was proposed to predict Hg removal efficiency to optimize MEUF process without doing laborious and time-consuming experimental work. ANN model performance was evaluated on the basis of statistical values such as mean square error (MSE) and coefficient of determination (R2). The experimental results presented that optimum operating parameters were found as 10 ppm of Hg concentration, pH 7.0, molar ratio of SDS to Hg 8:1 and retentate pressure was 1.5 bar. MEUF results showed 95.75% and 50.91% removal of Hg and SDS, respectively, while 96.83% Hg and 97.15% of SDS rejection was achieved using MEUF-ACF. The statistical values of proposed ANN model presented high degree of agreement between experimental and predicted values by ANN (R2 > 0.95 for training, validation and testing dataset). Resultantly, MEUF-ACF can eradicate issue of secondary pollution and proposed ANN model can be a competitive, powerful and fast alternate to laborious experimental work for MEUF process optimization.

1. Introduction The contamination of water resources with heavy metals has become a global concern particularly mercury (Hg) is one of the most harmful pollutant due to its toxic, persistence, and bio-accumulative characteristics [1]. The discharge of Hg into the environment is occurring through natural sources including, natural deposits, forest fires, volcanoes and thermal springs that are contributing about one third of the present Hg air emissions [2]. Additionally, a significant amount of Hg is released to the environment through anthropogenic sources such as coal combustion, waste landfills, industrial and manufacturing processes, laboratory equipment applications, medical products, fungicides and electrical industries [3,4]. The presence of Hg in waterways have a potential risk to human health because of its accumulation in the sea food chain which ultimately reach to human body [5,6]. Strict regulations regarding Hg concentration in drinking water and its limits in the wastewater are imposed. Therefore, nowadays, removal of Hg from

water and wastewater streams getting more attention worldwide. Various membrane techniques were used for Hg removal from water and wastewater such as, adsorptive ultrafiltration (UF), microfiltration (MF), nanofiltration (NF), and reverse osmosis (RO) [7–10]. UF technique is a pressure-driven membrane separation that mainly differs from RO and NF in terms of applied pressure and provides higher flux with less energy consumption. Enhancements to the conventional UF by applying polymer and surfactants are known as polymer and micellar enhanced ultrafiltration (PEUF) and (MEUF) respectively, used for the removal of Hg previously, [11]. In MEUF process a surfactant is added into the solution above its critical micellar concentration (CMC) to form large micelles that capture the relatively small-sized pollutants and therefore, the contaminated micelles get rejected by the UF membrane [12,13]. A study for Hg removal through MEUF presented that 94% rejection was achieved at optimum values of feed solution concentration, flow rate and pressure 5 ppm, 16 L/min and 4 atm, respectively, [14]. The

Abbreviation: ACF, Activated carbon fiber; ANN, Artificial neural network; CF, Cartridge filter; R2, Coefficient of determination; CMC, Critical micellar concentration; ICP-OES, Inductively coupled plasma-optical emission spectrometry; MSE, Mean square error; Hg, Mercury; MEUF, Micellar enhanced ultrafiltration; MF, Microfiltration; MWCO, Molecular weight cut off; NF, Nanofiltration; PEUF, Polymer enhanced ultrafiltration; PAN, Polyacrylonitrile; RO, Reverse osmosis; SDS, Sodium dodecyl sulfate; UF, Ultrafiltration ⁎ Corresponding author. https://doi.org/10.1016/j.jwpe.2019.101046 Received 8 August 2019; Received in revised form 23 September 2019; Accepted 4 November 2019 2214-7144/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Water Process Engineering 33 (2020) 101046

M. Yaqub and S.H. Lee

through experimental work and ANN model may provide further insight to the researchers to investigate the removal of other heavy metals from the wastewater.

previous studies for different heavy metals removal through MEUF [15–21] provided a foundation for further research work. Previously, this process was optimized by changing one parameter at a time while others were kept constant that is tedious, lengthy and expensive work and use of chemicals is hazardous to the environment as well. Therefore, process modeling that establishes a relation between operating parameters and removal efficiency of the system can be helpful in both designing and operational phase to optimize and control the process. Recently, in environmental engineering artificial neural network (ANN) modelling received more attention because of their superior characteristics in processing of non-linear and complex datasets with reliable results [22]. ANN selection as a suitable validation technique might be supported by its flexibility and ease, by route of which it might not only resolve non-linearity related with action of operating parameters but also learn and predict relationship between inputs and outputs without any requirement of mathematical equations [23]. As reported in the recent literature, fuzzy wavelet neural network was successfully proposed to simulate and predict river water quality [24] and indoor air quality was monitored by using soft sensor modeling technique [25]. In our previous work, predictions of proposed ANN models provided good results for cadmium and chromium removal from wastewater using polymeric inclusion membranes [26,27]. A response surface methodology and ANN approach was used for modeling of dye removal from water through adsorption [28]. The characterization of physical and chemical parameters of drinking water was modeled by ANN with reliable results [29]. Neural networks were used for modeling of fouling growth and flux drop in nanofiltration and reverse osmosis systems [30]. Moreover, previously, ANN application for optimization of MEUF process for zinc removal from wastewater provided reliable results [31] and response surface methodology improved the understanding of the process performance and its optimization for cadmium and zinc removal [32]. The fuzzy modeling for lead removal from aqueous solution provided acceptable results for MEUF optimization [33]. This literature review indicated the importance of ANN modeling in water treatment systems that could be an alternative approach to minimize experimental work load, therefore, this soft computing technique was selected for this study. Due to the widespread applications of MEUF process as described in the literature, it needs further studies for large scale implementations. For this purpose, major issues should be addressed first, including, secondary pollution caused by surfactant monomer, laborious, lengthy and expensive work for optimization of the process. Therefore, process optimization is required to ensure good operational stability of UF membrane to make this process economically feasible. The application of ANN models to optimize MEUF process and its numerous characteristics has not yet been completely discussed in the literature. To address all the question raised, we conducted MEUF process optimization for Hg removal from simulated wastewater by considering the effect of Hg concentration, pH, molar ratio of SDS to Hg and retentate pressure. Previously, few MEUF studies for Hg removal from simulated wastewater was conducted but no study was found with an application of MEUF-ACF as per author’s knowledge. This technique can be helpful to address secondary pollution caused by leakage of surfactant monomer to the permeate water. Moreover, there is no previous work done to predict and optimize MEUF process for Hg removal using ANN modeling technique. Therefore, novelty of this work is to optimize the Hg removal efficiency of 30 kDa molecular weight cut off (MWCO), polyacrylonitrile (PAN) membrane from concentrated Hgcontaminated wastewater by using SDS. Investigation of MEUF-ACF performance for Hg and SDS removal from simulated wastewater was also conducted. Moreover, proposed ANN model was used to optimize MEUF process and then a comparison was drawn between experimental and model predicted results in order to justify the predictions made by the model. This model might be helpful in future studies without doing laborious experimental work, very fast to perform, economical and environmentally friendly. Resultantly, optimization of MEUF process

2. Materials and methods 2.1. Materials and experimental work Mercury nitrate (Hg(NO3)2.H2O) of 99% purity was purchased from Sigma-Aldrich Co., USA (molecular weight of 342.62) and used for the preparation of feed solution. Sodium dodecyl sulfate (SDS) of 98% purity was procured from Junsei Chemicals, Japan (molecular weight of 288.38) and used as a source of surfactant without any further treatment. The fresh feed solution of mercury and surfactant was prepared for each run using deionized water. During solution preparation and cleaning process deionized water was used throughout the experiments to avoid interference of other ions. The solutions were prepared by mixing stoichiometric amounts of SDS surfactant and mercury in three liters of distilled water and kept shaking at 100 rpm for an hour. Hollow fiber membranes, of 30 kDa, was used for ultrafiltration (Chemicore Ltd., Korea). This was cross-flow ultrafiltration type in which the retentate is re-circulated into the feed tank and permeate water is collected in a separate tank. The characteristics of the membrane and ACF used in this process are presented in Table 1 and experimental setup of MEUF-ACF is shown in Fig. 1. ACF was purchased from ACF Korea Ltd., whose cartridge code no. is FC-B. Bulk density and iodine number of ACF were 0.2 kg/m3 and 1500 mg/g, respectively. The schematic diagram of lab-scale MEUF-ACF system is depicted in Fig. 1, which comprises of (1) mixer, (2) feed water tank, (3), (4) feed and cleaning pumps, respectively, (5) UF, (6) reject water circulation (7) MEUF product water tank, (8) cartridge filter, (9) activated carbon fibers, (10) MEUF-ACF product water tank. The experimental module consists of a feed and cleaning tank, ultrafiltration membrane, permeate and permeate storage tanks. Ultrafiltration is a cross-flow type in which rejection of permeate is recirculated to the feed tank and permeate water is collected in a separate tank. As shown in the figure, the ACF unit comprises a 10 μm cartridge filter (CF) connected with a feed tank through pump to prolong the life of the activated carbon fiber (ACF) units connected in series to maximize SDS removal from the MEUF effluent. After each experiment the membrane was flushed and backwashed with distilled water and cleaned with 0.1 M NaOH first followed by flushing with deionized water then cleaning was done with 0.5% HCl and flushed again. The CF and ACF were cleaned with distilled water before soaking in 0.1 M of NaOH and 2% of HCl for a day. Details of the experimental operational conditions are summarized in Table 2. 2.2. Analytical methods The glassware used in the experimentation was cleaned with regular laboratory detergent and rinsed with deionized water. The solutions were prepared by mixing stoichiometric amounts of Hg and SDS surfactant in three liters of deionized water and kept shaking at 100 rpm for an hour. For each run of experiments fresh solutions were prepared Table 1 Characteristics of ultrafiltration membrane and ACF. Membrane material Membrane type Flow direction Flow type Effective surface area, m2 Membrane diameter (inside/outside) mm Molecular weight cut-off (MWCO) ACF BET surface area m2/g Weight of ACF, g/cartridge

2

Polyacrylonitrile Hollow fiber Inside to outside Cross-flow 0.055 0.8/1.4 30 kDa 1000 30

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containing mercury was prepared from stock solution at predefined concentrations. Before mercury analysis feed water samples were diluted as per required. A maximum temperature of 400C was maintained in a chamber containing glass nebulizer with nebulizer gas flow of 0.75 L/min. The flow rate of water samples introduced to the apparatus was 0.5 L/min and axial analysis was done at 15 mm view distance. Plasma and auxiliary gas flows were maintained at 15 and 1.5 L/min, respectively, using argon gas. Initially instrumental calibration was monitored using mercury standard calibration solutions concentration as mentioned above. The test results were checked by observing the mercury concentration of standard quality control solutions and noted that results were within the limits. Triplicate samples were prepared and analyzed to confirm the results. The rejection percentage of SDS and mercury was calculated by using Eq. (1). Fig. 1. Schematic diagram of MEUF-ACF experimental setup.

R= (1 −

Table 2 MEUF experimental operational conditions. Sampling time, min Hg concentration, ppm pH Molar ratio of SDS to Hg Retentate pressure, bar

Cp )* 100 Ci

(1)

where, R = rejection (%); Cp = permeate concentration; Ci = influent concentration. 5, 10, 20, 30, 40, 50, 60 5, 10, 20, 30, 40, 50 4, 5, 6, 7, 8 2:1, 5:1, 8:1, 10:1, 12:1 1, 1.5, 2, 2.5, 3

2.3. Artificial neural networks (ANN) ANN model is a computational technique similar in structure and function to biological neural networks and their working is like human nervous system including receiving, processing, and transmission of information through computer application [35]. Basically these models are digital form of human brain where computer programs are developed to process the available information and predict for future [36]. The development of ANN model mainly comprises of the subsequent steps such as collection of dataset, pre-processing and analysis of the dataset, creation of model by configuration of model topology, training, validation of proposed model and lastly simulation and prediction was conducted by using validated network [26,35]. After defining topology of a network training is the significant step where model is learns the weights of the network. It is an iterative process proceed step by step with small update in weights at each iteration and ultimately model performance changes in each iteration. Finally, it defines whether model is good, or good enough, to solve defined problem. The validation is used to offer an unbiased assessment of ANN model fit on the

to avoid any interference. The pH of solution without any adjustment was measured by using Multifunction meter CX-505, Elmetron, Poland and then pH of solution was adjusted as per required in this study. MEUF samples were pretreated as per standard methods for the examination of water and wastewater [34]. SDS concentration in the simulated wastewater and permeate water samples was measured through extraction-spectrophotometric method by using UV/VIS3600Plus (Shimadzu Corporation) spectrophotometer at wavelength of 625 nm. Freshly prepared standard solutions of SDS and blank sample (DDI water) were used for spectrophotometer calibration. Mercury concentration in simulated wastewater, permeate and retentate water samples was determined by using inductively coupled plasma-optical emission spectrometry ICP-OES technique (720-ES, Varian) at wavelength of 194.16 nm. The standard calibration solutions

Fig. 2. Artificial neural network schematic diagram. 3

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study we used most common min-max normalization technique for data preprocessing. In min-max normalization variables are normalized in the range of [0–1] by using Eq. (5).

training dataset for the tuning of model hyperparameters. Model testing (unseen dataset) provides the predicted results of the final model learned on the basis training dataset [37]. The architecture of ANN included an input layer with inputs x1 to xn, one or more hidden layers having number of neurons n1 to nn and an output layer as shown in Fig. 2. Nodes (neurons) are the main character of each layer that perform mathematical operations on its received inputs. These nodes are linked to the nodes in the following layer through synapses that carries weight to characterize the significance of node output for subsequent operation. Finally, output layer collects all data available in the developed neural network into the anticipated output parameter, where each output has their own node [38,39]. In each neuron propagation and activation functions are performed as shown in Fig. 2 and described below. Propagation function and activation functions are expressed in Eq. (2) and (3) respectively, node output is given in Eq. (4) below [38].

X=

2.3.2. Proposed ANN model topology The training of ANN model is the most significant step to find the best combination of weights and bias through selection of appropriate training algorithm and by tuning hyperparameters. A training algorithm conduct learning through introduction of input data to the input nodes and then randomly assumed weights are used to calculate output. Afterwards, model continue to learn from input and output dataset and keeps changing the weights until the output error stops improving or when fixed number of iterations achieved [44]. Most commonly used Levenberg-Marquardt (trainlm) learning algorithm is a training function that update the weights and bias values as per Levenberg-Marquardt optimization. It is a variation of Newton’s method that was designed to minimize functions which are sum of other nonlinear functions. Moreover, it is capable to learn more quickly between the training techniques available in the MATLAB toolbox, and mostly considered as a first-choice supervised algorithm, but more memory requirements in this technique as compared to other algorithms [45]. The number of nodes in each layer is an important criterion to determine the topology of the ANN model as shown in Fig. 3. There is no standard method for finding the ideal number of hidden nodes. A trialand-error method used to find best ANN topology although it is a timeconsuming technique [46]. To determine the optimum number of nodes in the hidden layer, different topologies were examined, in which the number of nodes was varied and MSE was used as an error function [47]. Training of model was stopped either after attaining the minimum MSE between the model prediction and experimental results or as reaches the maximum prescribed iterations. The overfitting problem occur when error on training dataset is driven to a very small value however, when new dataset fed to the model then results shows big error. This problem can be avoided by improving generalization of a network by early stopping that is automatically provided for all supervised models. In this technique when network starts overfitting then validation error begins to increase and as it reaches to a defined value of iterations or MSE then training is stopped, and the weights and biases for minimum validation error are restored [48]. Another issue happen during training of a model is the local minima that can be avoided probabilistically by randomizing starting weights of ANN, that can be done by training a model more than once. The normalization of dataset is also helpful to avoid local minima in regression problems [49]. ANN model basically develops a relationship between independent and dependent variables and their learning capacity depends upon the size of training dataset but there is no ideal distribution of data for training, validation and testing [50]. In present study, experimental dataset contained 147 rows was divided randomly into training (70%), 103 rows, validation (15%), 22 rows and testing (15%), 22 rows (unseen dataset) for modeling. The training facilitates to get optimum weights, validation helps to stop the training at the point of best generalization while testing is required to measure the performance of the model with unseen dataset. MATLAB R2017b was used for ANN modeling.

∑ wij Xi (t ) − bi (2)

j=1

1 f ≥0 σ (f ) = ⎡ ⎣ 0 Otherwise

(3)

n

Yi (t + 1) = σ

⎛ ⎞ w X (t ) − bi ⎜ ∑ ij i ⎟ = j 1 ⎝ ⎠

(4)

In these equations wij is the weight, Xi is the input, bi is the bias and t represent time step. Feed forward neural networks with back propagation that are most commonly employed in previous studies as mentioned in encyclopedia of machine learning [40]. The feed forward is a static neural network model that develop an input-output relationship which can be defined through an algebraic nonlinear mapping function. This static neural network is characterized by memory-less nonlinear equations and their output is a function of the existing inputs [41]. 2.3.1. Data preprocessing for ANN modeling The removal efficiency (RE) of mercury (Hg) from simulated wastewater using MEUF process was optimized by considering operational parameters such as, time, Hg concentration and pH of feed solution, molar ratio (MR) of SDS to Hg and retentate pressure. The dataset was obtained from this experimental work and statistically analyzed to calculate maximum, minimum, mean and standard deviation as presented in Table 3. It was noted that dataset is spread on a wide range and large value of standard deviation confirms the non-normality of data distribution. Therefore, normalization of both input and target datasets was required to speed up the learning process by systematic weight initialization that leads to faster convergence. Normalization changes the value of datasets to a common scale, without misrepresenting the differences in ranges of the values as shown in Table 3. There are number of techniques that can be applied for normalization of dataset including, min-max, z-score decimal scaling [42,43]. In this Table 3 Data statistics of model variables (n = 147). Variables

Input Layer Time Conc. of Hg Retentate pressure MR of SDS to Hg pH Output Layer RE of Hg

Units

Unnormalized data

Normalized data

xmin

xmax

xmean

xmin

xmax

xmean

min ppm bar – –

5.00 5.00 1.00 2.00 4.00

60.0 50.0 3.00 12.00 8.00

30.714 25.833 2.000 7.400 6.000

0.100 0.100 0.100 0.100 0.100

0.900 0.900 0.900 0.900 0.900

0.474 0.348 0.269 0.569 0.652

Cp/Ci

85.085

90.67

87.16

0.804

0.900

0.871

(5)

where, minA and maxA are the minimum and maximum values of variable A. The ‘x’ and ‘X’ represents original and normalized value of an attribute.

n

v=

x − minA maxA − minA

2.3.3. Model performance evaluation The aim of model performance evaluation is to confirm accuracy of proposed model to check any error for its application with confidence [51,52]. In this study, two performance criteria were used to evaluate proposed model predicted results. The mean square error (MSE) Eq. (6) 4

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Fig. 3. Best topology of proposed model with input, hidden and output layer.

and coefficient of determination (R2) Eq. (7) were used as performance measuring criteria in evaluation of the model that are described below [26,45].

concentrations ranges from 10 to 50 ppm in feed solution at SDS concentration of 0.915 mM below CMC value. Literature studies described that below CMC of surfactant micelles are absent firstly but they form micelles when retentate surfactant concentration reached beyond CMC. A higher concentration of surfactant may be considered in the adjacent layer to the membrane surface. Moreover, additional effect of membrane including charge on the membrane surface, asymmetricity or hydrophobicity may be affecting parameters. The monomers of surfactant may also be retained by the membranes to some extent that can accumulate at the surface of membrane. Resultantly, surfactant concentration may surpass CMC value of the surfactant therefore, micelles may be existing near membrane surface even no micelles were present in the bulk [53–55]. As mentioned in literature a study for cadmium removal by using 0.3242 mM of SDS indicated the average RE of 58.7% so we decided to consider higher concentration (0.915 mM) in this study. The results showed that average RE of Hg was 97.80, 95.75, 83.86, 64.04, 47.95 and 30.99% at Hg concentration of feed solution such as 5, 10, 20, 30, 40 and 50 ppm, respectively. As expected, the RE decreased with an increase in Hg concentration of feed solution caused mainly due to less micelle surface area availability for electrostatic adsorption of higher concentrations [56]. This resulted in lower RE at higher concentration of Hg in feed solution without any adjustment of pH. The decrease in RE with passage of time was occurred because metal

n

1⎛ ⎞ i i − X pre MSE = [ ⎜∑ (Yexp ) 2⎟ ] n i=1 ⎠ ⎝ n

R2

(6) n

n

⎡ n∑i = 1 yexp, i ypre, i − (∑i = 1 yexp, i )(∑i = 1 ypre, i ) =⎢ n n n n 2 ⎢ [n∑i = 1 y exp, i − (∑i = 1 yexp, i )2] × [n∑i = 1 y 2 pre, i − (∑i = 1 ypre, i )2] ⎣

2

⎤ ⎥ ⎥ ⎦ (7)

In these equations, ‘n’ represents the number of experimental values i i and Y pre and Yexp presented model predicted and their corresponding experimental values, respectively. 3. Results and discussion 3.1. Experimental results 3.1.1. Effect of mercury concentration in feed solution Number of experiments were conducted to investigate the percentage removal efficiency (RE) of Hg at various mercury concentrations in the feed solution as shown in Fig. 4. RE of Hg was studied with

Fig. 4. Effect of Hg concentration and pH of feed solution, molar ratio (MR) of SDS to Hg and retentate pressure. 5

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severe secondary pollution of surfactant monomers.

concentration increased of the feed solution as retentate water was recirculated to feed water tank, fouling of membrane and concentration polarization. In literature similar results were presented for different heavy metals including cadmium, chromium and nickel [18,57,58]. This suggests that concentration of Hg in permeate water increased proportionally to its initial concentration of feed solution due to availability of lesser micelle surface area to adsorb higher metal concentrations electrostatically i.e. the higher initial concentration, the lower removal efficiency. As a result of increase in metal concentration available surface for metal ions adsorption becomes insufficient so excess ions remained soluble in the solution and ultimately drop the removal efficiency [59]. The 10 ppm initial Hg concentration was found optimum as it provides higher RE of 95.75% while other parameters were kept constant.

3.1.4. Effect of retentate pressure on mercury removal The effect of retentate pressure on Hg removal was investigated by varying values from 1 to 3 bar while Hg and SDS concentrations were 10 ppm and 0.915 mM, respectively, pH was 7.0 and molar ratio of SDS to Hg was 8:1 an. Results presented in Fig. 4, indicated that average Hg removal was 89.76, 94.58, 95.38, 96.82 and 97.67% at retentate pressure of 1, 1.5, 2, 2.5 and 3 bar, respectively. It was noted that increase in retentate pressure resulted in higher removal of Hg. The removal efficiency decreased with respect to time due to increased metal concentration in the feed solution and membrane fouling, pores blocking and concentration polarization. It was noticed that rejected micelle-metal complexes were accumulated at the surface of membrane during MEUF process, this phenomenon increases the micelles concentration at the surface that eventually improved the metal ions removal. At higher pressure similar results were reported to remove cadmium, chromate and copper [56,62,63]. It is considered that higher pressure increases the gel layer thickness at surface of the membrane, that ultimately increases rejection of metal-micelle complex. On the contrary, fast drop in permeate was noted as retentate pressure was increased due to the higher concentration polarization at the membrane surface. During this experiment permeate flux was dropped from 38.45 L/m2.h to 9.76 L/m2.h when retentate pressure was increased from 1 to 3 bar and optimum value of permeate flux 30.92 L/m2.h was achieved at 1.5 bar with maximum RE of Hg of 95.19% while pure water flux was noted as 63.73 L/m2.h. Therefore, 1.5 bar was found as optimum retentate pressure because it presented better permeate flux and removal efficiency.

3.1.2. Effect of pH on mercury removal Experiments were performed to investigate the effect of pH on the removal of Hg for mercury concentration of 10 ppm while SDS concentration was 0.915 mM. As shown in Fig. 4, average RE was noted 67.62, 74.24, 76.73, 77.65, and 77.72% at pH value of 4, 5, 6, 7 and 8 respectively. A significant increase in RE was observed by increasing pH of feed solution at first until a maximum removal was achieved and then started decreasing slowly. At lower pH, competition between mercury and H+ ions happen for adsorption onto micelles surface and because of competition with H+ ions, mercury adsorption onto the micelle surface was dropped. On the other hand, H+ bounding with functional groups dissociate easily at higher pH value and then deprotonated functional groups enhance the binding of Hg as similar results were reported for other heavy metals [56]. The influence of pH is dependent on type of metals needs to be selected from the feed solution regardless of H+ ions competition with the metal ions during electrostatic adsorption on micelles surface [60]. As decrease of RE was noted with respect to time due to higher metal concentration in the feed solution and membrane fouling because of concentration polarization with the passage of operating time. Similar trend was noted for other heavy metals including cadmium, chromium and nickel [18,57,58]. The adsorption of Hg can be controlled by using the same principles as for adsorption of the other heavy metals. Even polymer enhanced ultrafiltration showed similar results of Hg removal from wastewater using ploy (vinyl pyrrolidinone) and provided reproducible quantitative results [61]. The pH 7.0 of feed solution was noted as optimum value while other operating parameters were maintained constant.

3.1.5. Comparative study of UF, MEUF and MEUF-ACF Simple ultrafiltration (UF) showed average removal of Hg only 31.57% that may be attributed to the membrane characteristics including charge, asymmetricity or hydrophobicity of the membrane. MEUF presented better results for Hg removal from simulated wastewater up to 95.75% but surfactant removal was only 50.91%. It is assumed that higher MWCO of membrane and very low concentration of SDS were the reasons to achieve only 50.91% surfactant removal during MEUF process. This result was supported by a recent study for cadmium removal at low surfactant concentration. It was reported that UF membrane may accumulate 50% of the total missing quantity of the surfactant (SDS). Moreover, it was noticed that accumulated amount of the surfactant on UF membrane increased with the increase of its concentration [64]. A set of experiments was performed to explore the removal of excess Hg ions and unbound surfactant monomer present in the MEUF permeate by coupling with ACF unit. The highest removal of SDS was observed in MEUF-ACF while a slightly increase of Hg removal was noted as shown in Fig. 5. It is clear that SDS rejection was markedly increased in MEUF-ACF as compared to MEUF while a minor increment in Hg removal was also noted. Hence, experimental results proved that MEUF-ACF may be a promising technique for Hg and SDS removal from simulated wastewater. Similar results were presented in previous studies for other heavy metals removal from wastewater [15,56,62], because of adsorptive capacity of ACF, removal efficiencies were improved. In this technique, a drawback of MEUF, secondary pollution caused by leakage of surfactant monomers to the permeate technique was addressed. In conclusion, at optimum operating parameters Hg removal was 95.75% and SDS rejection was only 50.91% in MEUF while 97.25% and 98.87% removal of Hg and SDS, respectively was accomplished in MEUF-ACF. Therefore, with higher removal efficiencies MEUF-ACF can be a promising technique for heavy metals removal from wastewater.

3.1.3. Effect of molar ratio of SDS to Hg To find the effect of molar ratio of SDS to Hg, laboratory experiments were conducted at 10 ppm of Hg concentration and pH was maintained at 7.0 while SDS concentration was kept constant as 0.915 mM. As shown in Fig. 4, average Hg removal was > 95% for a molar ratio of 8:110:1 and 12:1 while RE was decreased to 82.14 and 71.79%, at molar ratios of 5:1 and 2:1, respectively. The increase in RE was noted with an increase in molar ratio of SDS to Hg. The drop in RE was observed with respect to time due to increased metal concentration in the feed solution and membrane fouling, pores blocking and concentration polarization. Previous studies supported these results as it was the case with other heavy metals cadmium and chromate, removal using MEUF technique [56,62]. It was found that RE of Hg was higher at higher SDS concentration that means more micelle surface is available to adsorb Hg through electrostatic attraction. As critical micelle concentration (CMC) of SDS concentration approaches then further addition of surfactant converted to micelles that adsorb metal ions which can be retained by ultrafiltration. Therefore, higher molar ratio of SDS to Hg results in large micelle surface area available for electrostatic attraction of Hg ion as noted in this study. On the other hand, very higher concentration of surfactant may result in causing severe secondary pollution in the permeate water. The optimum molar ratio 8:1 presented better results in terms of Hg removal and without causing

3.2. ANN modeling results In this study for MEUF optimization we used static feed forward 6

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Fig. 7. Best validation performance of proposed ANN model. Fig. 5. Hg and SDS removal by MEUF and MEUF-ACF.

neural networks with backpropagation due to their widespread applications in modeling of nonlinear processes [41]. The fastest LevenbergMarquardt (trainlm) backpropagation algorithm was selected for training of model to update the weight and bias values. The experimental dataset contained 147 rows was divided randomly into training (70%), 103 rows, validation (15%), 22 rows and testing (15%), 22 rows (as unseen dataset) for modeling. To evade overfitting problem early stopping technique was used while local minima was avoided by randomizing starting weights of network and by normalization of dataset. To find best topology of network trial and error method was used. Initially model was run at 1000 epoch and validation performance was evaluated on the basis of MSE while number of neurons were varied from 2 to 30 and results are shown in Fig. 6. This trial and error method indicated that best training was accomplished at 10 neurons in the hidden layer. The number of epochs were also defined as training was stopped at 29 epochs because minimum set value of MSE was achieved at 23 epochs. Afterwards, further increment in number of neurons caused an increase in MSE, therefore, the best topology of proposed ANN was found (5-10-1) on the basis of reproducible MSE value of 8.28*10−4 at 23 epochs as shown in Fig. 7. The proposed topology of ANN model (5-10-1) represents one input layer consists of 5 nodes, a hidden layer contains 10 nodes, and an output layer has only one node. The operating parameters including, time (t), Hg concentration (Hg), pH, molar ratio of SDS to Hg (MR) and retentate pressure (P), were considered as input layer nodes while Hg removal efficiency (RE) was used as output layer node as shown in Fig. 8. The results of proposed ANN model, training, validation and testing

Fig. 8. Proposed ANN model architecture.

are presented in Fig. 9. Training confirm best learning of the model while validation approved fine tuning of hyperparameters, resultantly testing dataset showed a good match between experimental and predicted results by ANN model. Therefore, proposed model can be useful to find the optimum operating parameters of MEUF process to remove Hg from simulated wastewater and then can be confirmed through experimental analysis and its application can be extended to other

Fig. 6. Mean square error (MSE) vs number of neurons. 7

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M. Yaqub and S.H. Lee

Fig. 9. Scatter plot of proposed ANN model training, validation and testing results.

predicted results of model on unseen dataset named as testing has shown outstanding results because of superlative training of model as presented in Fig. 9. Moreover, comparison of the experimental results and predictions by ANN model for training, validation and testing datasets as presented graphically in Fig. 10 cleared that training results are well distributed around X = Y line in a narrow area that indicates best learning of model as confirmed by prediction results by ANN model in testing phase for unseen dataset. Therefore, proposed model proved the robustness of ANN application in optimization of MEUF process that can be applied confidently to similar processes for optimization of process parameters.

heavy metals removal processes. Then predicted results of proposed ANN model were evaluated on the basis of performance criteria such as MSE and R2 values. The MSE values for training, validation and testing were noted as, 0.00083, 0.00096 and 0.0025, respectively while R2 values were found as, 0.98, 0.97 and 0.96 for training, validation and testing. This criteria provided information on general error range between model predictions and experimental results to assess the performance of proposed model [65]. Results of the proposed ANN model on the basis of R2 values are shown in Fig. 10 including, training, validation and testing that demonstrates the relationship between prediction by ANN model and experimental results. The learning of proposed ANN model during training offered best learning, validation to avoid overfitting or underfitting. The 8

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Fig. 10. Scatter plot of removal efficiency (RE) between experimental and predicted results by ANN training, validation and testing.

4. Conclusion

water and the proposed ANN model can be a competitive, powerful and fast alternate to laborious experimental work in MEUF process optimization.

The Hg removal efficiency of MEUF process from simulated wastewater using polyacrylonitrile (30 kDa) membrane and sodium dodecyl sulfate (SDS) surfactant was optimized. To address the secondary pollution caused by leakage of surfactant monomer to permeate water MEUF in combination with activated carbon fiber (MEUF-ACF) was investigated. The effect of operating parameters including, concentration of Hg and pH of feed solution, molar ratio of SDS to Hg, and retentate pressure was explored to optimize MEUF process. ANN model was developed using available experimental dataset for the prediction and optimization of Hg removal efficiency from simulated wastewater through MEUF process. The experimental results indicated that average RE of Hg was 97.80, 95.75, 83.86, 64.04, 47.95 and 30.99% for Hg concentrations of feed solution 5, 10, 20, 30, 40 and 50 ppm, respectively and 67.62, 74.24, 76.73, 77.65, and 77.72% at pH value of 4, 5, 6, 7 and 8 respectively. Moreover, average Hg removal was > 95% for a molar ratio of 8:1,10:1 and 12:1 while it was dropped to 82.14 and 71.79%, at molar ratios of 5:1 and 2:1, respectively and 89.76, 94.58, 95.38, 96.82 and 97.67% removal was observed at retentate pressure of 1, 1.5, 2, 2.5 and 3 bar, respectively. The optimum operating parameters were found experimentally as 10 ppm of Hg concentration, pH 7.0, 8:1 molar ratio of SDS to Hg and retentate pressure of 1.5 bar. MEUF results showed 95.75% and 50.91% removal of Hg and SDS, respectively, while 96.83% Hg and 97.15% of SDS rejection was achieved using MEUF-ACF. The statistical values of proposed ANN model presented high degree of agreement between experimental and predicted values. The MSE values for training, validation and testing were noted as, 0.00083, 0.00096 and 0.0025, respectively while R2 values were found as, 0.98, 0.97 and 0.96 for training, validation and testing. The proposed ANN model proved a reliable data-driven approach to optimize MEUF process. Therefore, MEUF-ACF combined process can eliminate the issue of secondary pollution by removing > 97% of surfactant monomers from permeate

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