CHAPTER FOUR DATA PRESENTATION AND ANALYSIS 4.1 Characterization of MTCNS (Adsorbent) 4.1.1 Physicochemical Properties Table 4.1 depicts the various physicochemical parameters of the modified almond nut shells (MTCNS). Table 4.1: Physicochemical Characteristics of MTCNS (adsorbent) Parameters Mean Values ± Standard Deviation pH 4.42 ± 0.13 Conductivity (µs/cm) 197.00 ± 3.61 Moisture Content (%) 7.73 ± 0.61 Bulk Density (g/ml) 0.30 ± 0.02 Specific Gravity 1.58 ± 0.20 Porosity (%) 39.95 ± 2.00 Ash Content (%) 2.32 ± 0.23 Pore Volume (ml) 4.93 ± 0.42 pH: The pH value determines whether the activated carbon is acidic or basic.
The acid or basic nature of an activated carbon depends on the means it was prepared, inorganic matter and chemically active groups on its surface as well as the kind of treatment applied. The pH value obtained in this present investigation revealed that MTCNS with pH of 4.42 as presented in Table 4.1 is acidic in nature which was consistent with the result of Almond shells activated carbon (ASAC) subjected to phosphoric acid treatment having pH of 4.5 as reported by Bhatti et al. (2007). Also, this value was in agreement with the finding carried out by Cheremisinoff and Ellerbusch (1978) that the pH of either raw or modified agricultural by-products in water suspension can vary between 4 and 12, hence, it can be deduced that MTCNS is a good activated carbon material. Conductivity: This is a measure of the ability of water to allow the passage of an electrical current, and the unit is in micromhos per centimetre (µmhos/cm) or microsiemens per centimetre (µs/cm). Conductivity can be affected by many factors which includes the presence of inorganic dissolved solids (ions that carries negative and positive charges such as Cl-, NO3-, SO42- , PO43-, Ca2+, Na+, Mg2+, Al3+, etc.); organic compounds (like oil, phenol, alcohol & sugar); and temperature (the warmer the water, the higher the conductivity).
From the result obtained, it was observed that MTCNS studied has conductivity of 197 µs/cm as revealed in Table 4.1. In a similar research work, the conductivity of the phosphoric acid activated (ASAC) sample obtained by Bhatti, et al. (2007) was discovered to be 40 µs/cm. Moisture Content: The moisture content of a sample refers to the amount of water physically bound on the sample under normal condition. The laboratory result of the moisture content for MTCNS was determined to be 7.73 % as shown in Table 4.1; however, this was slightly higher than 7.21 % moisture content of Almond shells as reported by Erhan, et al., (2004) in their studies. The permissible limit of moisture content is 3 – 8 %; low moisture content is desired by activated carbon because its presence increases the rate of adsorption of contaminants into the microspore of the activated carbon (Inyang, et al., 2010).
High moisture content allows penetration of more contaminants into the matrix of the adsorbent thus reducing working capacity of the adsorbent (Appendix A2). Bulk Density: Bulk density is the ratio of mass of the aggregate to the volume of aggregate particles with voids between them; hence, it is used to convert quantities by mass to quantities by volume. The bulk density of activated carbon depends on several factors such as size; shape and degree of compaction of individual particles, and its data are useful to Engineers for the estimation of tank, cartridge or packing volume. The American Water Work Association has set a lower limit on bulk density at 0.25 g/ml for Granular Activated Carbon (GAC) to be of practical use (AWWA, 1991). The bulk density of prepared MTCNS (adsorbent) sample used for this work is within that limit, which is calculated to be 0.30 g/ml (Appendix A3). Specific Gravity: This is ratio of the weight of a given volume of material (activated carbon) to the weight of an equal volume of water, indicating how much heavier (or lighter) the material is than water.
The knowledge is necessary in the computation of fine particle properties like void ratio, degree of saturation, size distribution etc. The result obtained from this present study of specific gravity of MTCNS (adsorbent) was found to be 1.58 (Appendix A4), meanwhile 4.45 was obtained from activated carbon prepared from chemically treated Terminalia catappa nut shells (TTCNS) (Andal & Gohulavani, 2013). Porosity: This is used to explain how much empty or void, space is present in a given sample. It shows the capacity of activated carbon in terms of its efficiency. Porosity of the studied MTCNS (adsorbent) was evaluated to be 39.95 % (Appendix A5). Activated carbon used in determining pore volume by Aneke and Okafor (2005) gave porosity of 21.4 %.
Pore Volume: Pore volume is of importance in the facilitation of the adsorption process by providing sites and the appropriate channels to transport the adsorbate. The result obtained for MTCNS (adsorbent) was estimated to be 4.93 ml (Appendix A5). But in a similar research work carried out by Andal and Gohulavani (2013) using chemically treated Terminalia catappa nut shells (TTCNS), the pore volume was discovered to be 6.80 ml which shows that MTCNS is a good activated carbon with highly developed porous structure. Ash Content: The ash content of a sample is the inorganic (non-carbon) residue left after the organic matter has been burnt off which is not chemically combined with the carbon surface; also the ash content primarily depends on the types of raw material used for the production of the activated carbon. The percentage of ash content for MTCNS (adsorbent) sample studied was found to be 2.32 % (Appendix A6) which was consistent with Romero Gonzalez, et al., (2001) reported result of 2.14 % for almond shells. The obtained value for MTCNS was favourable because the ash content serves as interference during the adsorption (Kha, et al., 2009).
High ash content is not desirable and is considered as an impurity for activated carbon since it reduces the mechanical strength of carbon and affects its adsorptive capacity. The lower the ash content, the better the quality of the activated carbon. 4.1.2 Fourier Transform Infrared Analysis Figure 4.1 surmise the FTIR spectrum obtained in order to give an idea about the organic functional groups present in modified almond nut shells (MTCNS) sample that can participate in bonding with 2,6-DCP during adsorption process. The peaks emerging in the FTIR spectrum were assigned to a variety of functional groups in accordance to their respective wave numbers as stated in literatures. Fig. 4.1: FT-IR Spectrum of Modified Terminalia catappa Nut Shells (MTCNS) Table 4.2: FT-IR Spectrum Elucidation of MTCNS (Adsorbent) ? (cm-1) Assignment (Suspected Functional Group) Adsorption Peak Intensity 3777.89 Sharp OH (non-bonding) Free 3394.00 Strong, Broad OH (stretch), N-H (stretch) 2923.13 Sharp, Medium C-H (stretch) 1607.35 – 1734.38 Sharp, Medium C-H (alkane), C=C (stretch), C=O (stretch) 1247.17 – 1442.00 Weak C-H (bend), C-O (alcohol), C-N, OH (carboxylic acid) 1046.88 Strong C-O (alcohol), C-H (in plane), C=N (bend) 604.70 Weak C-H (bend), C=C (out of plane) ? is the wave number Table 4.2 shows the FT-IR spectrum elucidation of modified almond nut shells (MTCNS).
A sharp peak is recognized around 3777.89 cm-1 which is attributed to non-bonding (free) hydroxyl (–OH) group of water. The strong and broad absorption peak at 3394.00 cm-1 depicts that of OH bond of alcohol and carboxylic acid groups; and N-H bond of amide groups with stretched vibrations. The peak observed at 2923.13 cm-1 was associated with the stretching vibrations of C-H bond of methyl, methylene and methoxy groups (Feng et al., 2008), and those peaks appearing around 1607.35 – 1734.38 cm-1 corresponded to C-H (alkane), C=C (aromatic) and C=O stretch. On the other hand, the absorption bands 1247.17 – 1442.00 cm-1 were ascribed to C-H bend, C-O (alcohol), C-N, and OH (carboxylic acid) and the one at 1046.88 cm-1 to C-O (alcohol), C-H and C=N bend (nitriles) respectively. The weak band with wave number of 604.70 cm-1 was assigned to C-H bend and C=C which are out of plane. Consequently, the FT-IR spectra indicates that hydroxyl, carboxyl, and carbonyl groups were very important (hetero-atoms) functional groups which participate in the binding of 2,6-DCP to the surface of MTCNS (adsorbent).
4.2 Adsorption Process Studies 4.2.1 Effect of pH on Adsorption pH of an aqueous solution is an essential operational parameter prevailing the adsorption process of organic chemicals or metals in solution as it not only affects the solubility of the chemical ions concentration of the counter ions on the functional groups of the adsorbent, but also influences the degree of ionization of adsorbate during reaction (Agarry et al., 2013b). The effect of variation of pH in the range of 2 -10 on the adsorption of 2,6-DCP by MTCNS (adsorbent) was studied from the data by keeping other parameters constant as presented in Table 4.3. The relations between removal percentage and pH were revealed in Fig. 4.2. It was observed that the percentage of 2,6-DCP removal increased from 92.24 % at pH 2 to 96.92 % at pH 6 which is the maximum uptake and decreased to 94.52 % at pH 10.
The apparently high adsorption of 2,6-DCP at lower pH was due to high electrostatic attraction between the negatively charged 2,6-DCP molecules and positively charged adsorption sites. Increase in the pH present fewer H+ ions in the solution, consequently more negatively charged sites were made available which facilitate a decreased in 2,6-DCP removal due to electrostatic repulsion (Morlu & Bareki, 2017). Table 4.3: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various pH pH Ce (mg/L) Qe = Co-Ce (mg/L) qe (mg/g) Removal Efficiency (%) 2 7.76 92.24 4.612 92.24 4 5.46 94.54 4.727 94.54 6 3.08 96.92 4.846 96.92 8 4.26 95.74 4.787 95.74 10 5.48 94.52 4.726 94.52 Co = 100 mg/L, mass = 2 g, contact time = 30 minutes. Fig. 4.2: Effect of pH for the Adsorption of 2,6-DCP onto MTCNS 4.2.2 Effect of Adsorbent Dosage on Adsorption In this study, five different dosages of MTCNS were selected ranging from 2.0 to 10.0 g, while other parameters were kept constant. The results are presented in Table 4.4 while the relationship between adsorbent dosage and removal efficiency of 2,6-DCP is shown in Fig.
4.3. It can be explain from this figure that as adsorbent dosage increases there is an increase in the removal efficiency. This kind of a trend is mostly ascribed to an increase in the adsorptive surface area and the availability of more active binding sites on the adsorbent surface (Das & Mondal, 2011). Table 4.4: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various Adsorbent Doses Mass (g) Ce (mg/L) Qe = Co-Ce (mg/L) qe (mg/g) Removal Efficiency (%) 2 4.52 95.48 4.774 95.48 4 3.34 96.66 2.417 96.66 6 1.19 98.81 1.647 98.81 8 0.47 99.53 1.244 99.53 10 0.67 99.33 0.993 99.33 Co = 100 mg/L, pH = 7, Contact Time = 30 minutes.
Fig. 4.3: Effect of Adsorbent Dosage for the Adsorption of 2,6-DCP onto MTCNS However, significant changes in value of adsorbent dosage (from 8.0 to 10.0 g) yield little or no change in percentage adsorption of the 2,6-DCP. This revealed that the adsorption sites remain unsaturated during the adsorption reaction whereas the number of sites available for adsorption increases by increasing the adsorbent dose. Furthermore, maximum 2,6-DCP removal efficiency of 99.53 % was recorded at 8.0 g adsorbent dose of MTCNS. 4.2.3 Effect of Contact Time on Adsorption The variation in contact time (30 – 150 minutes; 30 mins. Interval) on the adsorption of 2,6-DCP by MTCNS (adsorbent) was investigated at fixed adsorbent dose of 2 g, pH of 7.0 and initial concentration of 100 mg/l, the results are shown in Table 4.5.
The effect of contact time on removal of 2,6-DCP by MTCNS as a function of time is depicted in Fig. 4.4. It can be seen that the removal efficiency of 2,6-DCP increased considerably until the optimal removal efficiency reached within about 100 minutes contact time, where a saturation adsorption has been shown. Further increase in contact time beyond this point did not show significant changes. In general, the rate of removal of adsorbate increases with an increase in contact time to a certain extent, further increase in contact time does not increase the uptake due to deposition of adsorbate on the available adsorption site on adsorbent material (Ansari & Mosayebzadeh, 2010).
Table 4.5: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various Period of Contact Time (mins.) Ce (mg/L) Qe = Co- Ce (mg/L) qe (mg/g) Removal Efficiency (%) 30 4.52 95.48 4.774 95.48 60 3.61 96.39 4.820 96.39 90 1.11 98.89 4.945 98.89 120 0.83 99.17 4.959 99.17 150 0.75 99.25 4.963 99.25 Co = 100 mg/L, pH = 7, mass = 2 g Fig. 4.4: Effect of Contact Time for the Adsorption of 2,6-DCP onto MTCNS 4.2.4 Effect of Initial Concentration on Adsorption The adsorption of 2,6-DCP onto the MTCNS (adsorbent) was studied for different concentrations ranging from 100 – 500 mg/l keeping pH 7, adsorbent dose 2.0 g and exposure time 30 minutes fixed in all the samples. The data obtained are provided in Table 4.6. The removal efficiency of 2,6-DCP was found to decrease with the increase in the initial concentration as shown graphically in Fig. 4.5.
Maximum removal efficiency of 95.68 % occurred for low initial concentration which showed gradual reduction when initial concentration was raised. It could be attributed to the fixed amount of adsorbent. Table 4.6: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various Concentrations Co (mg/L) Ce (mg/L) qe (mg/g) Ce/qe (g/L) Removal Efficiency (%) 100 4.32 4.784 0.903 95.68 200 12.22 9.389 1.303 93.89 300 21.30 13.935 1.529 92.90 400 32.28 18.386 1.756 91.93 500 43.95 22.803 1.927 91.21 pH = 7, mass = 2 g, contact time = 30 minutes. Fig. 4.5: Effect of Initial Concentration for the Adsorption of 2,6-DCP onto MTCNS The adsorption sites were occupied and attained saturation at low concentration, with increase in 2,6-DCP concentration no further adsorption will be achieved at high concentration due to non-availability of active sites which resulted to reduced removal efficiency. Similar results have been reported in literature on the extent of removal of dyes, the initial adsorbate concentration provides an important driving force to overcome mass transfer resistance of ions between the aqueous and solid phases (Donmez & Aksu, 2002).
4.2.5 Adsorption Isotherm Modelling The adsorption isotherm play a vital role in describing the interaction between adsorbate and adsorbent, it gives an insight about the adsorption capacity of the adsorbent. This indicates how the adsorption molecules between the liquid and solid phases distribute in order to attain equilibrium state during adsorption process. The surface phase may be considered as a monolayer or multilayer (Salleh et al., 2011). In this present study, Langmuir and Freundlich isotherm models relating to adsorption equilibrium are tested. The Langmuir isotherm is described mathematically by equation (2.3) or (2.4), where qmax and KL are Langmuir constants related to adsorption capacity (maximum amount adsorbed per gram of adsorbent) (mg g-1) and energy of sorption (L mg-1), respectively.
Values of qmax and KL can be calculated from the slope and intercept of the linear plot of Ce/qe against Ce (Appendix C) as illustrated in Fig. 4.6 with a correlation coefficient (R²) of 0.9433, thus indicating that the adsorption equilibrium data conform well to the Langmuir isotherm model, confirming monolayer adsorption and the monolayer adsorption capacity (qmax) was found to be 40.49 mg/g. Similar research conducted by Sathishkumar et al. (2009) obtained 17.94 mg/g as the maximum monolayer adsorption capacity of maize cob carbon for the adsorption of 2,4-DCP while Agarry et al. (2013) obtained 14.25 mg/g as the maximum monolayer adsorption capacity of modified plantain peels for the adsorption of 2,6-DCP. The essential characteristics of the Langmuir isotherm can be expressed in terms of dimensionless constant Separator Factor (RL) which is defined as R_L= 1/(1+ K_L C_o ) ………… (4.1) where Co is the initial 2,6-DCP concentration.
The value of RL indicates the type of isotherm to be either unfavourable (RL > 1), linear (RL = 1), favourable (0 < RL < 1), or irreversible (RL = 0) (Hameed, et al., 2008). The values of RL (Table 4.8) in the present investigation was calculated with initial concentration range 100 – 500 mg/L were between (0 < RL Langmuir. However, the Freundlich isotherm model provided the best fit with a higher correlation coefficient hence considered desirable model to describe the adsorption process. 4.2.6 Adsorption Kinetic Models In this study, three (3) different models were applied to evaluate the experimental data of the adsorption kinetic of 2,6-DCP onto MTCNS namely: Lagergren’s Pseudo-first-order and Pseudo-second-order, and Webber-Moris intra-particle diffusion models. The pseudo-first order kinetic model equation describes the rate of adsorption is directly proportional to the number of unoccupied sites by the solutes (Lagergren ; Svenska, 1898).
Pseudo-second-order equation describes the rate of occupation of adsorption sites is proportional to the square of the number of unoccupied sites (Dada et al., 2012). Intra-particle diffusion plays a significant role in controlling the kinetics of the adsorption process. The linear forms of these three models are expressed by equations (2.4), (2.8) and (2.9) respectively, where the terms qe and qt have the same meaning as previously described in chapter 2 with unit mg g -1 while k1, k2 and kp are pseudo-first-order, pseudo-second-order and intra-particle diffusion model rate constants, expressed in min-1, g / mg min and mg / g min0.5 respectively. Table 4.9: Kinetic Study Data for the Removal of 2,6-DCP at Different Initial Concentration Time (t) Min. Initial 2,6-DCP Concentration (Co) in mg/L 100 mg/L 200 mg/L 300 mg/L 400 mg/L 500 mg/L Ct qt Ct qt Ct qt Ct qt Ct qt 30 4.32 4.784 12.22 9.389 21.30 13.935 32.28 18.386 43.95 22.803 60 3.61 4.820 11.72 9.414 20.22 13.989 31.72 18.414 43.05 22.848 90 1.11 4.945 10.84 9.458 19.14 14.043 30.92 18.454 41.85 22.908 120 0.83 4.959 10.20 9.490 18.60 14.070 29.96 18.502 41.05 22.948 150 0.75 4.963 9.92 9.504 18.18 14.091 29.32 18.534 40.80 22.960 Note: Final 2,6-DCP Concentration (Ct) in mg/L and Adsorption Capacity (qt) in mg/g @ Time (t) The slopes and intercepts of plots were used to calculate qe, k1, k2 and kp as illustrated in Figures 4.8 – 4.10.
These model parameters and constants along with the corresponding linear regression coefficient R2 values are depicted in Table 4.10. The applicability of the kinetic model is compare by judging the correlation coefficient R2 and the agreement between the calculated and experimental qe values. Table 4.10: Kinetic Parameters and Correlation Coefficients (R2) obtained for the Adsorption of 2,6-DCP onto MTCNS (Adsorbent) Kinetic Models Parameters Initial Concentration Co (mg/L) 100 200 300 400 500 qe, Exp. (mg g-1) 4.963 9.504 14.091 18.534 22.960 Pseudo First Order log?(q_e ?-? q_t )=log??q_e ?-k_1/(2.303) t k1 (min-1) 0.045 0.023 0.023 0.017 0.028 qe, Cal.
(mg g-1) 1.070 0.292 0.344 0.286 0.480 % ?qe 78.44 96.93 97.56 98.46 97.91 R2 0.9284 0.9163 0.9812 0.9058 0.9179 Pseudo Second Order t/q_t =1/(k_2 ?q_e?^2 )+t/q_e k2 (g mg-1 min-1) 0.107 0.113 0.132 0.120 0.122 qe, Cal. (mg g-1) 5.028 9.560 14.144 18.587 22.989 % ?qe 1.29 0.59 0.37 0.29 0.13 R2 0.9999 1.0000 1.0000 1.0000 1.0000 Intra-particle Diffusion q_t=K_p.t^(1/2)+C kp (mg g-1 min-0.5) 0.0301 0.0312 0.0237 0.0225 0.0248 C (mg g-1) 4.6176 9.1444 13.8080 18.251 22.665 R2 0.8843 0.9233 0.9891 0. 9697 0.9804 It can be observed that the correlation coefficients (R2) obtained from the plots of log (qe – qt) versus time (t) (Appendix D) for pseudo-first-order equation (Fig. 4.8) were moderately high (0.9058 – 0.9812), but the calculated qe values from pseudo-first-order kinetic plots were deviating (% ?qe) much as compared to the experimental qe values, and were not in agreement with the experimental qe values suggesting that the removal of 2,6-DCP by adsorption on MTCNS did not fit the pseudo-first-order model.
Fig. 4.8: Pseudo-first-order Kinetic plots for Removal of 2,6-DCP by MTCNS Fig. 4.9: Pseudo-second-order Kinetic plots for Removal of 2,6-DCP by MTCNS Fig. 4.10: Intra-particle Diffusion Kinetic plots for Removal of 2,6-DCP by MTCNS On the other hand, the R2 values from the plots of t/qt versus time (t) (Appendix D) for pseudo-second-order model (Fig. 4.9) were extremely high (0.9999 – 1) for all the initial concentrations of 2,6-DCP. The calculated qe values were closer to the experimental qe values and the calculated qe values agreed well with the experimental ones.
This indicated that the kinetics data fitted perfectly well with the pseudo-second-order model. This model assumes that, the rate-controlling step in the removal of 2,6-DCP by adsorption with MTCNS is chemisorptions involving valence forces through sharing or exchanging of electrons between adsorbent and adsorbate (Parate ; Talib, 2015). According to Intra-particle diffusion model, the intercept (C) of the plots qt versus t1/2 (Appendix D) give an idea about boundary layer thickness. The larger the intercept, greater the boundary layer effect, and if the plots qt versus t1/2 pass through the origin then intra-particle diffusion is the rate-controlling step. When the plots do not pass through the origin, this is indicative of some degree of boundary layer control and this further show that the intra-particle diffusion is not the only rate-limiting step, but also other kinetic models may control the rate of adsorption, all of which may be operating simultaneously (Arami et al., 2008). It can be seen from Figure 4.10; the interception of the line does not pass through the origin showing that the mechanism of adsorption is not solely governed by intra-particle diffusion process.
In a view of these both considerations, we may conclude that the pseudo-second-order mechanism is predominant. Similar observations have been reported for the adsorption of chlorophenols onto other single adsorbents (Wang et al., 2011; Agarry et al., 2013).
