The model with a topology of 3-10-1 (corresponding to input, hidden, and output neurons, respectively) provided satisfactory results in the identification of the optimal conditions.

1. Contents lists available at ScienceDirect
Minerals Engineering
journal homepage: www.elsevier.com/locate/mineng
Removal of boron from mining wastewaters by electrocoagulation method:
Modelling experimental data using artificial neural networks
Thiago da Silva Ribeiro, Caroline Dias Grossi, Antonio Gutiérrez Merma,
Brunno Ferreira dos Santos, Maurício Leonardo Torem
⁎
Department of Chemical Engineering and Materials, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ 22453-
900, Brazil
A R T I C L E I N F O
Keywords:
Electrocoagulation
Boron
Artificial neural network
A B S T R A C T
Excess boron in drinking and irrigation water is a serious environmental and health problem because it can be
toxic to many crops and lead to various diseases in humans and animals upon long-term consumption. In this
work, the removal of boron from aqueous solutions was achieved by electrocoagulation using aluminium as the
anode and cathode. The operating parameters influencing the efficiency of boron removal, namely, initial pH
(pH0), current density, and treatment time, were investigated. An optimum removal efficiency of 70% was
achieved at a current density of 18.75 mA/cm2
and pH0 = 4 within 90 min of treatment time. An artificial neural
network (ANN) was utilised for modelling the experimental data. The model with a topology of 3-10-1 (cor-
responding to input, hidden, and output neurons, respectively) provided satisfactory results in the identification
of the optimal conditions. The sum of squared error and correlation coefficient (R2
) were 0.616 and 0.973,
respectively, confirming the good fit of the ANN model.
1. Introduction
Boron is an element found in nature, and it forms compounds with
oxygen, mainly borates. Boron is present in oceans, sedimentary rocks,
coal, shale and some soils. Boron compounds are naturally released into
the atmosphere and aquatic environments by geothermal steam flows,
erosion of clay-rich sedimentary rocks, and anthropogenic sources (Kot,
2009). The major anthropogenic sources are coal mining and combus-
tion, oil exploration, and mining and processing of boron ores
(Schlesinger and Vengosh, 2016).
In aqueous medium, boron is usually present as boric acid (H3BO3)
and borate ion [B(OH)4]−
. The dominant form of inorganic boron in
natural water systems is undissociated boric acid, which is a weak Lewis
acid that behaves as an electron acceptor in solution by accepting OH−
ions. Its ionisation constant is Ka = 5.8 × 10−10
(pKa = 9.24) at 25 °C.
Boron compounds are used in applications such as metallurgy,
micro-electronics, glassware, and agriculture. Boron is an essential
micronutrient for the development of microorganisms, plants, animals,
and humans. However, it can be toxic in large concentrations and
therefore needs to be removed from wastewaters (Ferreira et al., 2006).
The maximum concentration of boron recommended by the World
Health Organization guideline for drinking water is 2.4 mg/L. Although
this value is below the tolerable level of risk to human health, it exceeds
the limit concentration for various types of crops. Therefore, many
countries continue to implement their own standard. (Wang et al.,
2014).
There is no easy or simple method for the removal of boron from
wastewater. The main technologies used for the removal of boron from
effluents are precipitation, adsorption, ion exchange, reverse osmosis,
and electrocoagulation (EC) (Parks and Edwards, 2005). Most of these
techniques have numerous limitations; for example, the adsorption
process is limited by high pH selectivity, low adsorption capacity, poor
physical integrity of the adsorbent, need for acidification, and reduced
efficiency of boron removal after each regeneration. The most widely
recognised method for boron removal is ion exchange, but its dis-
advantages are difficulty in regeneration and waste disposal after
treatment (Wolska and Bryjak, 2013). In this context, therefore, there is
a need for a boron removal process with high removal efficiency and
manageable solid by-product.
Electrocoagulation (EC) requires simple and easy-to-operate equip-
ment, which can monitor current and potential through automation.
The gas bubbles formed promote the homogenisation of the coagulating
agent in the solution. In addition, low sludge production is observed
and the sludge can be easily dehydrated owing to its high concentration
https://doi.org/10.1016/j.mineng.2018.10.016
Received 31 August 2018; Received in revised form 23 October 2018; Accepted 24 October 2018
⁎
Corresponding author.
E-mail addresses: bsantos@puc-rio.br (B.F. dos Santos), torem@puc-rio.br (M.L. Torem).
Minerals Engineering 131 (2019) 8–13
0892-6875/ © 2018 Elsevier Ltd. All rights reserved.
T

2. of hydroxides and oxides. Another advantage is the formation of large,
stable flocs that can be separated by filtration. The gas bubbles facilitate
the removal of pollutants by flotation. EC equipment has few moving
parts, hence presenting lower maintenance costs (Mollah et al., 2001).
As the industry turns to greener processes, the use of electrochemical
technologies such as EC is preferred considering the advantages, such as
the non-requirement of additional chemicals, small volume of sludge
generated, and relatively low maintenance.
EC involves three main mechanisms: (i) generation of coagulants by
electrolytic oxidation of the anode, (ii) destabilisation of contaminants,
particulate suspension, and breaking of emulsions, and (iii) aggregation
of destabilised phases to form a floc (Vasudevan et al., 2010). The de-
stabilisation of colloidal particles occurs through the compression of the
electric double layer, charge neutralisation, sweep flocculation, and
bridge formation (Holt et al., 2002). Several of these mechanisms occur
simultaneously, although the dominant mechanism depends on several
parameters, such as the concentration of the pollutant, pH of the so-
lution, and type of coagulant.
The most widely used electrode materials in the EC process are
aluminium and iron. In the case of aluminium, the main reactions are:
→ + =+ −Al Al e E VAt the anode: 3 1.662 ,s aq( ) ( )
3 0
+ → + = −− −
H O e OH H E VAt the cathode: 2 2 2 0.8277 .aq g2 ( ) 2( )
0
During the oxidation of the sacrificial anode, aluminium forms
polymeric species, such as [Al6(OH)15]3+
, [Al7(OH)17]4+
,
[Al8(OH)20]4+
, [Al13O4(OH)24]7+
, and [Al13(OH)34]5+
, that eventually
transform into Al(OH)3(s) (Ghosh et al., 2008). Al(OH)3(s) has a large
surface area for effective adsorption, and its flocs are separated from
solution by sedimentation or flotation.
Wastewater treatment using the EC process is complicated owing to
the complexity of the reactions, such as electrodissolution of anode,
hydrolysis of metal ions, formation of the hydroxyl complexes, and
adsorption of pollutants on amorphous metal hydroxide precipitates
among others (Mollah et al., 2001). Considering its dependence on
several factors, EC process modelling is challenging and cannot be
solved by simple linear multivariate correlation.
Artificial neural networks (ANNs) are important artificial in-
telligence systems capable of solving a range of complex problems. An
ANN is a computational system made up of units known as neurons.
Neurons are interconnected processors working in parallel to perform a
given task (Reed and Marks, 1999). A training algorithm is used to
adjust the parameters (weights and bias). There are many algorithms
for training neural networks, but the first algorithm developed was
backpropagation. The simplest implementation of backpropagation
learning updates the network weights and biases in the direction in
which the performance function decreases most rapidly, which is the
negative of the gradient (Aggarwal et al., 2005). This algorithm shows
poor performance owing to the low rate of convergence and depen-
dence on the learning rate parameter. However, with regularisation
techniques in the backpropagation training algorithm, it is possible to
obtain a small error for functioning approximation algorithm. ANN
models have shown potential for modelling the EC process, as seen in
the studies of Mirsoleimani-azizi et al. (2015), Manh (2016), Valente
et al. (2014), and others.
In this work, the removal of boron from aqueous solutions was
achieved by EC using aluminium as anode and cathode. A 5.5-L EC cell,
consisting of an arrangement of four monopolar electrodes with a 10-
mm spacing between them, was used in this research at a stirring speed
of 250 rpm. NaCl was added as the supporting electrolyte. The effect of
operating parameters, including initial pH, current density, and treat-
ment time, on the efficiency of boron removal was investigated. An
important objective was to develop an ANN model (with
Backpropagation Bayesian Regularisation called trainbr) that allows for
a reliable prediction of the efficiency of the EC process.
2. Materials and methods
2.1. Preparation of synthetic wastewater
Synthetic wastewater simulating the range of boron concentrations
found in mining wastewater was prepared by dissolving the appropriate
quantity of boric acid (H3BO3) in distilled water to form a stock solu-
tion.
2.2. Experimental apparatus
The system consisted of an electrochemical cell and DC power
supply (ICEL-PS-1001). The synthetic wastewater was stirred magne-
tically at 250 rpm to ensure homogenisation. The pH was monitored
with a pH meter (Hanna). All experiments were carried out at room
temperature (25 °C ± 1 °C).
The EC unit was made of acrylic with the following dimensions:
length = 150 mm; width = 190 mm; and height = 170 mm. The cell
was equipped with four fully-submerged aluminium electrode plates.
The electrodes were 120-mm long, 130-mm wide, and 1-mm thick, and
were perforated uniformly with a 5-mm drill bit to facilitate the
movement of the solution inside the cell. The volume of the cell was
5.5 L. The cell was operated in monopolar mode, which means that all
the electrodes were physically connected to either the positive or ne-
gative outlet of the DC power supply. The distance between an anode
and neighbouring cathode was 10 mm.
2.3. Experimental procedure
The experiment started with the introduction of the solution into the
cell. The initial pH was adjusted to the desired value (3, 4, 5, 6, 7, and
8) using 1 M HCl or NaOH. The concentration of the supporting elec-
trolyte used was 0.88 g/L NaCl. A current density (6.25, 12.50, and
18.75 mA/cm2
) was then applied to the cell and maintained during
each experimental run. Samples were collected at 0, 5, 10, 15, 20, 40,
60, 90, 120, and 150 min during the operation. The pH was also mea-
sured at the same time intervals. For each experiment, the residual
boron concentration in the samples filtered through a 0.45-μm filter
paper (Millipore) was determined by inductively coupled plasma mass
spectrometry (ICP-MS, DRC II, Perkin Elmer).
In order to avoid any interference and ensure surface reproduci-
bility, electrodes were prepared prior to the EC test in the following
manner: (1) mechanically polished with 5 μm abrasive paper; (2)
cleaned in 1 M HCl solution for 2 min; (3) rinsed with distilled water;
and (4) dried with absorptive paper.
2.4. Development of models
The data sets collected from each batch run were used to develop
models based on ANN, and the simulations were conducted with the
assistance of MATLAB R2017a (The MathWorks Inc., USA). The ex-
perimental data were divided into training and test sets and expanded
via cubic spline interpolation, to a total of 2448 vectors in the data set.
The monitored variables were current density (J), pH, and treatment
time.
The adopted ANNs were feedforward with multilayer perceptron as
described in Eqs. (1) and (2).
∑= +
=
bb f a V Bias( )j
j
n
i ij
1 (1)
∑= +
=
cc f b W Bias( )j
j
n
j ij
1 (2)
The activation functions in each neuron from hidden to output
layers were calculated using Eqs. (3) and (4).
T. da Silva Ribeiro et al. Minerals Engineering 131 (2019) 8–13
9

3. = =
+
f b c logsig b c
b c
( | ) ( | )
1
1 exp( | )
j j j j
j j (3)
= =
+
−f b c tansig b c
b c
( | ) ( | )
2
1 exp( | )
1j j j j
j j (4)
The ANN model was trained by Levenberg-Marquardt with Bayesian
Regularisation (trainbr). It minimises a combination of squared errors
and weights and then determines the correct combination so as to
produce a network that generalises well. Validation stops are disabled
by maximum validation failures equal zero so that training can con-
tinue until an optimal combination of errors and weights is found. In
trainbr, regularisation adds an additional term and an objective function
to penalise large weights that may be introduced in order to obtain
smoother mapping.
The training stops when any of these conditions is fulfilled: (1) The
maximum number of epochs is reached; (2) The maximum amount of
time is exceeded; (3) Performance is minimised to the goal; (4) The
performance gradient falls below the minimum performance gradient;
or (5) The Marquardt adjustment parameter exceeds its maximum
value.
The evaluation of the model performance was assessed using the
sum of squared error (SSE) and coefficient of determination (R2
) (Eqs.
(5) and (6), respectively),
∑= −
=
SSE Y Y( )
i
n
observed predicted
1
2
(5)
= −
∑ =
R
SSE
Y
1 ( )
i
n
predicted
2
1
2
(6)
where Yobserved are the observed values (experimental values of the
electrocoagulation efficiency) and Ypredicted are the predicted values from
the model.
3. Results and discussion
In this section, the following operational parameters were in-
vestigated: initial pH, current density, and treatment time.
3.1. Effect of initial pH
The initial pH strongly affects the performance of the EC process,
particularly, the degree of hydrolysis of the Al3+
cation. In EC, the pH
of the solution increases during the process as a result of hydroxyl ion
generation in the cathode. Accordingly, the range of pH values that the
solution exhibits throughout the process plays a key role.
In this study, the effect of initial pH (pH0) on the percentage of
boron removal as a function of time was investigated for pH0 = 3–8, as
shown in Fig. 1. As can be observed, the highest percentage of boron
removal was achieved at pH0 = 4. Several authors (Ezechi et al., 2012;
Missaoui et al., 2013; Yilmaz et al., 2005) achieved the highest per-
centage of boron removal at pH0 = 7. This difference can be explained
by the different experimental conditions used in this study; unlike
previous experiments, pH was not kept constant throughout the EC
process in the present study.
Fig. 2 shows the behaviour of pH throughout the process where the
highest percentage of boron removal was achieved. According to the
results obtained by Cañizares et al. (2006), the predominant coagulant
species at pH 4 are represented by monomeric cations. At this pH, boron
mostly exists as boric acid; thus, it is removed mainly through the
charge neutralisation mechanism. As the pH of the solution increases
from 4 to 8 over time, the predominant removal mechanism changes to
sweep flocculation involving the precipitates of aluminium hydroxide,
which is the predominant species. Consequently, boron removal may
occur by a combination of mechanisms involving colloid charge de-
stabilisation, adsorption to floc surfaces, and incorporation within
amorphous precipitates. It is important to note that aluminium
hydroxide exhibits its lowest solubility in the pH range 6–8.
3.2. Effect of current density
A key parameter in the EC process is the current density since it has
a significant effect on the reaction kinetics. The current density is re-
lated to the extent of anodic dissolution. At the same time, the rate of
electrolytic gas generation and the size of bubbles also depend on the
applied current density. Therefore, current density directly affects the
dosage of coagulant, removal of the contaminant by flotation, and
mixing of the solution.
To investigate the effect of current density on the percentage of
boron removal, experiments were performed at different current den-
sities. Fig. 3 shows the percentage of boron removal as a function of
0 20 40 60 80 100 120 140
0
5
10
15
20
25
30
35
40
45
50
55
BoronRemoval(%)
Time (min)
pH0 = 3.0
pH0 = 4.0
pH0 = 5.0
pH0 = 6.0
pH0 = 7.0
pH0 = 8.0
J = 6.25 mA/cm2
Fig. 1. Effect of the initial pH (pH0) on the percentage of boron removal
([NaCl] = 15 mM, rpm = 250, d = 10 mm, [B] = 50 mg/L, J = 6.25 mA/cm2
).
4 5 6 7 8
0
5
10
15
20
25
30
35
40
45
50
55
BoronRemoval(%)
pH
0
5
10
15
20
40
60
90
150
Time (min)
Charge Neutralization
Sweep Flocculation
Fig. 2. Behaviour of the pH throughout the process (pH0 = 4; [NaCl] = 15 mM;
rpm = 250; d = 10 mm; [B] = 50 mg/L; J = 6.25 mA/cm2
). At pH 4, the pre-
dominant coagulant species are represented by monomeric Al3+
, thus boron is
removed mainly through the charge neutralisation mechanism. As the pH of the
solution increases from 4 to 8 over time, the predominant removal mechanism
changes to sweep flocculation involving the precipitates of aluminium hydro-
xide, which is the predominant species.
T. da Silva Ribeiro et al. Minerals Engineering 131 (2019) 8–13
10

4. initial pH at current densities of 6.25, 12.50, and 18.75 mA/cm2
.
It can be seen from Fig. 3 that the percentage of boron removal
increases with increase in current density. These results converge with
those obtained by Ezechi et al. (2012). Such behaviour may be justified
by the increased generation of Al3+
due to increasing anodic dissolu-
tion and a consequent increase in the hydrolysis products, especially
aluminium hydroxide precipitates. In addition, with increasing current
2 4 6 8
35
40
45
50
55
60
65
70
BoronRemoval(%)
Initial pH
6.25 mA/cm2
12.50 mA/cm2
18.75 mA/cm2
Fig. 3. The percentage of boron removal as a function of initial pH at different
current densities ([NaCl] = 15 mM, rpm = 250, d = 10 mm, [B] = 50 mg/L,
t = 90 min).
Table 1
Performance indices of several neural topologies for predicting the percentage
of boron removal. The coefficient of determination (R2
) and sum of squared
error (SSE) values were analysed for the selection of the best model. The input
neurons are current density (J), pH, and treatment time.
Boron removal model
Neurons in hidden layer Activation function R2
SSE
5 tansig 0.97828 3.250
6 tansig 0.97631 3.010
5 logsig 0.98271 0.943
6 logsig 0.94441 0.751
7 logsig 0.97441 0.775
8 logsig 0.96694 0.627
10 logsig 0.97268 0.616
14 logsig 0.96504 0.550
15 logsig 0.95026 0.508
Fig. 4. ANN used in the prediction of the percentage of boron removal.
Fig. 5. MATLAB Neural Network Toolbox settings for the best model.
Fig. 6. Predicted percentage of boron removal (using ANN) versus experi-
mental data.
Fig. 7. Representation of the behaviour of the predicted data with respect to the
observed data of each sample.
T. da Silva Ribeiro et al. Minerals Engineering 131 (2019) 8–13
11

5. density, an approximation between the percentage of boron removal at
different initial pH values is observed. This behaviour can be explained
by the high concentration of precipitates in the solution.
3.3. ANN modelling
The topology of an ANN is determined by the number of layers,
number of neurons in each layer, and nature of the transfer functions.
Training of ANN topology is the next important step in the development
of a model. Table 1 displays the ANN modelling results for each case
studied, with different number of neurons in the hidden layer and ac-
tivation functions. The R2
and SSE values were analysed for the selec-
tion of the best model.
Fig. 4 displays the configuration of the neural networks used for the
prediction of the percentage of boron removal. All ANN models used in
this work had an input layer structured with 3 neurons, corresponding
to the current density, pH, and treatment time. The output layer con-
sisted of 1 neuron, corresponding to the percentage of boron removal.
In addition, the topology containing 10 neurons in the hidden layer
was adopted and a feedforward backpropagation ANN was used for the
modelling of the process (Fig. 5). The activation functions applied to the
neurons of the hidden and output layers were logsig and purelin, re-
spectively. The applied training algorithm was trainbr.
The network was evaluated by comparing its predicted output va-
lues with the experimental ones using an independent set of data (test
set). Fig. 6 demonstrates the plot of the experimental data (test set)
versus the predicted data. It shows that the points are well distributed
around the X = Y line in a narrow area. A correlation coefficient of
R2
= 0.973 indicates the reliability of the model. Fig. 7 shows how the
predicted data behave in relation to the observed data from each
sample.
Table 2 presents the optimised parameter values (weights and bias)
of the ANN used to predict the percentage of boron removal.
The associated network weights and biases for the ANN model
predict the percentage of boron removal for different experimental
conditions. For comparative assessment of ANN model of the system for
the boron removal efficiency varied in the range of current density, pH
and treatment time, obtaining good agreement with the experimental
data.
A few similar studies have attempted modelling of the EC process
through ANN. Bhatti et al. (2011) carried out assays to optimise the
percentage of chromium removal by varying the electrolysis voltage
and treatment time. Consequently, modelling of boron removal by the
ANN technique shows the importance and contribution of these results.
4. Conclusions
In the present study, the EC process for boron removal demonstrated
significant results; thus, it is not only feasible but also an environment-
friendly technique. The optimum conditions for boron removal were as
follows: initial pH0 = 4, J = 18.75 mA/cm2
, and treatment time of
90 min. Under these conditions, a boron removal efficiency of ap-
proximately 70% was achieved. Boron removal may occur by a com-
bination of mechanisms involving colloid charge destabilisation, ad-
sorption to floc surfaces, and incorporation within amorphous
precipitates.
The use of an ANN model provided a proper correlation between
variables in the EC system. An ANN topology of 3-10-1, logsig in the
hidden layer, purelin in the output layer, and Bayesian regularisation
backpropagation as the training algorithm presented a suitable per-
formance with an SSE of 0.616 and R2
of 0.973, indicating its practical
application. The optimised parameters (weights and bias) were vali-
dated after the training procedure based on minimisation of output
error between target and experimental data.
Treatment of synthetic wastewater in a continuous flow system and
process scale-up remain subjects of further research and can be de-
signed using optimisation models.
Acknowledgements
The authors acknowledge the Pontifical Catholic University of Rio
de Janeiro (PUC-Rio), Conselho Nacional de Desenvolvimento
Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior (CAPES), and Fundação de Amparo à
Pesquisa do Estado do Rio de Janeiro (FAPERJ).
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