Olive oil mill wastewater (OMWW) poses an undeniable environmental problem due to its high organic loads and phenolic compounds (PCs) content. This study determined the optimal conditions for preparing a new bio-sorbent from olive pomace (OP) and the adsorptive treatment of OMWW by this bio-sorbent. The activation reaction was performed with hydrogen peroxide. The results of the combination effect optimization of the three preparation variables: the activation temperature (°C) X1, the activation time (min) X2 and the impregnation ratio X3, are presented by the response surface methodology (RSM). The maximum adsorption capacity was obtained at activation time 300 min, temperature 80 °C and ratio equal to 6.2:1. The bio-sorbent was characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffractometer (XRD). The adsorption process performance of this bio-sorbent was examined in batch and fixed-bed columns. An adsorption capacity of 446 mg g-1 has been achieved for 4000 mg L-1 concentration of PCs. The adsorption isotherm and kinetics were consistent with the Langmuir and pseudo-second-order models. Therefore, the Thomas model best fit the fixed bed column experimental data. The bio-sorbent gave a high desorption percentage of PCs, which was above 70% using HCl (0.1M).
Research Article
Optimization of preparation conditions of a novel low-cost natural bio-sorbent from olive pomace and column adsorption processes on the removal of phenolic compounds from olive oil mill wastewater
https://doi.org/10.21203/rs.3.rs-1314058/v1
This work is licensed under a CC BY 4.0 License
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Bio-sorbent
Hydrogen peroxide
Olive oil mills wastewater
Olive pomace waste
Phenolic compounds
Regeneration
The cultivation of the olive tree is part of the Mediterranean tradition. It is a symbol of civilization among the Mediterranean peoples and, at the same time, a favorite dish of their table. In addition to its main product, oil leaves two by-products: the OP and the vegetation water, also called OMWW. However, these effluents have a low economic value in Morocco (Lissaneddine et al. 2021).
OMWW is viscous, acidic and rich in organic matter (total sugars, nitrogenous substances, organic acids, PCs, fats) (Achak et al. 2009; Lissaneddine et al. 2021). Therefore, the OMWW poses an undeniable environmental problem because of their high chemical oxygen demand (COD) content and the presence of PCs. Since these PCs cause inhibition of seed germination and plant growth (Elayadi et al. 2019). Consequently, also affect groundwater, surface water, and soil (Dhouib et al. 2006). However, these PCs have economic value if recovered, as they are characterized by a high antioxidant activity (Aissa et al. 2017). In addition, those PCs are considered as additives for numerous consumable products (Ciriminna et al. 2017) and cosmetic uses (Galanakis et al. 2018). Therefore, the main appeal in PCs offers an opportunity for high-added-value by-products that may reimburse the treatment expense of the OMWW and give a considerable edge for earnings.
Different techniques for PCs removing from OMWW have been studied (adsorption, coagulation-flocculation, artificial wetlands, distillation, infiltration-percolation and distillation) (Achak et al. 2009; Leone et al. 2017; Elayadi et al. 2019; Papaoikonomou et al. 2019). But these processes show several limitations, such as being more expensive, generating large amounts of sludge and incomplete removal of pollutants. However, researchers have focused on the adsorption technique, which is considered the cheapest, simple and most effective method for removing and recovering different types of pollutants (Boumya et al. 2021; Lissaneddine et al. 2021).
For large scale applications, it's mandatory to carry out continuous flow treatment using a column model to evaluate the intended contact time in which the adsorbate attain an equilibrium (Aziz et al. 2020). Fixed-bed processes have advantages such as ease of operation, high efficiency, lowest cost, large wastewater flows can be treated by an adequate amount of porous material, and the possibility of regeneration (Aksu and Gönen 2004; Lakshmipathy and Sarada 2015; Aziz et al. 2020).
In similar recent works, they studied the activation of olive pomace (OP) by CuO (Yuney et al. 2020), NaOH (Lissaneddine et al. 2021) and KOH (Şirazi and Aslan 2021) to adsorb PCs from OMWW. However, those works performed a carbonization step at an elevated temperature during the adsorbent preparation. Moreover, this adsorbent was only tested under batch conditions on diluted OMWW. These reasons make their adsorbent difficult to prepare, and it isn't easy to apply in the industry. Therefore, it is suggested in the present study to use raw OMWW and bio-sorbents activated with hydrogen peroxide only in an activation step without carbonization.
The objective of the present work was to prepare a bio-sorbent from OP and then employ it as a low-cost adsorbent for the recovery of PCs from OMWW, which represent a green circular economy approach. First, MINITAB software was used to determine the optimal conditions for the preparation of the bio-sorbent. Then, batch adsorption experiments were performed under different operational conditions (contact time, initial solution pH, initial PCs concentration and temperature). Finally, a fixed bed reactor was used to remove and regenerate the bio-sorbent, targeting a large-scale application.
1. Sample collection and characterization physic-chemical of OMWW
The OMWW samples were collected in the region of Marrakech-Safi of Morocco during the 2020/2021 olive oil extraction season.
Measurements of pH and electrical conductivity are performed by a probe Hanna HI 9829. The chemical oxygen demand (COD) was estimated by oxidation with an excess of potassium dichromate in a hot and acidic environment according to the standard (AFNOR T 90-101). The Kjeldahl nitrogen, Nitrite, Nitrates, Orthophosphate, Total phosphorus and Sulfate are determined according to the standards AFNOR T90-110, AFNOR T90-013, RODIER 1984, AFNOR T90-022, AFNOR T90-023 and RODIER 7th EDITION, respectively. The concentration PCs were determined by the Folin-Ciocalteu method (Singleton and Rossi, 1965) using caffeic acid as the standard.
2. Preparation of bio-sorbent
In this study, we used an OP waste was located in the region of Marrakech-Safi, Morocco. After washing with distilled water, the OP was dried in an oven at 105°C (SF7/S Stuart, United Kingdom) for 24 hours. Hydrogen peroxide H2O2 is a chemical agent used to produce a bio-sorbent with chemical activation. OP samples were mixed by stirring with 250 g of hydrogen peroxide H2O2 solution (10%) at different temperatures under stirring. The resulting bio-sorbent was washed with distilled water and dried in an oven at 60°C for 24 hours.
3. Characterization of bio-sorbent
The physicochemical characteristics of the bio-sorbent were studied using XRD. FTIR (ALPHA-P, Germany) was used to characterize the functional groups in the wavelength range of 400–4000 cm−1. In addition, pore size distribution, specific surface area and pore volume were evaluated by N2 sorption measurements using Brunauer Emmett and Teller SBET. Examination of the grain morphology of the bio-sorbent was performed using SEM measurements (Fuveau, France), which were taken with a scanning microscope, The analysis of elements on the micro-scale and element distribution on the bio-sorbent by EDS analyzer (TEAM™ EDS).
4. Experimental setup and procedure
4.1. Batch adsorption and isotherm studies
The bio-sorbent prepared was studied as an adsorbent for the adsorption of PCs from OMWW. The effect of contact time on the adsorption process of PCs was evaluated in the range of 0 - 240 min at optimum values of adsorbent dose 25 mg, initial concentration 4000 mg L−1, at pH 4.0 and temperature 25°C with a stirring speed of 200 rpm (KS 3000i control, Germany). Concerning the study of the PCs concentration effect on the adsorption capacity, a series of concentrations was carried out 2000, 3000 and 4000 mg L−1, for 240 min and at pH 4.0. This corresponds to the effect of pH; OMWW was adjusted to values ranging from 2.0 to 12.0 by adding hydrochloric acid (HCl) or sodium hydroxide (NaOH) of equal concentrations to 1.0N. To study the effect of temperature, the adsorption process was performed at temperatures of 20, 40 and 60°C. In an adsorbent dose of 25 mg, a volume of 25 mL of OMWW, at a concentration of 4000 mg L−1 and pH 4.0.
The adsorption capacity was determined according to equation (eq. (1)):
\(q=\frac{({C}_{0}-{C}_{t})}{W}*V\)(Eq. 1)
Where, Ct (mg L−1) is the PCs concentration after a certain time of contact with bio-sorbent, Co (mg L−1) is the initial concentration of PCs, V is the volume of solution (L) and W is the amount of bio-sorbent (g).
4.1.1. Langmuir model
Langmuir model is based on several assumptions and adsorption is limited to monolayer adsorption. The energy of adsorption is assumed to be the same all over the surface; no interactions between adjacent molecules on the surface and molecules adsorb at fixed sites and do not migrate over the surface as given by the equation (eq. (2)). As a result, the Langmuir isotherm equation has the following by Okeola and Odebunmi (2010) and Aliakbarian et al. (2015):
\(\frac{{C}_{e}}{{q}_{e}}=\frac{1}{{K}_{l}*{q}_{m}}+\frac{{C}_{e}}{{q}_{m}}\)(Eq. 2)
Where, qe (mg g−1) is the amount of PCs adsorbed at equilibrium; Ce (g L−1) is the concentration of PCs at equilibrium; KL is the Langmuir constant related to the energy of adsorption (L g−1); and qm (mg g−1) is the maximum adsorption capacity.
4.1.2. Freundlich model
The Freundlich isotherm mainly describes the adsorption equilibrium of sorption on a heterogeneous surface and is given by the equations (eq. (3)). The Freundlich isotherm equation is expressed by Okeola and Odebunmi (2010) and Aliakbarian et al. (2015):
\(log{q}_{e}=\text{log}{K}_{F}+\left(\frac{1}{n}\right)\text{log}{C}_{e}\)(Eq. 3)
Where, n is the adsorption intensity constant, KF is the Freundlich constant indicating adsorption capacity.
4.2. Column adsorption and isotherm studies
The experiment column adsorption was conducted using a tube with a height of 30 cm and a diameter of 0.5 cm. The bio-sorbent dose 4.5 g was introduced into the column. The OMWW was introduced in ascending flow using a pump (Antlia - 3C Dutscher, France) at a constant flow rate of 0.5 mL min−1. All experiments are carried out at a temperature of 60°C for 120 min, with samples taken every 15 min.
4.2.1. Thomas model
The Thomas model (Thomas, 1944) is based on the assumption that the adsorption is of the Langmuir type; the adsorption kinetics obey reversible second order kinetics. This model applies to a system with a constant flow rate and without axial dispersion. This model is given by equation (eq. (4)) (Aksu and Gönen 2004):
\(\frac{C}{{C}_{0}}\)= \(\frac{1}{(1+exp?({K}_{Th}*{q}_{Th}* \frac{m}{Q}-{K}_{Th}*{C}_{0}*t) }\) (Eq. 4)
Where, kTH (mL min−1 mg−1) is the Thomas rate constant; q0 (mg g−1) is the adsorption capacity; m total amount of bio-sorbent sent to the column (g); and Q is the flow rate that circulates through the column (mL min−1).
4.2.2. Yoon-Nelson model
The Yoon and Nelson model (Yoon and Nelson 1984) assumes that the decrease in adsorption probability for each adsorbate molecule is proportional to the solute adsorption probability and the adsorbate breakthrough probability (Gong et al. 2015). The Yoon-Nelson equation is written as follows:
\(\frac{C}{{C}_{0}}=\frac{exp?({K}_{YN}*t- *{K}_{YN})}{1+exp?({K}_{YN}*t-*{K}_{YN})}\)(Eq. 5)
Where, kYN the Yoon-Nelson rate constant (min−1) and τ is the time required for 5 % adsorbate breakthrough (min).
5. Desorption
Desorption gives the bio-sorbent a chance for reuse. The desorption tests were done in the same way as the adsorption experiments. First, the bio-sorbent was saturated with PCs and then washed with distilled water in the first series. Next, desorption of the bio-sorbent was done with 0.1M HCl solution at 60°C. Then, the bio-sorbent is reused for further PCs adsorption tests. Finally, the desorbed amount of PCs was calculated by equation (eq. (6)).
Desorbed = \(\frac{{q}_{r}}{{q}_{0}}\)*100% (Eq. 6)
Where, qr and q0 (mg g−1) are adsorption capacities of the PCs after and before regeneration, respectively.
6. Statistical analysis
The effect of process parameters on the optimal bio-sorbent preparation conditions and adsorption capacity was analyzed using the MINITAB statistical software.
The MINITAB software is a statistical technique used to identify significant parameters to determine the optimal bio-sorbent preparation conditions. The three parameters chosen for the experiment were the activation temperature X1, activation time X2 and rate X3 rate. Each parameter has three different levels (coded as -1, 0 and +1): Activation temperature (60, 70 and 80°C), activation time (180, 240 and 300 min), and rate (3.1:1, 4.6:1 and 6.2:1) (Table 1). The three parameters studied yielded twelve experimental designs.
Modeling of the optimal bio-sorbent preparation conditions was based on the second-order polynomial equation (eq. (7)):
(Eq. 7)
Where, b0 the constant, bi the linear effect of input factor xi, bij the linear by a linear interaction effect between input factors xi, xj, bij the quadratic effect of input factor xi, n number of factors.
In addition, the statistical software MINITAB was used to study the effect of operating conditions on the adsorption capacity. Finally, an analysis of variance was performed to determine the significance of the interactions and effects between the operating conditions.
|
Process variables |
Units |
Symbols |
Levels |
||
|---|---|---|---|---|---|
|
-1 |
0 |
1 |
|||
|
Activation time |
Min |
X1 |
180 |
240 |
300 |
|
Activation temperature |
°C |
X2 |
60 |
70 |
80 |
|
Ratio |
X3 |
3.1:1 |
4.6:1 |
6.2:1 |
|
1. Phys-chemical properties of the OMWW
The physicochemical analysis of the studied OMWW (Table 2) shows these effluents have an acidic pH (4.81), indicating that OMWW is an acidic effluent. The same pH value was observed by Bouknana et al. (2014), Lee et al. (2019), Dehmani et al. (2020) and Lissaneddine et al. (2021). Table 2 shows a lower electrical conductivity (12 mS cm−1) than that found by Lissaneddine et al. (2021), Elayadi et al. (2021) and El Ghadraoui et al. (2021). The Chemical Oxygen Demand (80 g O2. L−1) is high and is also characterized by a predominance of toxic substances and the presence of PCs (Vuppala et al. 2021). Inorganic loadings such as potassium (4.8 g L−1), and sodium (0.3 g L−1).
|
Parameters (Unit) |
OMWW |
|---|---|
|
pH |
4.81 |
|
Electrical Conductivity (mS cm−1) |
12 |
|
COD (g-O2 L−1) |
84.1 |
|
Total phosphorus (mg L−1) |
2.54 |
|
Orthophosphate (mg L−1) |
1.69 |
|
Nitrite (mg L−1) |
0.06 |
|
Sodium (g L−1) |
0.3 |
|
Potassium (g L−1) |
4.8 |
|
Ammoniacal nitrogen (mg L−1) |
0.96 |
|
Kjeldahl nitrogen (NTK) (mg L−1) |
215 |
|
Sulfate (mg L−1) |
2769.23 |
|
Nitrate (mg L−1) |
0.331 |
2. Characterization of the bio-sorbent
Figure 1(a and b) shows the FTIR spectrum of OP before and after treatment by hydrogen peroxide. FTIR spectrums were obtained in the wavelength range of 480-4000 cm−1. The FTIR spectrum of OP waste (Fig. 1a), the peak at 3457 cm−1, can be attributed to the -OH stretching vibration. The peak at 2925 cm−1 is due to C-H stretching vibrations in aliphatic CH, CH2, and CH3. The peaks at 1461 and 1645 cm−1 were attributed to the symmetrical stretching vibrations of CH2 and C=O groups, respectively (Wang et al. 2019). The FTIR spectrum of bio-sorbent (Fig. 2b) at 3416 cm−1 corresponds to the -OH of hydroxyl functional groups. The peak at 1628 cm−1 is attributed to the stretching vibration of carboxylic groups -C=O (Martín-Lara et al. 2008), the bond at 1380 cm−1 is attributed to the symmetrical stretching vibration CH2 (Allwar et al. 2020). The peak at 1033 cm−1 can be attributed to the bending vibration of the -OH group (Nasrullah et al. 2018). The FTIR spectra (Fig. 2c) between the bio-sorbent before and after PCs adsorption showed no significant difference.
Biosorption can be explained by the interaction between the functional groups present on the bio-sorbent and the CPs, considering two types of chemical and physical interactions. Biosorption involves different mechanisms such as coordination, complexation, microprecipitation or electrostatic attraction (Veglio and Beolchini 1994). Identifying these processes and the characterization of the active sites of the bio-sorbent are essential steps for optimizing the operating conditions in the development of the adsorption capacity of PCs.
SEM analysis allows visualization of the bio-sorbent pores. This analysis was performed to determine the elements present on the surface of the samples by EDX. The results obtained from olive pomace before and after chemical activation at the same magnifications are presented in Fig. 2.
Figure 2a shows that the surface of the olive pomace does not contain pores or voids. However, after activation, relatively more homogeneous pores with constant diameters appeared on the external surface of the bio-sorbent, improving the porosity; consequently, a high number of pores on the surface of the bio-sorbent.
The EDX analysis of the OP before and after activation allowed the elemental analysis of the elements present on the external surface. The analysis shows that both materials are composed mainly of carbon atoms and contain oxygen (Table 3a). After activation (Table 3b), Mg and Cu appear on the surface.
Şirazi et al. (2021) observed similar texture characteristics using OP activated by KOH. This shows that the OP's SEM surface before activation has a porosity less than that of the adsorbent after activation, which is similar to the present study results.
Table 3 EDX spectra of OP before (a) and after chemical activation (b)
(a)
|
Element |
Atomic % |
|---|---|
|
C |
74.13 |
|
O |
24.46 |
|
Si |
0.26 |
|
P |
0.26 |
|
S |
0.17 |
|
K |
0.54 |
|
Ca |
0.18 |
|
Total |
100 |
(b)
|
Element |
Atomic % |
|---|---|
|
C |
70.50 |
|
O |
29.10 |
|
Cu |
0.22 |
|
Mg |
0.18 |
|
Totals |
100 |
The XRD patterns for pure and activated OP are shown in Fig. 3. The XRD patterns of OP before and after activation are very similar, suggesting that no alteration of the OP structure occurred during the preparation of the bio-sorbent. This is normal because we only did a chemical activation without carbonization. The two diffractograms showed peaks located at 2θ = 22.31° (Trache et al. 2014), 39.16°, 45.43°, 61.58°, 77.65°, 84. 47°, which is attributed to the native cellulose, while the other constituents of OP are mainly amorphous, which is explained by the appearance of the spectrum Schneider and Brenbner (1985) and Zhang et al. (2015).
The pH at which the zero surface charge is called the point of zero charges (pzc). The surface's electrokinetic properties are defined by pHPZC (Yagub et al. 2014). The plot pHf = f (pHi) is shown in Fig. 4. Results show that pHPZC of the bio-sorbent was around 8.64.
This value, superior to 7, indicates a concordance between this result, the acid's contents, and the surface's basic functions. Thus, the bio-sorbent surface is positively charged for pH values lower than pHPZC. As a result, the solution will become less acidic (consumption of protons from the solution), while for higher values, the surface is negatively charged, and the solution will become more acidic (release of surface protons). This is consistent with the result obtained by Şirazi et al. (2021) since the KOH-activated OP, pHPZC for adsorbent, was 8.0.
3. Process optimization
3.1. Optimization of preparation conditions for bio-sorbent
The preparation variables are presented in a design matrix; their ranges and responses (adsorption capacity (mg g−1)) are displayed in Table 4. The statistical software MINITAB is applied to compare and correlate the independent variables in Table 5. It was applied to develop polynomial regression equations, representing all quadratic expressions suggested by MINITAB. The equation expression was selected according to the sequential sum of the square model, which is based on the highest order of the polynomial where the model was not aliased and the extra terms were significant (Sahu et al. 2010; Garba et al. 2014; Salman 2014). There was a clear correlation between the experimental and predicted data, as indicated by the model R2 values 0.9745 for adsorption capacity. The final empirical model equation for the Yq response of the adsorption capacity is given by equation (eq. (8)):
Yq= 686 – 3.72X1 – 13.9X2 + 85X3 + 0.00583X12 + 0.190X22 + 58.3X32 + 0.0292X1X2 + 0.238X1X3 – 2.98X2X3 (Eq. 8)
|
Run |
Level |
Variables |
Repones q (mg g−1) |
||||
|---|---|---|---|---|---|---|---|
|
X1 |
X2 |
X3 |
|||||
|
1 |
-1 |
-1 |
-1 |
240 |
60 |
3.1: 1 |
273.21 |
|
2 |
+1 |
+1 |
-1 |
180 |
60 |
4.6: 1 |
123.21 |
|
3 |
-1 |
-1 |
-1 |
240 |
60 |
6.2: 1 |
326.78 |
|
4 |
+1 |
+1 |
-1 |
180 |
80 |
4.6: 1 |
416.35 |
|
5 |
-1 |
-1 |
+1 |
180 |
70 |
3.1: 1 |
340.71 |
|
6 |
+1 |
+1 |
+1 |
240 |
80 |
6.2: 1 |
486.07 |
|
7 |
-1 |
-1 |
+1 |
180 |
70 |
6.2: 1 |
305.35 |
|
8 |
+1 |
+1 |
+1 |
300 |
60 |
4.6: 1 |
292.85 |
|
9 |
-1.682 |
0 |
0 |
240 |
80 |
3.1: 1 |
551.78 |
|
10 |
+1.682 |
0 |
0 |
300 |
70 |
6.2: 1 |
510.71 |
|
11 |
0 |
-1.682 |
0 |
240 |
70 |
4.6: 1 |
332.14 |
|
12 |
0 |
+1.682 |
0 |
240 |
70 |
4.6: 1 |
332.14 |
|
13 |
0 |
0 |
-1.682 |
300 |
70 |
3.1: 1 |
489.00 |
|
14 |
0 |
0 |
+1.682 |
240 |
70 |
4.6: 1 |
332.14 |
|
15 |
0 |
0 |
0 |
300 |
80 |
4.6: 1 |
656.00 |
|
Source |
df |
Sum of Squares |
Mean Square |
F-Value |
p-Value |
|---|---|---|---|---|---|
|
Model |
9 |
242316 |
26924 |
21.20 |
0.002 |
|
A-Time |
1 |
72758 |
72758 |
57.28 |
0.001 |
|
B-Temperature |
1 |
149644 |
149644 |
117.81 |
0.000 |
|
C-Ratio |
1 |
83 |
83 |
0.07 |
0.808 |
|
A2 |
1 |
1624 |
1624 |
1.28 |
0.309 |
|
B2 |
1 |
1332 |
1332 |
1.05 |
0.353 |
|
C2 |
1 |
12563 |
12563 |
9.89 |
0.026 |
|
AB |
1 |
1225 |
1225 |
0.96 |
0.371 |
|
AC |
1 |
814 |
814 |
0.64 |
0.460 |
|
BC |
1 |
3557 |
3557 |
2.80 |
0.155 |
Table 4 represents the variance (ANOVA) results of the full factional design using the statistical software MINITAB. The model F-value was recorded at 21.20, enlightening that the model was significant. On the other hand, high values of R2 (0.9745) and Adj-R2 (0.9285) were estimated, indicating that the selected model can describe 92.85% of the total variation on adsorption capacity data. The parameters having a p-value probability value of less than 0.05 are significant. In this case, A, B and C2 are the significant model terms. Values superior to 0.1 are considered non-significant terms.
3.2. The Pareto chart
The presentation of the relative importance of the main effects and their interaction was achieved using the Pareto chart, as shown in Fig. 5. The individual factors and the likely combinations obtained are represented as bars. To show whether the effects studied differ significantly from zero, the t-test was used (Abdel-ghani et al. 2016). For a 97.45% confidence level and nine degrees of freedom, the t-test value was found to be equal to 2.57. The values on the horizontal axes are the t-test values for each effect and their interaction (Carmona et al. 2005; Rathinam et al. 2011; Saadat and Karimi-Jashni 2011). Variables A, B and C2 have an absolute value greater than 2.57, which places them on the right side of the vertical line and makes them significant. While all other factors have an absolute value below the reference line, making them insignificant. By analyzing the Pareto chart, it can be seen that B, A and C2 have the greatest influence on the adsorption of PCs in the bio-sorbent. On the other hand, the effect of ratio (C) made the smallest contribution to the adsorption of PCs into bio-sorbent because it is far from the reference line of the Pareto chart.
3.3. Contour plots and response surface
The 3D contour plots and response surface show the interactive effects of several parameters on the response. The contour plots and response surface were acquired by Minitab software to indicate the adsorption efficiency of PCs considering independent factors such as activation time, activation temperature and ratio were shown in Fig. 6. The effect of activation time, ratio and activation temperature was investigated using response surface methodology (RSM) by Minitab software. From Table 5, the F-value of the model is 21.20 and the p-value is 0.023. The model terms were considered significant when the p-value was less than 0.05. The contour plot (Fig. 6a) for the interaction between activation time and ratio shows at time 300 min and ratio 6.2:1 (3) the interaction was declared significant. The graph of the interaction between activation temperature and ratio (Fig. 6b) shows that at ratio 3.2:1(1) and temperature 80°C, the adsorption of PCs is significant. The interaction between activation temperature and activation time (Fig. 6c) indicates at time 300 min and temperature 80°C, the biosorption is significant.
The optimal conditions for bio-sorbent preparation using response optimization were obtained at ratio = 6.2:1 (3), activation time = 300 min and activation temperature = 80°C.
4. Adsorption efficiency
4.1. Batch adsorption
4.1.1. Effect of initial concentration
The driving force is very important by the initial concentration of PCs to overcome all the mass transfer limitations between the two phases, liquid and solid (Liu et al. 2019).
The plot of adsorption capacity versus time at different concentrations was presented in Fig. 7. The maximum adsorption capacity is 789.28 mg g-1, at the initial phenol concentration of 4000 mg L-1 and time 120 min. This increase in adsorption capacity with increasing concentration was attributed to the fact that this concentration was the driving force in order to overcome the resistance in the mass transfer of PCs between the liquid phase (OMWW) and the solid phase (bio-sorbent surface), resulting in the increase of their transfer rate into the adsorbent (Lissaneddine et al. 2021). Comparing the results with different studies, the adsorption of PCs on activated carbon by Achak et al. (2009) reported a decrease in adsorption efficiency with an increase in the initial concentration. This phenomenon was attributed to activated carbon adsorbing more phenolic compounds at low initial concentrations than higher initial concentrations. On the other hand, in the study of Lissaneddine et al. (2021) on the use of SA-AC beads for the adsorption of PCs, an increase in adsorption capacity was observed with the increase in initial concentration. This is due to the increasing amount of PCs in the solution, which causes a rise in the mass transfer driving force (Liu et al. 2019).
4.1.2. Adsorption isotherm
When the adsorption process reached equilibrium, the adsorption isotherms were analyzed to describe the distribution of PCs between the solid and liquid phases (Liu et al. 2019). Several models have been proposed to describe this relationship during the adsorption of PCs OMWW, the main ones being the isothermal models of Langmuir and Freundlich. Some assumptions are applicable according to the Langmuir theory. First of all, all sites are energetically equivalent, i.e., the energy of the adsorption sites is considered equal, while they can contain at most one adsorbate molecule, forming a monolayer on the adsorbent surface. In addition, the surface of the adsorbent is assumed to be homogeneous, with the identification of the active sites. Moreover, there is no interaction between the adsorbed molecules (Swenson and Stadie 2019). On the contrary, Freundlich's theory considers the heterogeneous adsorption surface, while several layers of adsorbate molecules can be formed. Furthermore, this theory considers that interactions between adsorbed molecules occur (Dada et al. 2012; Osagie and Owabor 2015). Figure 8 shows the adsorption isotherm of PCs with the bio-sorbent. The adsorption capacity of PCs increased with the increase of PCs concentration until it reached 789.28 mg g−1 at the concentration of 4000 mg L −1. The driving force of mass transfer is the increase of the PCs concentration in the solution (Li et al. 2019).
From the linear regression data (Table 6) in Fig. 9(a and b), it is clear that the model with a correlation coefficient (R2= 0.9898), indicating that the active adsorption sites of PCs on the bio-sorbent are energetically homogeneous and a significant monolayer coverage of PCs on the outer surface of the bio-sorbent is formed without any interaction between the PCs (Mojoudi et al. 2019). Therefore, the Langmuir model represents the best model for the adsorption of PCs onto the bio-sorbent with a maximum adsorption capacity is 1125 mg g-1.
|
Freundlich |
Langmuir |
||||
|---|---|---|---|---|---|
|
KF (mg 1−(1/n) L 1/n g−1) |
n |
R2 |
KL (L mg−1) |
qm (mg g−1) |
R2 |
|
0.934 |
1.22 |
0.967 |
16.10−3 |
1250 |
0.9898 |
4.1.3. Effect of contact time
The effect of contact time allowed the essential parameters during adsorption. Adsorption time can vary depending on the nature of adsorbent and adsorbate, while it is affected by different parameters, such as concentration of PCs in the solution, temperature, and pH of the OMWW. In all cases, the equilibrium is reached at a given time; this is the state in which the increase of the contact time does not significantly increase PC adsorption efficiency. The equilibrium time varies with the studied adsorbents, such as SA-AC beads (120 min) (Lissaneddine et al. 2021), pomegranate seed (20 min) (Papaoikonomou et al. 2019), the raw clay (10 min) (Chaari et al. 2020), MgCl2-impregnated activated carbons (60 min) (Hamadneh et al. 2020) and hydroxyapatite-sodium alginate composite (Benaddi et al. 2021).
Figure 10 shows the sorption equilibrium obtained after about 150 min, then remains stable with an adsorption capacity value of 789.28 mg g-1. Adsorption can be considered a two-step process, a rapid initial adsorption step, attributed to many adsorption sites available on the bio-sorbent for PCs adsorption. This rapid step leads to a rapid increase in the amount of PCs accumulated on the bio-sorbent surface. Existing residual sites are then difficult to occupy by PCs, as repulsive forces develop between the PCs on the solid surface of the bio-sorbent and the bulk phase (Lissaneddine et al. 2021). In addition, PCs are small molecules and can diffuse into the internal pores (Papaoikonomou et al. 2019), thus reducing the driving force for mass transfer (Din et al. 2009). Obviously, during the slow step, the molecules encounter greater resistance to enter the adsorption sites (Lissaneddine et al. 2021; Achak et al. 2009).
4.1.4. Adsorption kinetic
The adsorption kinetic study provides important information to predict the adsorption process. In this work, two linear kinetic models studied the adsorption of PCs from OMWW by the bio-sorbent as a function of time, including pseudo-first-order and pseudo-second-order.
From Fig. 11, both models show clear linearity of the equation with a correlation value of R²=0.9456 for pseudo-first-order kinetics and a correlation of R²=0.992 for pseudo-second-order kinetics. Therefore, according to Table 7, the model with the experimentally calculated maximum equilibrium sorption capacity value approximately equal to that determined theoretically is the pseudo-first-order. Therefore, the adsorption kinetics of PCs on the bio-sorbent is of pseudo-second-order type. Furthermore, this model assumes that the sorption of PCs is mainly controlled by chemisorption, which includes valence changes through electron exchange or partitioning between the bio-sorbent and PCs. Therefore, external adsorption occurs more often than micropore adsorption (Abdelhay et al. 2017; Tao et al. 2019).
In a batch test, the bio-sorbent removed 789.28 mg g−1 of PCs. Compared to olive pomace treated with H3PO4+ 2MHNO3 (28.57 mg g−1) (Soudani et al. 2013), HCl + ZnCl2 (78.74 mg g−1) (Temdrara et al. 2015) and H3PO4 (110.30 mg g−1) (Soudani et al. 2017). The bio-sorbent of this study showed better removal efficiency of PCs.
|
Pseudo-first-order |
Pseudo-second-order |
|||||||
|---|---|---|---|---|---|---|---|---|
|
qethero (mg g−1) |
qeexp (mg g−1) |
Klag (min−1) |
R2 |
qethero (mg g−1) |
qeexp (mg g−1) |
Kb (mg g−1 min−1) |
R2 |
|
|
446 |
408.92 |
0.024 |
0.9456 |
446 |
423.21 |
60.64×10−4 |
0.9929 |
|
4.1.5. Effect pH
pH is a more important parameter in the adsorption process. The pH affects the sorption mechanisms onto the bio-sorbent surface and the nature of physicochemical interactions between the bio-sorbent adsorption sites and the PCs (Achak et al. 2009). In addition, pH can also affect the surface charge of the adsorbent (Aksu and Gönen 2004).
The adsorption capacity as a function of time at different pH values was shown in Fig. 12. A significant effect of pH on the adsorption of PCs was observed. It can be explained by the π-π electron donor-acceptor interactions between the aromatic ring of the PCs (electron acceptor) and the free oxygen of the surface basic sites (electron donor) (Moreno-Castilla 2004; Dabrowski et al. 2005; Hamdaoui and Naffrechoux 2007). The adsorption capacity of PCs on the bio-sorbent increases from 150 to 473 mg g−1 for pH values between 2 and 4, the maximum adsorption of PCs is reached at pH equal to 4.0. If the pH value is exceeded 4.0, there is a decrease in the adsorption capacity of PCs. This decrease can be attributed to the phenol ionization to phenolate ions form since the absorption of this later is prevented by hydroxyl ions presented on the adsorbent (Halhouli and Darwish 1995). The adsorption capacity is higher at acidic pH than at basic pH. The pH of the OMWW is acidic, which is advantageous in this study, so pH adjustment would not be necessary during effluent treatment. Therefore, OMWW can be used directly without pH adjustment in large-scale treatment systems, which minimizes costs during treatment. A study by Alwan (2008) showed that pH adjustment in industrial wastewater treatment plants influences the treatment efficiency, which leads to an increase in wastewater treatment cost.
4.1.6. Effect of temperature
Temperature is one of the most important parameters during the sorption process. Figure 13 shows the plot of adsorption capacity versus time at different temperatures. The maximum adsorption capacity is found to be 1307 mg g−1 at a temperature of 60°C. The increase in adsorption capacity with increasing temperature can be attributed to the increase in temperature promoting the polymerization phenomenon between the PCs, increasing adsorption efficiency. Furthermore, this increase in adsorption capacity can be associated with an increase in adsorbent swelling, allowing more active sites of the bio-sorbent to become available to the adsorbate (Ververi and Goula 2019). However, the temperature is a parameter that depends on other characteristics of the adsorbent and the solution (Moreno-Castilla 2004).
4.1.7. Thermodynamic studies
The thermodynamic parameters (ΔG0), (ΔH0) and (ΔS0) were calculated by using the following equations 10 and 11:
\(\varDelta {G}^{^\circ }=\varDelta {H}^{^\circ }-T\times \varDelta {S}^{^\circ }\)(Eq. 10)
\(ln{K}_{c}=-\frac{\varDelta {H}^{^\circ }}{R\times T}+\frac{\varDelta {S}^{^\circ }}{R}\)(Eq. 11)
Plotting ln(Kc) as a function of 1/T (Fig. 14) allows the calculated ΔH0, ΔG0 and ΔS0. The thermodynamic parameters for the adsorption of PCs onto the bio-sorbent are given in Table 8. The value of ΔG0 for all three temperatures was obtained negative, confirming the spontaneous and feasible adsorption of PCs on the bio-sorbent. In addition, the ΔG0 values for physisorption are less than -20 kJ mol−1 and chemisorption is greater than -80 kJ mol−1 (Turco et al. 2019). In this work, ΔG0 was -4229.38, -4355.49, -4732.51 and -5036.33 J mol−1, which indicated the adsorption of PCs with the bio-sorbent is chemisorption. In addition, ΔH0 and ΔS0 are other critical thermodynamic parameters that provide information about the adsorption process of PCs. In this study, the positive value of ΔH0 was 30.82 kJ mol−1, which demonstrates the endothermic nature of the adsorption process of PCs with the bio-sorbent. As for ΔS0, a positive value was obtained 49.26 J mol−1 K−1, so the increased randomness can explain it at the liquid/solid interface during the adsorption process (Sun et al. 2005). These outcomes are identical to those of Lissaneddine et al. (2021), who have found that ΔS° = 74 J K−1 mol−1 and ΔH° = 29 kJ mol−1.
|
ΔG0 (J mol−1) |
ΔH0 (KJ mol−1) |
ΔS0 (J mol−1 K−1) |
|||
|
293.5K |
298.5K |
313.5K |
333.5K |
||
|
-4229.38 |
-4355.49 |
-4732.51 |
-5036.33 |
30.82 |
49.26 |
5. Fixed-bed column
A continuous fixed-bed column adsorption process using a bio-sorbent as a sorbent was considered an alternative method to separate PCs from OMWW. Figure 15 shows the breakthrough curves for the adsorption of PCs on bio-sorbent in a fixed-bed column adsorption system at 60°C.
In Fig. 15, C0 is the initial concentration of PCs entering the column and Ct is the concentration of PCs leaving the column every 15 minutes. All PCs were adsorbed in the first 40 minutes, resulting in a low concentration of PCs in OMWW output. During the adsorption process, the concentration of PCs in the treated effluent gradually increases. The bio-sorbent sites are saturated and the adsorption zone progresses vertically in the column (Kundu and Gupta, 2005). In our work, the qe value reached 643.92 mg g−1 for Ci= 4251 mg L−1 and Q= 0.5 mL min −1, while the removal rate was 64% (Table 9).
Using Yoon-Nelson and Thomas's empirical models, the mathematical analysis of PCs adsorption study data on fixed-bed columns were analyzed. The model proposed by Yoon-Nelson considers the adsorption probability of each molecule to be directly proportional to the breakthrough probability of the adsorbate on the adsorbent and the adsorption probability of the adsorbate (Ajmani et al. 2020). The Thomas model is a theoretical model commonly applied in analyzing column adsorption data. It is based on the assumptions of the Langmuir isotherm model and the second-order kinetic model of the batch adsorption process (Ajmani et al. 2020). The Yoon-Nelson and Thomas model parameters for the PCs adsorption on the bio-sorbent in a fixed-bed column are presented in Table 10.
From Table 10, the Yoon-Nelson model correlates R2= 0.9572, demonstrating that the Yoon-Nelson model was approachable and could be used to describe the adsorption of PCs in continuous mode on the bio-sorbent. Table 10 shows that the mean absolute error (MAE) values were 0.0125 and 0.0104 for the Yoon-Nelson and Thomas model, respectively. In contrast, the two models' root means square error (RMSE) values were calculated as 0.12 and 0.10, respectively. The RMSE and MAE values of the Thomas model were lower than those of the Yoon-Nelson model. The Thomas model fit the experimental data; the results followed Langmuir kinetics (Chue 2010).
|
Ci (mg L−1) |
Q (mL min−1) |
Vef (mL) |
mtotal (mg) |
R (%) |
qe (mg g−1) |
|---|---|---|---|---|---|
|
4251 |
0.5 |
60 |
500 |
64 |
643.92 |
|
Model |
Parameters |
Value |
|---|---|---|
|
Thomas |
KTh (mL mg− 1 min− 1) |
0.00144 |
|
q0 cal (mg g− 1) |
547.78 |
|
|
R2 |
0.94571 |
|
|
MAE |
0.0104 |
|
|
RMSE |
0.10 |
|
|
Yoon-Nelson |
KYN (min− 1) |
0.3411 |
|
tCal (min) |
57 |
|
|
R2 |
0.9571 |
|
|
MAE |
0.0125 |
|
|
RMSE |
0.12 |
6. Desorption
The desorption study aims to elucidate the adsorption process, the recovery of PCs, and the adsorbents' reuse. In the first adsorption cycle, the bio-sorbent was saturated with PCs with a removal rate of 64%. Then, the bio-sorbent was washed with distilled water and treated with 0.1M HCl solution for 120 min at room temperature. The adsorption process was duplicated several times, and the efficiency of the bio-sorbent in removing PCs was analyzed. At the second reuse cycle, the adsorption rate of the bio-sorbent is decreased from 64 to 30% (Fig. 16), which shows that the bio-sorbent is a good sorbent with excellent reusability and stability. Several studies on the desorption of PCs for adsorbent regeneration have been performed. According to Lissaneddine et al. (2021), who used Sodium-alginate-active carbon beads, the percentage of desorption of PCs was 58.5%. Yuney et al. (2020) used CuO-coated OP, reaching 77% of the PCs concentration desorbed.
In this paper, we are interested in testing the effectiveness of new bio-sorbent low-cost in removing PCs from OMWW in a continuous flow. The optimum condition of preparation of the bio-sorbent was determined using the statistical software MINITAB. The structure of bio-sorbent was characterized by FTIR and Brunauer, Emmett and teller SBET. The adsorption of PCs was found to be dependent on the contact time, initial concentration, pH solution and temperature. The Langmuir isotherm best represented the equilibrium adsorption data, and the adsorption capacity was found to be 446 mg g−1. The adsorption kinetics was described well by the pseudo-second-order model. The positive ΔH° value demonstrated that the adsorption was endothermic, and the negative ΔG° indicated the spontaneous adsorption reaction. Thomas is the best model that was better at predicting PCs column adsorption. The yield desorption decreases from 74 to 36% in the two cycles. To conclude, bio-sorbent can be stated among the effective alternatives for PC sorption since it provides ready separation and easy recycling from low-cost solutions.
Acknowledgements
This work was supported by the Morocco-Tunisian bilateral scientific cooperation project (20 /PRD-MT-02).
Competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
Ethics approval and consent to participate: We all declare that manuscript reporting studies do not involve any human participants, human data, or human tissue. So it is not applicable.
Consent for publication: Our manuscript does not contain data from any individual person, so it is “Not applicable.”
Data availability and materials: The datasets and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Funding information: This work was supported by the Morocco-Tunisian bilateral scientific cooperation project (20 /PRD-MT-02).
Authors’ Contributions: Imane HAYDARI (Data curation) (writing - original draft), (interpretation). Amina LISSANEDDINE (optimization) (Review and editing). Khalid AZIZ (Modelization) (Review and editing). Naaila OUAZZANI (Review and editing). Laila MANDI (Review and editing). Ayoub El GHADRAOUI (Participating in Data curation). Faissal AZIZ (Supervision) (Review and editing).
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