Adsorption of Copper and Lead Ions in a Binary System onto Orange Peels: Optimization, Equilibrium, and Kinetic Study

Abstract

Agricultural waste materials have been proven to be efficient for heavy metal sequestration from wastewater. In this paper, the interactive effects of initial concentration, adsorbent dosage, and particle size on the removal of copper and lead ions in a binary system onto orange peels were investigated using a central composite design. The pHpzc of orange peels was determined to be 3.85. The Fourier transform infrared (FTIR) and energy dispersive x-ray (EDX) revealed the functional groups and elemental composition present on the surface of the bio-sorbent, respectively, before and after adsorption. The ANOVA showed a good fit with a coefficient of determination (R²) of 0.973 and 0.993 for Cu and Pb, respectively. The bio-sorption of Cu and Pb increased with increasing adsorbent dosage while the percentage removal of Pb was consistently higher than Cu. The highest percentage removal of Cu and Pb gave 86.27% and 98.85%, respectively. The kinetic and isotherm studies showed that pseudo-second-order and Langmuir isotherm models fitted the experimental data suggesting chemisorption and monolayer adsorption, respectively. The treatment of wastewater is very essential to avoid water scarcity and to achieve the Sustainable Development Goals (SDGs). This study demonstrates the potential of utilizing orange peels as bio-sorbent for the treatment of wastewater containing Cu and Pb ions.

1. Introduction

Copper and lead are toxic elements for plant, animal, and human health. These metals are major contaminants in drinking water. They are released into water bodies both naturally and via anthropogenic sources, such as herbicides in agriculture, mining, and industrial activities, which are the major causes of surface and groundwater pollution [1]. The remediation of metal-contaminated wastewater generated from anthropological activities needs adequate management actions from designated authorities; however, mitigation technologies are required to control the natural sources of copper and lead. Lead is a carcinogen while copper is not; however, long-term exposure to copper can be very severe and chronic. These toxic metals can cause health problems in humans when ingested, such as skin diseases, nausea, vomiting, diarrhea, and abdominal disorders. Thus, the removal of these metal ions from wastewater is a serious concern for environmental sustainability and human health. The World Health Organization (WHO) and the European Union (EU) have set the permissible limit for copper and lead in drinking water at 2 and 0.01 ppm, respectively [2]. In addition, the South African National Standard (SANS) has set the maximum limit for copper and lead in wastewater as 1 mg/L [3,4].

The conventional methods used for the removal of toxic metal ions from wastewater include ion exchange, coagulation, precipitation, electrochemical, membrane separation, and so on. Among these techniques, adsorption has been proven to be more effective due to its ease of operation, low cost, and simple design [5]. Recently, researchers have focused on the use of agricultural wastes as adsorbents for the removal of heavy metals from wastewater. These agricultural wastes, also referred to as green adsorbents, are readily available, cheap, and eco-friendly. These wastes include shells, hulls, and peels from fruits and nuts, sawdust, corncob waste, sunflower stalks, and straw [6]. The bio-sorption process depends on the metal and the properties of the biomass surface. Many researchers have reported the functional groups present on the surface of bio-sorbents and how it affects the adsorption mechanism.

Citrus is the largest fruit in the world with an annual production of more than 124.3 million tons worldwide. After consumption, about 40–60% of the fruit is discarded as unwanted waste. Yearly, 110–120 million tons of citrus waste are generated from citrus processing industries worldwide, which creates serious environmental challenges [7]. Presently, a huge amount of the waste is not utilized nor processed but consumed by animals as feed materials or dumped on sites. The citrus waste contains cellulose and hemicellulose with functional groups that enhance the adsorption of heavy metal ions from wastewater. Hence, the utilization of citrus waste as adsorbents will ensure environmental sustainability, which is one of the sustainable development goals. Citrus waste is, therefore, an inexpensive and sustainable resource biomaterial for laboratory and industrial research. This study is focused on the removal of copper and lead ions in the binary system from wastewater using orange peels by applying the central composite design (CCD) method.

Many physical and chemical factors affect the adsorption process of heavy metals from wastewater, which are pH, initial metal ion concentration, temperature, adsorbent dosage, particle size, etc. These factors control the overall performance of a bio-sorption process. Many studies have reported on the effect of different operating parameters on the bio-sorption of heavy metal ions; however, many of the studies have investigated one factor at a time (OFAT). This method wastes time and materials. In addition, many studies have focused on a single solute system; however, it is very rare for wastewater to contain a single metal ion. Even though many studies have been reported on the application of CCD in the field of environmental pollution, the interactive effect of different operating parameters in the binary solute system using CCD is an area of interest. In this study, the CCD was used to study the interactive effect of the operating parameters, such as initial metal ion concentration, adsorbent dosage, and particle size, on the percentage removal of copper and lead in a binary solute system.

In addition, the isotherm and kinetic studies were conducted to determine the adsorption mechanism and the rate of the adsorption process. For an industrial wastewater treatment plant, the rate of adsorption and the sorption efficiency must be investigated. This helps to determine the size of the column for industrial scale. A small column is required for a fast adsorption kinetic while a column with good height is necessary for a slow sorption kinetic. The equilibrium and kinetic parameters were determined using isotherm models (Langmuir and Freundlich) and kinetic models (pseudo-first order and pseudo-second order).

2. Materials and Methods

2.1. Preparation of Bio-Sorbent and Characterization

Ripened orange peels were collected from a local market in Durban, South Africa. The preparation processes were carried out as discussed by [8]. The Fourier transform infrared spectroscopy (FTIR) (Perkin Elmer, Frontier, Waltham, MA, USA) was used to determine the functional groups present on the surface of the bio-sorbent before and after adsorption. The surface morphology of the bio-sorbent before and after adsorption was determined using a scanning electron microscope (SEM) (FEI Nova NanoSEM 230, Lausanne, Switzerland) supported by energy-dispersive X-ray spectroscopy (EDS) (Oxford X-Max detector, Oxford, UK and INCA software v7.3, Stuttgart, Germany).

2.2. Preparation of the Synthetic Solution

A 1000 mg/L solution containing a mixture of copper and lead was prepared in a volumetric flask by dissolving 3.8 g of Cu(NO₃)₂.3H₂O and 1.6 g PbNO₃ in distilled water. The chemicals were of analytical grade and purchased from Laboratory Analytical Supplies Limited, South Africa. The desired concentration needed for each experimental run was obtained from the prepared stock solution by dilution. A digital pH meter (edge pH HI 2002) was used to determine the pH value of the solution while 0.1 M H₂SO₄ or 0.1 M NaOH was added in drops to obtain the desired pH value.

2.3. Batch Adsorption Studies

The batch experiments were conducted at room temperature. All experiments were carried out using a 250 mL conical flask containing a 100 mL mixed solution of copper and lead nitrate ions with varying initial concentrations (10–100 mg/L). The adsorbent dosage and the particle size also varied from 0.1–1 g and 75–455 μm, respectively. The pH of the solution was maintained at 5, which is the optimum pH obtained for the single solution systems [8]. The solution mixing was achieved using a linear shaker (Orbital shaker 262, Johannesburg, South Africa) with an agitation speed of 180 rpm and contact time of 120 min. After adsorption, the supernatant portion of the solution was filtered using Whatman filter paper and syringe filters of 0.45 μm. Thereafter, the samples were analyzed using a micro-plasma atomic emission spectrophotometer (MP-AES, MY 18379001, Agilent, Santa Clara, CA, USA). The percentage removal of the metal ions in the solution and the quantities of metal ions adsorbed onto orange peels were calculated using Equations (1) and (2), respectively.

$$\% Removal=\frac{\left(C_0-C_e\right)}{C_0}\times 100$$

(1)

$$q_t=\frac{\left(C_0-C_e\right)}{m}\mathrm{V}$$

(2)

where $C_0$ and $C_e$ are the initial and final concentration (mg/L), respectively, $q_t$ is the amount of metal ion adsorbed (mg/g), m and V are the mass of the adsorbent (g), and the volume of the solution (mL), respectively.

2.4. Bio-Sorption Equilibrium

Adsorption isotherm study is one of the methods of comparing the amount of metal ion adsorbed during the adsorption process. Langmuir and Freundlich isotherm models are suitable for predicting the experimental equilibrium parameters of a bio-sorption process and these models are mostly used by researchers [9]. The isotherm experiments were conducted at a pH of 5 while 0.5 g of orange peels bio-sorbent was added in the solution containing Cu and Pb with an initial concentration of 10, 25, 50, 60, 80, 100, 125, 150, and 200 mg/L. All the isotherm experiments were conducted in triplicate, and the average of the values was reported.

2.4.1. Langmuir Isotherm

The Langmuir isotherm model assumes that the surface of the bio-sorbent is homogenous, and bio-sorption occurs in a single surface layer. The bio-sorption process is uniform, and the active sites are equal [10]. The model equation is expressed in Equation (3).

$$q_e=\frac{q_{max}b{C}_e}{1+b{C}_e}$$

(3)

where $q_e$ is the quantity of metal ions adsorbed at equilibrium,$q_{max}$ is the bio-sorption capacity (mg/g), and b is the Langmuir isotherm constant (L/mg). The dimensionless factor $R_L$ expression is presented in Equation (4). The value of $R_L$ is used to determine whether the bio-sorption process is favorable (0 < $R_L$ < 1), unfavorable ($R_L$ > 1), or irreversible ($R_L$ = 0) [11].

$$R_L=\frac{1}{1+b{C}_0}$$

(4)

2.4.2. Freundlich Isotherm

The Freundlich isotherm assumes the surface of the bio-sorbent is heterogeneous and the surface-active site energy of the bio-sorbent is different [12]. The Freundlich isotherm is presented in Equation (5).

$$q_e=K_f{C}_e^{1/n}$$

(5)

where $K_f$ (mg/g) and $n$ are the Freundlich constants. The value of $n$ suggests the bio-sorption condition. The value $n<1$ means the bio-sorption process is unfavorable, $n>1$ means the bio-sorption process is favorable, and $n=1$ indicates the bio-sorption process is linear [13].

2.5. Adsorption Kinetic Studies

The adsorption kinetic study explains the adsorption reaction with time at the solid-solution interface. The kinetic studies were conducted with an initial concentration (10, 55, and 100 mg/L) of solution containing Cu and Pb with an adsorbent dosage of 1 g. The samples were taken at an interval of 0.25, 0.5, 1, 2, 3, 4, 5, 6, and 24 h. The two most popular adsorption kinetic models, pseudo-first-order and pseudo-second-order models, are used in this study. The pseudo-first-order model expression is given below:

$$\log \left(q_e-q_t\right)=\log q_e-\frac{K_1}{2.303}t$$

(6)

The pseudo-second-order model is expressed as follows:

$$\frac{t}{q_t}=\frac{1}{K_2{q}_e^2}+\frac{t}{q_e}$$

(7)

where $q_e$ and $q_t$ (mg/g) are the quantity of metal ions adsorbed at equilibrium and at any time t. $K_1$ and $K_2$ are the pseudo-first-order (min⁻¹) and pseudo-second-order (g mg⁻¹ min⁻¹) rate constant, respectively, and t is the contact time (min).

2.6. Central Composite Design (CCD)

The design expert software (V.11.0.5.0, Stat-Ease Inc., Minneapolis, MA, USA) was used to generate the statistical design of the experimental runs. The experimental design was performed using face-centered CCD adapted from the RSM using three factors at three levels (−1, 0, +1) and six center points or replicate runs for the operating parameters. The three operating parameters selected for the bio-sorption of Cu²⁺ and Pb²⁺ ions in binary solute system onto orange peels are initial metal ion concentration, adsorbent dosage, and particle size at specified factor levels (

Table 1. Operating parameters, ranges, and levels.

Parameters	Factor	Range and Level
		−1	0	+1
Initial concentration	X1	10	55	100
Adsorbent dosage	X2	0.1	0.55	1
Particle size	X3	75	265	455

). The CCD generated a total of 20 experimental runs with six replicates using a face-centered design. In the optimization study, the second-order polynomial equation was used to explain the interactive effects of the independent variables. The quadratic model used to optimize the process variables is shown below.

$$Y={\beta }_o+{{\displaystyle \sum }}_{i=1}^k{\beta }_i{X}_i+{{\displaystyle \sum }}_{i=1}^k{\beta }_{ii}{X}_i^2+{{\displaystyle \sum }}_{i=1}{{\displaystyle \sum }}_{j=i+1}{\beta }_{ij}{X}_i{X}_j+Ɛ$$

(8)

where Y is the predicted response; X_i and X_j are the independent variables; β_o, β_i, β_ii, and β_ij are the regression coefficients; and Ɛ is the residual error. The interpretation of the experimental results and the significant variables are explained using mathematical functions called the analysis of variance (ANOVA).

3. Results and Discussion

3.1. Point of Zero Charges of Orange Peels (pHpzc)

The point of zero charges (pHpzc) is a surface property usually investigated to determine the potential of a bio-sorbent for the removal of metal ions from an aqueous solution. The point of zero charges of an adsorbent is the point at which the surface of the adsorbent has zero net charges [14]. The pHpzc of an adsorbent gives insight into the ionization potential and the interactive effect between the adsorbent surface and the adsorbate. When the pHpzc is less than the solution pH, adsorption of cations is favored, and when the pHpzc is greater than the solution pH, the adsorption of anions is favored. In Figure 1, the pHpzc of natural orange peels gave 3.85. This means that the surfaces of orange peels are acidic and, therefore, favor the adsorption of cations.

3.2. Characterization of Bio-Sorbent

3.2.1. FTIR Spectroscopy Analysis

The FTIR spectrum is used to identify the functional groups present on the surface of an adsorbent. Adsorption is a physical phenomenon that involves the interaction between the functional groups on the surface of a bio-sorbent and the metal ions in an aqueous solution. These functional groups provide active sites for the adsorption of the metal ions on the surface of the adsorbent. The FTIR spectra represented in Figure 2 show the functional groups present on the surface of orange peels before adsorption. The FTIR of natural orange peels were obtained within the range of 500 to 4000 cm⁻¹ wavenumber.

The spectra of orange peels before and after adsorption show the presence of a broad peak at 3382.62 cm⁻¹, which indicates the presence of an intermolecular bonded O–H group consisting of alcohols and phenols. The peaks at 2918.92 cm⁻¹, 2850.97 cm⁻¹, 1441.94 cm⁻¹, 1374.74 cm⁻¹, and 887.26 cm⁻¹ indicate the presence of a saturated C–H substitution bond, which can be likened to alkanes that are available in the pulp. The peaks at 1734.38 cm⁻¹ and 1607.04 cm⁻¹ reveal the presence of the carboxylic C=O bond and the unsaturated C=C bond, respectively, which are attributed to aldehydes and ketones. The existence of a high quantity of hydroxyl and carboxyl groups on the surface of the bio-sorbent suggests the tendency of orange peels to adsorb positively charged metal ions. The presence of these functional groups plays an important role in the bio-sorption of heavy metals through ion exchange, which occurs as a result of the affinity of metal ions to the functional groups.

A comparison of FTIR spectra of orange peels before and after adsorption is represented in Figure 2. All four patterns look similar, which suggests that the same functional groups are present on the surface of the bio-sorbent before and after adsorption. However, there are significant shifts in the peaks and intensities of the spectrum. These shifts are attributed to the interaction between the functional groups present on the surface of the bio-sorbent and the copper and lead ions in the solution. The significant shift occurred after adsorption in the peaks representing the O–H and C–O–H stretching, which are peculiar to the adsorption of both copper and lead. This shows that hydroxyl and carboxyl groups played a major role in the adsorption of copper and lead.

3.2.2. Scanning Electron Microscope-Energy Dispersive X-ray (SEM-EDX)

The SEM analysis of orange peels before and after adsorption of Cu and Pb was carried out to determine the morphological structure of the bio-sorbent (Figure 3a,b). The surface of the bio-sorbent prior to the adsorption process revealed an uneven and highly irregular surface. Additionally, sharp and coarse particles with varying shapes and sizes can be seen deposited randomly on the surface of the bio-sorbent. After adsorption of Cu and Pb ions, a highly significant change can be observed on the surface structure of the bio-sorbent. The surface structure of orange peel bio-sorbent loaded with Cu and Pb ions in a binary system appeared rough with little deposit of irregular particles distributed on the surface. In addition, the surface appeared shiny, which suggests physicochemical interactive effects of the metal ions with the functional groups present on the surface of the bio-sorbent.

The SEM results were supported by an EDX analysis of orange peel bio-sorbent before and after adsorption as shown in Figure 4a,b, respectively. Figure 4a reveals the chemical composition of orange peel bio-sorbent on a percent weight basis; the surface indicated the presence of carbon (C, 55.78%), oxygen (O, 43.24%), magnesium (Mg, 0.16%), sulphur (S, 0.06%), potassium (K, 0.19%), and calcium (Ca, 0.57%). The EDX spectra of orange peels after adsorption of Cu ions and Pb ions in a binary system are shown in Figure 4b. The EDX spectra of orange peel loaded with copper and lead ions in a binary system indicate the presence of only carbon and hydrogen with Cu and Pb peaks. It is apparent that all other elements except carbon and hydrogen disappeared and were replaced with Cu and Pb. The percentage composition of Pb adsorbed on the surface of orange peel is more than Cu, which suggests that orange peel bio-sorbent has more affinity for Pb²⁺ ions than Cu²⁺ ions. The surface area of an adsorbent also plays an important role in an adsorption process. The BET surface area of orange peel bio-sorbent relative to the particle size 75, 255, and 455 µm gave 9.8471, 6.6383, and 3.5685 m²/g, respectively.

3.3. Experimental Design

3.3.1. Adsorption of Cu²⁺ and Pb²⁺ onto Orange Peels in Binary Solute Using CCD

The experimental design matrix for the biosorption of Cu²⁺ and Pb²⁺ ions onto orange peels is presented in

Table 2. CCD experimental matrix and responses of Pb²⁺ and Cu²⁺ removal onto orange peels.

Std	Run	Initial Conc. (mg/L)	Adsorbent Dosage (g)	Particle Size (µm)	Responses
					Lead % Removal		Copper % Removal
					Exp	Pred	Exp	Pred
15	1	55	0.55	265	83.75	83.93	72.89	71.48
5	2	10	0.1	455	80.34	80.56	59.1	59.06
2	3	100	0.1	75	62.07	61.95	79.5	78.69
17	4	55	0.55	265	82.86	83.93	70.17	71.48
12	5	55	1	265	90.47	91.18	85.06	86.55
4	6	100	1	75	88.12	88.04	84.72	84.77
6	7	100	0.1	455	70.86	71.13	59.95	60.57
20	8	55	0.55	265	84.15	83.93	73.72	71.48
9	9	10	0.55	265	90.12	88.88	72.82	70.86
3	10	100	1	75	98.85	98.71	86.2	85.60
16	11	55	0.55	265	84.45	83.93	72.1	71.48
1	12	10	0.1	75	77.97	78.86	68.98	70.76
19	13	55	0.55	265	83.21	83.93	69.65	71.48
8	14	10	1	455	84.78	84.03	82.65	80.89
18	15	55	0.55	265	84.05	83.93	70.25	71.48
10	16	10	0.55	265	78.15	78.83	69.29	71.19
14	17	55	0.55	455	81.75	81.75	65.98	66.34
13	18	55	0.55	75	83.45	82.90	74.55	74.13
11	19	55	0.1	265	76.06	74.80	70.53	68.98
7	20	100	1	455	86.95	87.21	87.32	88.14

with the predicted and experimental responses. The second-order polynomial Equation (6) with multiple regression analysis was used to generate the responses (percentage removal of Cu²⁺ and Pb²⁺) using the three design factors.

The quadratic regression model shows the model parameters generated for the responses; percentage removal of Pb (Y₁) and percentage removal of Cu (Y₂) written for coded factors as a function of initial concentration (A), adsorbent dosage (B), and the particle size (C) are represented in Equations (9) and (10), respectively.

$${\mathrm{Y}}_1\left(\mathrm{Pb}\right)=83.93-5.03\mathrm{A}+8.19\mathrm{B}+1.56\mathrm{AB}+1.87\mathrm{AC}-3.3\mathrm{BC}-1.61{\mathrm{C}}^2$$

(9)

$${\mathrm{Y}}_2\left(\mathrm{Cu}\right)=71.48+8.79\mathrm{B}-3.89\mathrm{C}-2.19\mathrm{AB}-1.61\mathrm{AC}+3.56\mathrm{BC}+6.28{\mathrm{B}}^2$$

(10)

Model Equations (7) and (8) comprise three main effects (A, B, C), three interactive effects (AB, AC, BC), and three quadratic effects (A², B², C²). The order of the interactive factors to increase the percentage removal of Pb(II) is AC > AB > BC while for percentage removal of Cu(II) is BC > AC > AB.

3.3.2. Analysis of Variance (ANOVA) for the Models

The regression models were evaluated using ANOVA to determine the significant factors that fitted well with the models as presented in

Table 3. ANOVA for % Pb removal using orange peels.

Source	Sum of Squares	df	Mean Square	F-Value	p-Value Prob > F	Comments
Pb Model	1088.25	9	120.92	151.21	<0.0001	significant
A-Initial concentration	252.51	1	252.51	315.76	<0.0001
B-Adsorbent dosage	670.27	1	670.27	838.17	<0.0001
C-Particle size	3.34	1	3.34	4.18	0.0682
AB	19.47	1	19.47	24.35	0.0006
AC	28.05	1	28.05	35.08	0.0001
BC	87.12	1	87.12	108.94	<0.0001
A2	0.0138	1	0.0138	0.0173	0.8980
B2	2.43	1	2.43	3.04	0.1116
C2	7.09	1	7.09	8.87	0.0139
Residual	8.00	10	0.7997
Lack of Fit	6.17	5	1.23	3.39	0.1034	not significant
Pure Error	1.82	5	0.3647
Cor Total	1096.25	19
Std. Dev. 0.8942	R2 0.9927	Adjusted R2 0.9861	Predicted R2 0.9483	Adeq. Precision 58.15	Mean 82.62	C. V.% 1.08

and

Table 4. ANOVA for % Cu removal using orange peels.

Source	Sum of Squares	df	Mean Square	F-Value	p-Value Prob > F	Comments
Cu Model	1230.20	9	136.69	39.32	<0.0001	significant
A-Initial concentration	0.2856	1	0.2856	0.0822	0.7802
B-Adsorbent dosage	772.47	1	772.47	222.22	<0.0001
C-Particle size	151.71	1	151.71	43.64	<0.0001
AB	38.37	1	38.37	11.04	0.0077
AC	20.67	1	20.67	5.95	0.0349
BC	101.39	1	101.39	29.17	0.0003
A2	0.5762	1	0.5762	0.1658	0.6925
B2	108.53	1	108.53	31.22	0.0002
C2	4.28	1	4.28	1.23	0.2931
Residual	34.76	10	3.48
Lack of Fit	20.79	5	4.16	1.49	0.3365	not significant
Pure Error	13.97	5	2.79
Cor Total	1264.96	19
Std. Dev. 1.86	R2 0.9725	Adjusted R2 0.9478	Predicted R2 0.7934	Adeq. Precision 22.06	Mean 73.77	C. V.% 2.53

. The ANOVA helps to identify the factors that had no significant influence on the model and the adsorption process. The models gave a 95% confidence level, 5% significance level, and a degree of freedom of 9. All the model features, the sum of squares, F-values (Fisher variation ratio), p-values, adequate precision, and the lack of fit values, were acceptable and show the significance of the models. The p-values of less than 0.05 shows that the regression model terms are highly significant. The p-values of the regression models representing percentage removal of Pb²⁺ and Cu²⁺ gave <0.0001, which implies that the regression model equation fitted well with the experimental data. The lack of fit value greater than 0.05 signifies that the models have one or more terms that have no significant influence on the regression models because of pure error or noise. However, such terms were included in the model to justify the parent terms and the interactive behavior. In the case of percentage removal of Pb²⁺, the significant model parameters are initial concentration A, adsorbent dosage B, interactive terms (AB, AC, BC), and quadratic term C² while particle size C and quadratic terms (A² and B²) are not significant. The significant model parameters for the percentage removal of Cu²⁺ are adsorbent dosage B and particle size C, which have a first-order main effect, interactive terms (AB, AC, BC), and quadratic term B² while initial concentration A and quadratic terms (A², and C²) are not significant.

The overall performance of the models was evaluated based on the regression coefficient of determination (R²), the adjusted R², and the predicted R². The R² helps to ascertain the closeness of the experimental values to the predicted values, which range from 0 to 1, where 0 denotes no correlation between the data. The R² obtained for the regression models was found to be close to 1, which implies a good fit between the experimental and the predicted data as represented in Figure 5. The predicted R² and the adjusted R² for the regression models of Pb²⁺ and Cu²⁺ are in reasonable agreement with a difference of less than 0.2, which validates the significance of the models. The R² values for the models were found to be 0.9927 and 0.9725 for percentage Pb²⁺ removal and percentage Cu²⁺ removal, respectively. The adequate precision of the models, which is a measure of the signal-to-noise ratio, was desirable for a ratio greater than 4.

3.3.3. D Representation of the Interactive Effects on the Responses

The graphical representation of the interactive effects of the model parameters on each of the responses is shown in 3D plots depicted in Figure 6a–c. The surface plots were generated by writing code using MATLAB (MATLAB R2019a). Figure 6a shows the interaction between initial concentration and adsorbent dosage with the percentage removal of Pb²⁺ and Cu²⁺ ions. The bio-sorption capacity of Pb²⁺ increased with increasing adsorbent dosage while an increase in the initial concentration had slight changes in the adsorption capacity. This suggests that the surface of the bio-sorbent has reached the saturation point. In the case of the percentage removal of Cu²⁺, the initial concentration and the adsorbent dosage increased with increasing percentage removal. The percentage removal of Pb²⁺ and Cu²⁺ increased with the increasing dosage of orange peels. This result is reasonable because a little amount of bio-sorbent relates to a small active site, and since the surface of the bio-sorbent is acidic, more active sites must be occupied for proton-metal ion competition.

Figure 6b shows the interactive effect of initial concentration and particle size on the percentage removal of Pb²⁺ and Cu²⁺. The highest bio-sorption capacity of Pb²⁺ and Cu²⁺ gave 98.85% and 87.32%, respectively, at the initial concentration of 100 mg/L, while the particle size had no significant changes in the adsorption of Pb²⁺. On the contrary, the percentage removal of Cu²⁺ increased with increasing initial concentration and particle size.

Figure 6c depicts the interaction between the adsorbent dosage and the particle size on the percentage removal of Pb²⁺ and Cu²⁺. The bio-sorption capacity of Pb²⁺ and Cu²⁺ ions increased with increasing adsorbent dosage while the particle size had little effect on the adsorption efficiency of the metal ions. The intersection of the two graphs shows that the maximum percentage removal of Pb²⁺ and Cu²⁺ ions in the binary system was reached with a particle size of 75 microns. This result is significant because smaller particle sizes have a larger surface area and enhance adsorption capacity better than bigger particle sizes. Furthermore, Figure 6a–c showed that the percentage removal of Pb²⁺ was higher than Cu²⁺ with all the interactions, which are a result of the reactivity of Pb²⁺ over Cu²⁺ [15]. Hence, Pb²⁺ ions were more adsorbed in the binary system than Cu²⁺ ions, as represented in Table 2. In conclusion, adsorbent dosage had the highest influence on the percentage removal of Pb²⁺ and Cu²⁺ ions in the binary system followed by the initial concentration while particle size had little or no significant effect.

3.3.4. Optimization of the Adsorption Process

The goal of optimization is to obtain the optimal values for the operating parameters involved in a process. The purpose is to maximize the percentage removal of the metal ions to achieve the highest optimum removal. The desirability of the solution is used to evaluate the suitability of the optimum condition obtained. The optimization also helps to determine the performance and capacity of the adsorbent in the binary solute system. The optimized variable condition for the bio-sorption of Cu²⁺ and Pb²⁺ ions in the binary system using orange peels gave 98.85% and 86.27% removal for Pb²⁺ and Cu²⁺ ions, respectively, with an initial concentration of 100 mg/L, an adsorbent dosage of 1 g, and particle size of 75 µm. The desirability of the solution gave 0.895, which signifies that the optimum condition is reasonably acceptable.

3.3.5. Mechanism of Adsorption of Cu²⁺ and Pb²⁺ Ions

The bio-sorption uptake of Cu²⁺ and Pb²⁺ ions onto orange peels was achieved by the combination of the operating parameters. The functional groups identified on the surface of orange peels played a significant role in the bio-sorption process. The major shifts in the peaks representing –COOH and –OH after adsorption of Cu²⁺ and Pb²⁺ confirmed ion exchange as the adsorption mechanism. These functional groups become deprotonated at a solution pH higher than the pHpzc of the bio-sorbent, which favors adsorption uptake of cations. In addition, the displacement of the metals present on the surface of orange peels after adsorption (Figure 3) also revealed ion exchange as the adsorption mechanism responsible for the bio-sorption of Cu²⁺ and Pb²⁺ in a binary system using orange peels.

3.4. Equilibrium Study

The adsorption process for experimental data of Cu²⁺ and Pb²⁺ onto orange peels were fitted with Langmuir and Freundlich isotherm models. A very important guide to determining the best isotherm is to fit the experimental data with different isotherm models for estimation, and then compare the correlation coefficient (R²) values obtained [16].

The Langmuir isotherm model is a semi-empirical model that explains the adsorption mechanism based on the assumption that the surface of the bio-sorbent is energetically homogenous and adsorption energy is uniform for all sites [17]. The isotherm studies were performed with a fixed amount of bio-sorbent but varying initial concentrations. The quantity adsorbed for the binary adsorption system of Pb²⁺ and Cu²⁺ using orange peels is presented in

Table 5. Langmuir and Freundlich isotherm parameters.

Ion	System	Langmuir				Freundlich
		b	qm	R2	RL	Kf	n	R2
		(L/mg)	(mg/g)
Cu2+	Binary	0.15	38.18	0.988	0.4	5.19	1.56	0.934
Pb2+	Binary	0.77	40.05	0.998	0.02	24.06	4.29	0.898

.

Figure 7a shows the experimental data for the adsorption isotherm of Cu²⁺ and Pb²⁺ in the binary system as well as the linear fits for the Langmuir isotherm model. The adsorption of Cu²⁺ and Pb²⁺ increased with increasing initial metal ion concentrations. The Langmuir isotherm constant “b” is higher for Pb²⁺ (0.77 L/mg) than Cu²⁺ (0.15 L/mg), which buttresses the fact that Pb²⁺ was more adsorbed than Cu²⁺. In addition, the quantity adsorbed of Pb²⁺ was higher than Cu²⁺. Therefore, Pb²⁺ has a higher affinity for the active sites on the surface of orange peels. Comparing the correlation coefficient R² value for Langmuir and Freundlich isotherm models (Table 5), it is obvious that the Langmuir isotherm fitted the experimental data well for both Cu²⁺ and Pb²⁺ with values of R² very close to 1. Another important characteristic of the Langmuir isotherm model can be represented by a dimensionless factor R_L, also known as the separation factor. This is used to determine the adsorption behavior of the bio-sorbent as presented in Table 5. The dimensionless factor, R_L, for the adsorption of Pb²⁺ and Cu²⁺ in binary system onto orange peels falls within the range of 0 < R_L < 1, which implies that the adsorption process is favorable within the concentration range studied. Hence, the orange peel is efficient for the adsorption of Pb²⁺ and Cu²⁺ ions from aqueous solutions.

The Freundlich isotherm assumes the surface of an adsorbent is heterogeneous, and adsorption energy is exponentially distributed [18]. Figure 7b represents the plot of the experimental data and the Freundlich isotherm linear fit of Pb²⁺ and Cu²⁺. The parameters K_F and n are the Freundlich isotherm constants describing the adsorption capacity and intensity, respectively. The measure of the exponent ‘n’ explains the adsorbent–sorbent phenomenon capacity and favorability [19]. Table 5 shows the values of n and the interpretation. The Freundlich isotherm constant “n”, which evaluates the adsorption intensity of the metal ions was determined to be greater than 1 and in the range 1 < n < 10 for Pb²⁺ and Cu²⁺ in the binary system. This shows that the adsorption process is favorable and with high chemisorption. This implies there exist great bonds between the adsorbent and the adsorbate because of the chemisorption reactions.

3.5. Kinetic Modelling of Pb²⁺ and Cu²⁺ onto Orange Peels

The kinetic study of Pb²⁺ and Cu²⁺ onto orange peels in binary systems with the effect of time at different initial concentrations (10, 55, and 100 mg/L) was investigated using the linearized pseudo-first-order and pseudo-second-order model. The calculated constant parameters obtained from the pseudo-first-order and pseudo-second-order linear models using orange peels are presented in

Table 6. Pseudo-first-order model parameters for the adsorption of Cu²⁺ and Pb²⁺ using orange peels.

Metal Ion Concentration		Cu			Pb
		10 mg/L	55 mg/L	100 mg/L	10 mg/L	55 mg/L	100 mg/L
Pseudo-first order	Binary
qe		6.83E6	465.27	2087.37	0.05	14.72	2.18
K1		48.96	6.96	10.59	8.83	11.06	0.33
R2		0.958	0.762	0.897	0.970	0.891	0.924
Pseudo-second order	Binary
qe		42.48	9.49	13.39	3.82	15.96	17.09
K2		0.02	0.44	0.04	10.71	0.55	0.30
R2		0.999	0.999	0.999	1.000	0.999	0.999

. The effect of time on different initial concentrations was studied in the binary system containing Cu²⁺ and Pb²⁺. Figure 8a represents the plot of $\log \left(q_e-q_t\right)$ against t for the binary system of Cu²⁺ and Pb²⁺ using orange peels. The correlation coefficient (R²) for the binary solute Cu²⁺ and Pb²⁺ pseudo-first-order model was in the range of 0.762 to 0.958. The pseudo-second-order model showed that the quantity of Pb²⁺ adsorbed is higher than Cu²⁺ for all the initial concentrations. The pseudo-second-order constant K₂ value for Pb²⁺ was higher than Cu²⁺ signifying that adsorption of Cu²⁺ is negatively affected in the coexistence of Pb²⁺. The correlation coefficient for the pseudo-second order was observed to be very close to 1 for Cu²⁺ and Pb²⁺ at the different initial concentrations, which suggests that the adsorption rate is chemically controlled. Figure 8b shows the graph representing the linear form of the pseudo-second-order model of Cu(II) and Pb(II) in a binary system using orange peels. It can be observed that the graph started from the origin, indicating the good fit of the model.

3.6. Comparison of Cu²⁺ and Pb²⁺ Adsorption Capacity Using Orange Peels with Other Bio-Sorbents

The maximum uptake of Cu²⁺ and Pb²⁺ ions in binary solution is 38.18 mg/g and 40.05 mg/g, respectively. The adsorption uptake of Pb²⁺ was higher than Cu²⁺, which implies that orange peels have a higher affinity for Pb²⁺ than Cu²⁺. The maximum uptake of the metal ions obtained in this study is compared with other results reported using bio-sorbents as presented in

Table 7. Comparison of Cu²⁺ and Pb²⁺ ions uptake capacity with other bio-sorbents.

Bio-Sorbent	Cu2+ Uptake (mg/g)	Pb2+ Uptake (mg/g)	Reference
Orange peels	38.18	40.05	This study
Mango plant	22.51	24.4	[20]
Banana peels	28	7.97	[21]
Fumaria Indica	6.62	9.15	[22]
Banana peels	29.26	39.32	[23]
Cabbage waste	12.96	61.27	[18]
Orange peels	33.99		[24]
Orange peels	16.64		[25]
Orange peels	31.79		[26]

.

4. Conclusions

The adsorption of Cu²⁺ and Pb²⁺ in a binary system from an aqueous solution using orange peels as a low-cost bio-sorbent was experimentally investigated in a batch mode. The interactive effect of the operating parameters, such as initial metal concertation, adsorbent dosage, and particle size, was examined using CCD. The adsorption capacity of Pb²⁺ was consistently higher than Cu²⁺ in all the experimental runs. The percentage removal increased with an increasing adsorbent dosage, which is attributed to the increase in the active sites available on the surface of the bio-sorbent. The adsorption of Cu²⁺ and Pb²⁺ occurred at a pH higher than the pHpzc of orange peels, which is 3.85. This reveals that the adsorption of cation is favored at pH > pHpzc of the adsorbent. The EDX analysis also showed that potassium, magnesium, and calcium disappeared on the surface of the bio-sorbent after adsorption. Hence, ion exchange is confirmed as the adsorption mechanism for the bio-sorption of Cu²⁺ and Pb²⁺.

The equilibrium and kinetic experimental results fitted well with Langmuir and pseudo-second-order models, respectively. This suggests that adsorption occurred on the monolayer, and the adsorption process has high chemisorption. Overall, this study suggests that orange peels are an efficient and inexpensive adsorbent suitable for the removal of Cu²⁺ and Pb²⁺ from wastewater in a bi-solute system.