2. Materials and methods

#### 2.1. Preparation of the biosorbent

Batch biosorption experiments were conducted using banana flower. The flower was first dried at 378 K for 24 h, washed and separated into three fractions which comprised the bract, the floret and the stem. The fractions were subsequently crushed and sieved into different sizes. The average particle size retained on a sieve was calculated as the geometric mean of the diameter openings in two adjacent sieves in the stack. The geometric mean size (GMS) is expressed as (diameter of upper sieve diameter of lower sieve)0.5 [14].

#### 2.2. Determination of metal ions concentration

Stock solutions were prepared from analytical-grade copper(II) sulfate in distilled water (prepared by a Thermo Scientific Still of pH approximately 7 and conductivity <5 μS/cm). Cu(II) ions were analyzed by the cuprethol method using a Shimadzu UV-1800 Spectrophotometer and verified periodically using an Analytik-Jena contra 700 AAS.

#### 2.3. SEM combined with EDS analysis

The biosorbent was characterized using a scanning electron microscope (SEM) (Hitachi S-3000 N) and an energy dispersive spectroscopy (EDS) analyzer (IXRF Systems) at a voltage of 20 kV. The SEM and EDS were used to investigate the changes in the surface microstructures and the elemental composition of the biosorbent before and after biosorption.

#### 2.4. Biosorption kinetics

#### 2.4.1. Kinetic studies

[1]. Various technologies have been used for the removal of heavy metals including filtration, chemical precipitation, ion exchange, electrodeposition, membrane processes and adsorption using activated carbon [2]. Biosorption of heavy metals has attracted much attention recently due to its simplicity, efficiency, and availability of biomass and waste bioproducts [3]. Of keen interest are agricultural by-products such as peat [4], coconut shell [5], wood [6], banana trunk [7], rice husk [8], peanut shells [9], guava leaves [10] and banana stem [11]. A survey of the literature showed that no work had been reported on the use of banana floret as a biosorbent. In this study, Cu(II) is used as a model metal ion to assess the biosorptive potential of banana floret. Kinetic and equilibrium studies are critical to determining the applicability of biosorbents as well as for the successful design of biosorption systems. These analyses provide an indication of sorption capacity, mechanisms as well as give some insight into the affinity of the biosorbent for the metal ion species [12]. Additionally, the development of predictive models can save time and improve efficiency in experimentation and enable the effectual upgrade to full-scale systems [13]. The objectives of this study are: (i) to determine the biosorption efficiency of banana floret as a new biosorbent; (ii) to elucidate the transport and attachment mechanisms of biosorption through batch kinetic, equilibrium, thermodynamic and desorption studies; and (iii) through single-variable kinetic and equilibrium analysis and ANN-GA modeling simulate batch pro-

Batch biosorption experiments were conducted using banana flower. The flower was first dried at 378 K for 24 h, washed and separated into three fractions which comprised the bract, the floret and the stem. The fractions were subsequently crushed and sieved into different sizes. The average particle size retained on a sieve was calculated as the geometric mean of the diameter openings in two adjacent sieves in the stack. The geometric mean size (GMS) is expressed as

Stock solutions were prepared from analytical-grade copper(II) sulfate in distilled water (prepared by a Thermo Scientific Still of pH approximately 7 and conductivity <5 μS/cm). Cu(II) ions were analyzed by the cuprethol method using a Shimadzu UV-1800 Spectrophotometer

The biosorbent was characterized using a scanning electron microscope (SEM) (Hitachi S-3000 N) and an energy dispersive spectroscopy (EDS) analyzer (IXRF Systems) at a voltage of

cess, which will enable process design and upscaling.

(diameter of upper sieve diameter of lower sieve)0.5 [14].

and verified periodically using an Analytik-Jena contra 700 AAS.

2.2. Determination of metal ions concentration

2.3. SEM combined with EDS analysis

2. Materials and methods

386 Desalination and Water Treatment

2.1. Preparation of the biosorbent

Batch biosorption studies were conducted using the parallel method according to EPA OPPTS method 835.1230 [15]. The study of metal uptake was done in duplicate at room temperature (300 � 2 K) with an adsorbent mass 1.0 g/L and spiked with 50 mg/L of synthetic metal ion solution. Biosorbent masses were accurate to �0.001 g and solution volumes to �0.5 ml. Identical reaction mixtures were prepared for each time interval, agitated on a mechanical shaker and removed at predetermined time intervals [16]. The banana flower was first subjected to kinetic screening. Kinetic tests revealed the stem sorbed 14% more Cu(II) ions than the bract, while the floret sorbed 44% more than the stem. Consequently, a detailed analysis was performed on the banana floret and is reported in this study.

#### 2.4.2. Adsorption yield

The ratio of adsorbed metal ion concentration to the initial metal ion concentration was calculated from Eq. (1).

$$\%Adsorption = \frac{\mathbb{C}\_o - \mathbb{C}\_t}{M} \ast 100\tag{1}$$

#### 2.5. Equilibrium study

The effect of initial concentration was studied by equilibrating 1.0 g/L of adsorbent in synthetic Cu(II) solution of varying concentrations (within the range of 10–100 mg/L to ensure


Table 1. Error functions.

maximum sorption capacity was attained) in a shaking water bath (Julabo SW23) at temperatures varying from 300 � 2 to 328 � 2 K. The concentration of metal ions on the biosorbent was determined using the following mass balance equation:

$$q\_{\epsilon} = \frac{(\mathbb{C}\_{o} - \mathbb{C}\_{\epsilon})}{M} \ast V \tag{2}$$

qt ¼ Kid t

biosorption process. Linear and nonlinear forms are as follows:

Assuming, a linear region as t !0, the initial rate is given as:

Kinetic model Linear plot Nonlinear plot

Table 2. Results of linear and nonlinear regression analysis.

t 0:5 qt ¼ 1 qe � t <sup>0</sup>:<sup>5</sup> <sup>þ</sup>

and

3.1.1. Linear regression

The DC kinetic model was developed to simulate sorption of heavy metals onto heterogeneous media [21]. It is based on the assumption that both diffusion and chemisorption control the

Artificial Neural Network-Genetic Algorithm Prediction of Heavy Metal Removal Using a Novel Plant-Based…

qt <sup>¼</sup> <sup>1</sup> 1 qe þ t 0:5�1 KDC

ki <sup>¼</sup> <sup>K</sup><sup>2</sup>

Table 2 shows the results of the linear regression analysis. The goodness of fit was assessed using error functions presented in Table 1. First, the experimental data were modeled using each of the kinetic models through linear regression. The highest coefficient of determination (R<sup>2</sup> = 0.9981) was produced by the PSO model. This was followed by the DC model (R<sup>2</sup> = 0.9972), PFO model (R2 = 0.9831) and finally the ID model (R<sup>2</sup> = 0.9435). The equation parameters obtained from linear regression were subsequently used to construct the theoretical

Linear regression PFO 0.9831 59.9038 75.6477 898.5153 0.8270 PSO 0.9981 6.7008 13.1281 21.2823 0.9743 ID 0.9435 4.8812 7.1356 7.4810 0.9864 DC 0.9972 1.6146 2.4843 0.9845 0.9981 Nonlinear regression PFO 11.4339 15.1570 34.5790 0.9380 PSO 6.2754 8.3444 10.3636 0.9813 ID 12.9165 17.8090 49.6369 0.9864 DC 1.6035 2.4244 0.9607 0.9981

1 KDC

R<sup>2</sup> RPE MPSD HYBRID R<sup>2</sup>

<sup>1</sup>=<sup>2</sup> <sup>þ</sup> <sup>c</sup> (12)

http://dx.doi.org/10.5772/intechopen.74398

DC=qe (15)

(13)

389

(14)

#### 2.6. Error analysis

The goodness of fit by the various models to the experimental data was evaluated using the coefficient of determination, R2 , the Marquardt's percent standard deviation (MPSD), hybrid error function (HYBRID), mean square error (MSE) and relative percent error (RPE) and is presented in Table 1.
