The biosorption capability of Chrysanthemum indicum to remove nickel ions from aqueous solution in a fixed-bed column was examined in this study. Native C. indicum flower waste was improved for its biosorptive potential by pyrolysis to obtain its biochar form and, thereby, both raw (CIF-R) and biochar (CIF-BC) forms of the flower were used for Ni(II) removal. Fixed bed column studies were conducted to examine the influence of bed height (1.0–3.0 cm), flow rate (1.0–5.0 mL min−1) and initial metal ion concentration (25–75 mg L−1). The breakthrough curves (Cout/Cin vs time) were modelled using different dynamic adsorption models, viz. Adams-Bohart, Thomas and Yoon-Nelson model. Interpretation of the data revealed a favorable correlation with the Thomas model with higher R2 values and closer model-predicted and experimental biosorption capacity values. The equilibrium uptake capacity of CIF-R and CIF-BC for Ni(II) were found to be 14.02 and 29.44 mg g−1, respectively. Further, the column was regenerated using HCl as eluent, to desorb the adsorbed Ni(II) ions. The experimental results implied and affirmed the suitability of the biosorbents for nickel ion biosorption with its nature being favorable, efficient, and environmentally friendly.
INTRODUCTION
Heavy metals discharged from untreated industrial and municipal effluents are among the main sources of water pollution in the recent past. In the environmental context, heavy metals are defined as those which form positive ions in solution and have densities five times greater than that of water (Anandkumar & Mandal 2009). The acute toxicity and carcinogenicity associated with metal ions discharged into the aquatic system pose a serious threat to the environment. These heavy metals find their way into the microenvironment, the aquatic flora and fauna and, in turn, into the food chain exhibiting direct health effects on humans (Hashim et al. 2011). Nickel is a hard, silvery white, ferromagnetic, naturally occurring transition metal. Anthropogenic sources of nickel release into the environment includes, nickel mining operations, and industries like nickel-cadmium battery, nickel mining and smelting, and nickel alloy production units that exploit nickel, nickel compounds and nickel alloys for their production process. Exposure to higher concentrations of nickel can lead to lung embolism, respiratory failure, allergic dermatitis, higher chances of developing lung, nose, larynx and prostate cancer (Das et al. 2008). Considering the health hazards associated with nickel and its compounds and to meet the desired discharge standards, nickel concentration in industrial effluent should be reduced and treated effectively.
Various treatment techniques have been developed and adopted to mitigate heavy metal pollution, including, chemical precipitation, ion-exchange, adsorption, electro-coagulation, flocculation, membrane filtration (Fu & Wang 2011). The extent of applicability of a technique depends on the initial metal ion concentration, the capital investment and operating costs, presence of other components in wastewater, reliability of the technique and its environmental impacts. Considering all these factors, biosorption technique proves to be advantageous in terms of economics, ease, and in being environment-friendly and sustainable in removing heavy metal ions from aqueous solution.
Biosorption is a metal ion sequestration process that utilizes biomaterials for sorbing, sequestering and immobilizing inorganic heavy metal ions from aqueous solution (Volesky 2001). Biosorption is recognized as an effective and economic method for heavy metal removal with the advantages of being flexible in design and operation, producing high-quality treated effluent, in addition to being reversible as the biosorbents can be regenerated by suitable desorption methods (Fang et al. 2013). Column studies are essential for the application of the technology at pilot-plant and industrial scale treatment. Laboratory fixed-bed column studies are conducted to determine the effect of bed height, initial metal ion concentration and flow rate that could possibly affect the capacity of the biosorbent in removing metal ions from aqueous solution. Based on the metal binding capacities of various plant-based biomass, heavy metals can be separated from industrial effluents by a passive binding process (Choi & Yun 2006). The efficiency of biosorbents is based on their capacity, affinity and specificity for metal ions along with their physicochemical nature. Plant-based wastes, agricultural and forestry residues in their natural or modified form have been effective in nickel ion sequestration, as reported by many researchers (Oliveira et al. 2005; Parab et al. 2006; Bhatnagar & Minocha 2010; Reddy et al. 2011; Flores-Garnica et al. 2013; Chithra et al. 2014; Sudha et al. 2015).
The biosorbents can be enhanced to have the desired physical and chemical attributes to enrich its biosorptive capability and increase its affinity towards metal ion uptake from aqueous solution. Physical activation of biosorbents includes a range of thermochemical conversion technologies including pyrolysis, combustion and gasification. Pyrolysis, considered as the representative technology has been widely employed for biomass conversion to biochar and its subsequent use as biosorbent for metal ion removal. The properties of biochar including the porous structure, aromatic surface and the presence of oxygen functional groups makes it appropriate for entrapping the metal ions from aqueous solution (Inyang et al. 2012). Conversion of waste biomass to biochar and further applying it for pollutant remediation is an eco-friendly way of waste management and pollutant alleviation. Application of biochar for various pollution remediation strategies has been gaining widespread attention, due to its characteristic affinity for contaminants and its potential carbon sequestration ability (Inyang & Dickenson 2015).
In this research work, Chrysanthemum indicum, a flowering herb commonly called as ‘chrysanths’ and known mostly for its ornamental and decorative purpose, is studied for its biosorptive potential. C. indicum are grown in abundance and the flowers of the plant, after their decorative use, have no significant value and are considered waste. Considering its relative adequacy and abundant availability, these flowers can be a potential resource of natural biosorbent (Teixeira da Silva et al. 2013). Hence, in this study, C. indicum in its native and biochar form were used as biosorbents to remove Ni(II) ions from aqueous solution in continuous mode in a fixed-bed column. The biosorption capacity of C. indicum was investigated in terms of its effect on varying bed height, flow rate and initial nickel ion concentration. The column experimental data were evaluated and fitted using different dynamic adsorption models, viz. Adams-Bohart, Thomas and Yoon-Nelson models.
MATERIALS AND METHODS
Preparation of Ni(II) stock solution
Nickel ion stock solution of 1.0 g L−1 was prepared by dissolving an accurately weighed amount of NiSO4·7H2O (SDFCL, Mumbai) in 1 L of double distilled water. The metal ion stock solution was diluted to obtain Ni(II) ion concentrations of 25, 50 and 75 mg L−1. 0.1 M HCl was used as desorbing agent. Double distilled water was used for all dilutions and column experiments. All chemicals and reagents used in this study were of analytical reagent grade and were used without any further purification.
Preparation and characterization of biosorbents
The dried flowers of the C. indicum plant were collected and their petals were removed and washed with tap water to remove any dust particles adhered to them. The washed material was dried in an air oven at 80 °C for 12 h and subsequently powdered using a domestic mixer. The powder was further passed through a 150 μ sieve to obtain C. indicum flower biomass in its raw state (CIF-R). CIF-R was carbonized in a muffle furnace at 650 °C for a holding time of 60 min under oxygen limiting conditions to yield the biochar form of C. indicum (CIF-BC). The pyrolyzing temperature and holding time were chosen based on a series of trial experiments. CIF-R and CIF-BC were further used in column experiments to adsorb nickel ions from aqueous solution. The BET surface area of CIF-R and CIF-BC are 0.783 and 1.726 m2g−1, respectively (Vilvanathan & Shanthakumar 2016). The X-diffraction (XRD) pattern of the biosorbents, viz. CIF-R and CIF-BC were studied before and after the biosorption of Ni(II) ions employing a BRUKER D8 Advance Powder XRD using Cu-Kα radiation over the 2θ angle range of 5–80°.
Column biosorption experiments
Column data analysis
In the above equations, Q and ttotal represent the volumetric flow rate (mL min−1) and total flow time (min), respectively, Cin and Cout are the inlet and outlet metal ion concentrations (mg L−1), respectively, and m is the unit mass of biosorbent packed in the column (g).
Column modelling
To examine the column parameters and its interdependence on the column performance, adsorption column models are applied to the experimental data. Various mathematical models have been proposed to evaluate the effect of process variables on the efficiency of biosorption process for heavy metal removal in a fixed-bed column. Adams-Bohart, Thomas and Yoon-Nelson models are among the widely applied models for column data interpretation.
Adams-Bohart model
Thomas model
Yoon-Nelson model
Desorption studies
RESULTS AND DISCUSSION
XRD analysis of CIF-R and CIF-BC
XRD patterns of (a) CIF-R (b) CIF-BC before and after the biosorption of Ni(II) ions.
Influence of process parameters
Fixed-bed column biosorption studies were carried out to evaluate the effect of various process variables, viz. bed height, flow rate and initial concentration of Ni(II) on the breakthrough curve and biosorption capacity. The shape of the breakthrough curve (Cout/Cin vs time) and time of breakthrough point are predominant factors that determine the dynamic behavior of the column. In this study, breakthrough time (tb) is taken as the time at which the metal ion concentration in the outlet (Cout) reaches 5% of the inlet metal ion concentration (Cin) and the saturation time (tsat) is the point when Cout is 95% of Cin.
Effect of bed height
Fixed-bed column model parameters of Ni(II) biosorption onto CIF-R
Column parameters . | Experimental conditions . | Adams-Bohart model . | Thomas model . | Yoon-Nelson model . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Co (mg L−1) . | Q (mL min−1) . | Z (cm) . | Veff (mL) . | mtotal (mg) . | qtotal (mg) . | qeq (mg g−1) . | KAB (L mg−1 min−1) . | No (mg L−1) . | R2 . | KTH (mL min−1mg−1) . | qo (mg g−1) . | R2 . | KYN (min−1) . | τ (min) . | R2 . |
50 | 1 | 1 | 600 | 30 | 14.02 | 14.02 | 0.00032 | 117.45 | 0.805 | 0.00022 | 14.75 | 0.950 | 0.01149 | 294.97 | 0.926 |
50 | 1 | 2 | 660 | 33 | 19.24 | 9.62 | 0.00030 | 195.42 | 0.754 | 0.00020 | 9.82 | 0.935 | 0.01009 | 292.95 | 0.912 |
50 | 1 | 3 | 720 | 36 | 20.76 | 6.92 | 0.00028 | 370.47 | 0.776 | 0.00018 | 7.05 | 0.989 | 0.00999 | 287.79 | 0.954 |
50 | 1 | 1 | 600 | 30 | 14.02 | 14.02 | 0.00032 | 117.45 | 0.805 | 0.00022 | 14.75 | 0.950 | 0.01149 | 294.97 | 0.926 |
50 | 2.5 | 1 | 1,050 | 52.5 | 6.48 | 6.48 | 0.00042 | 62.89 | 0.655 | 0.00024 | 8.00 | 0.824 | 0.01213 | 168.03 | 0.813 |
50 | 5 | 1 | 1,500 | 75 | 4.55 | 4.55 | 0.00047 | 45.02 | 0.720 | 0.00028 | 5.17 | 0.757 | 0.01234 | 156.68 | 0.724 |
25 | 1 | 1 | 720 | 18 | 8.09 | 8.09 | 0.00088 | 52.78 | 0.462 | 0.00039 | 9.36 | 0.986 | 0.01184 | 374.46 | 0.964 |
50 | 1 | 1 | 600 | 30 | 14.02 | 14.02 | 0.00032 | 117.45 | 0.805 | 0.00022 | 14.75 | 0.950 | 0.01149 | 294.97 | 0.926 |
75 | 1 | 1 | 540 | 40.5 | 19.27 | 19.27 | 0.00024 | 149.91 | 0.749 | 0.00012 | 21.31 | 0.824 | 0.00904 | 286.48 | 0.513 |
Column parameters . | Experimental conditions . | Adams-Bohart model . | Thomas model . | Yoon-Nelson model . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Co (mg L−1) . | Q (mL min−1) . | Z (cm) . | Veff (mL) . | mtotal (mg) . | qtotal (mg) . | qeq (mg g−1) . | KAB (L mg−1 min−1) . | No (mg L−1) . | R2 . | KTH (mL min−1mg−1) . | qo (mg g−1) . | R2 . | KYN (min−1) . | τ (min) . | R2 . |
50 | 1 | 1 | 600 | 30 | 14.02 | 14.02 | 0.00032 | 117.45 | 0.805 | 0.00022 | 14.75 | 0.950 | 0.01149 | 294.97 | 0.926 |
50 | 1 | 2 | 660 | 33 | 19.24 | 9.62 | 0.00030 | 195.42 | 0.754 | 0.00020 | 9.82 | 0.935 | 0.01009 | 292.95 | 0.912 |
50 | 1 | 3 | 720 | 36 | 20.76 | 6.92 | 0.00028 | 370.47 | 0.776 | 0.00018 | 7.05 | 0.989 | 0.00999 | 287.79 | 0.954 |
50 | 1 | 1 | 600 | 30 | 14.02 | 14.02 | 0.00032 | 117.45 | 0.805 | 0.00022 | 14.75 | 0.950 | 0.01149 | 294.97 | 0.926 |
50 | 2.5 | 1 | 1,050 | 52.5 | 6.48 | 6.48 | 0.00042 | 62.89 | 0.655 | 0.00024 | 8.00 | 0.824 | 0.01213 | 168.03 | 0.813 |
50 | 5 | 1 | 1,500 | 75 | 4.55 | 4.55 | 0.00047 | 45.02 | 0.720 | 0.00028 | 5.17 | 0.757 | 0.01234 | 156.68 | 0.724 |
25 | 1 | 1 | 720 | 18 | 8.09 | 8.09 | 0.00088 | 52.78 | 0.462 | 0.00039 | 9.36 | 0.986 | 0.01184 | 374.46 | 0.964 |
50 | 1 | 1 | 600 | 30 | 14.02 | 14.02 | 0.00032 | 117.45 | 0.805 | 0.00022 | 14.75 | 0.950 | 0.01149 | 294.97 | 0.926 |
75 | 1 | 1 | 540 | 40.5 | 19.27 | 19.27 | 0.00024 | 149.91 | 0.749 | 0.00012 | 21.31 | 0.824 | 0.00904 | 286.48 | 0.513 |
Fixed-bed column model parameters of Ni(II) biosorption onto CIF-BC
Column parameters . | Experimental conditions . | Adams-Bohart model . | Thomas model . | Yoon-Nelson model . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Co (mg L−1) . | Q (mL min−1) . | Z (cm) . | Veff (mL) . | mtotal (mg) . | qtotal (mg) . | qeq (mg g−1) . | KAB (L mg−1 min−1) . | No (mg L−1) . | R2 . | KTH (mL min−1 mg−1) . | qo (mg g−1) . | R2 . | KYN (min−1) . | τ (min) . | R2 . |
50 | 1 | 1 | 600 | 30 | 14.72 | 29.44 | 0.00053 | 95.14 | 0.795 | 0.00025 | 32.14 | 0.946 | 0.01259 | 321.42 | 0.931 |
50 | 1 | 2 | 660 | 33 | 15.82 | 15.82 | 0.00051 | 184.08 | 0.862 | 0.00023 | 18.23 | 0.835 | 0.01140 | 284.59 | 0.814 |
50 | 1 | 3 | 720 | 36 | 20.54 | 13.69 | 0.00048 | 338.90 | 0.666 | 0.00020 | 14.14 | 0.972 | 0.01063 | 262.07 | 0.943 |
50 | 1 | 1 | 600 | 30 | 14.72 | 29.44 | 0.00053 | 95.14 | 0.795 | 0.00025 | 32.14 | 0.946 | 0.01259 | 321.42 | 0.931 |
50 | 2.5 | 1 | 1,050 | 52.5 | 6.76 | 13.52 | 0.00064 | 45.50 | 0.758 | 0.00029 | 14.74 | 0.942 | 0.01457 | 218.97 | 0.916 |
50 | 5 | 1 | 1,350 | 67.5 | 3.99 | 7.98 | 0.00069 | 34.08 | 0.475 | 0.00032 | 9.05 | 0.821 | 0.01542 | 132.10 | 0.807 |
25 | 1 | 1 | 660 | 16.5 | 9.15 | 18.30 | 0.00111 | 53.45 | 0.729 | 0.00052 | 18.85 | 0.957 | 0.01311 | 376.93 | 0.926 |
50 | 1 | 1 | 600 | 30 | 14.72 | 29.44 | 0.00053 | 95.14 | 0.795 | 0.00025 | 32.14 | 0.946 | 0.01259 | 321.42 | 0.931 |
75 | 1 | 1 | 420 | 31.5 | 15.16 | 30.33 | 0.00044 | 127.69 | 0.798 | 0.00021 | 32.15 | 0.960 | 0.01557 | 267.66 | 0.926 |
Column parameters . | Experimental conditions . | Adams-Bohart model . | Thomas model . | Yoon-Nelson model . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Co (mg L−1) . | Q (mL min−1) . | Z (cm) . | Veff (mL) . | mtotal (mg) . | qtotal (mg) . | qeq (mg g−1) . | KAB (L mg−1 min−1) . | No (mg L−1) . | R2 . | KTH (mL min−1 mg−1) . | qo (mg g−1) . | R2 . | KYN (min−1) . | τ (min) . | R2 . |
50 | 1 | 1 | 600 | 30 | 14.72 | 29.44 | 0.00053 | 95.14 | 0.795 | 0.00025 | 32.14 | 0.946 | 0.01259 | 321.42 | 0.931 |
50 | 1 | 2 | 660 | 33 | 15.82 | 15.82 | 0.00051 | 184.08 | 0.862 | 0.00023 | 18.23 | 0.835 | 0.01140 | 284.59 | 0.814 |
50 | 1 | 3 | 720 | 36 | 20.54 | 13.69 | 0.00048 | 338.90 | 0.666 | 0.00020 | 14.14 | 0.972 | 0.01063 | 262.07 | 0.943 |
50 | 1 | 1 | 600 | 30 | 14.72 | 29.44 | 0.00053 | 95.14 | 0.795 | 0.00025 | 32.14 | 0.946 | 0.01259 | 321.42 | 0.931 |
50 | 2.5 | 1 | 1,050 | 52.5 | 6.76 | 13.52 | 0.00064 | 45.50 | 0.758 | 0.00029 | 14.74 | 0.942 | 0.01457 | 218.97 | 0.916 |
50 | 5 | 1 | 1,350 | 67.5 | 3.99 | 7.98 | 0.00069 | 34.08 | 0.475 | 0.00032 | 9.05 | 0.821 | 0.01542 | 132.10 | 0.807 |
25 | 1 | 1 | 660 | 16.5 | 9.15 | 18.30 | 0.00111 | 53.45 | 0.729 | 0.00052 | 18.85 | 0.957 | 0.01311 | 376.93 | 0.926 |
50 | 1 | 1 | 600 | 30 | 14.72 | 29.44 | 0.00053 | 95.14 | 0.795 | 0.00025 | 32.14 | 0.946 | 0.01259 | 321.42 | 0.931 |
75 | 1 | 1 | 420 | 31.5 | 15.16 | 30.33 | 0.00044 | 127.69 | 0.798 | 0.00021 | 32.15 | 0.960 | 0.01557 | 267.66 | 0.926 |
Effect of bed height on Ni(II) removal using (a) CIF-R and (b) CIF-BC.
Effect of flow rate
Effect of flow rate on Ni(II) removal using (a) CIF-R and (b) CIF-BC.
Effect of initial Ni(II) concentration
Effect of initial ion concentration on Ni(II) removal using (a) CIF-R and (b) CIF-BC.
Column biosorption modelling
Adams-Bohart model
Experimental column biosorption data of Ni(II) removal using CIF-R and CIF-BC were analyzed using Adams-Bohart model. The values of the model characteristic parameters, viz. maximum saturation metal ion concentration (No) and Adams-Bohart rate constant (KAB) were determined from the slope and intercept of the model plot. Tables 1 and 2 present the calculated Adams-Bohart constant values for Ni(II) biosorption onto CIF-R and CIF-BC, respectively, for the different experimental parameters. It can be observed that the correlation co-efficient (R2 < 0.8) values are relatively less and the values of KAB increased with increasing flow rate, while it decreased with increasing bed height and initial Ni(II) concentration. In addition, No values increased with increasing bed height and initial ion concentration and decreased with increasing flow rate for both the biosorbents (Woumfo et al. 2015). The phenomena can be reasonably attributed to the system kinetics that is explicitly dominated by external mass transfer in the initial part of the breakthrough curve.
Thomas model
Breakthrough curves for the biosorption of nickel ions onto CIF-R and CIF-BC were further analyzed using the Thomas model. The values of the constants, KTH and qo, and correlation coefficients were determined from the model plot of ln(Cin/Cout − 1) vs time and are presented in Tables 1 and 2 for CIF-R and CIF-BC, respectively. It can be observed from the tables that, as the bed height and inlet Ni(II) ion concentration increases, the KTH value decreases while it increases for increasing flow rate. The R2 values are found to be higher for the biosorption of nickel ions onto both the biosorbents. The qo values calculated from the Thomas model were also found to be closer to the experimentally obtained values. Thus, the linearized Thomas model adequately describes the experimental breakthrough data as evident from the values obtained from the model. The suitability of Thomas model to the experimental data indicates external and internal diffusions are not the only rate-limiting steps in the biosorption process. Similar trends have also been reported in previous studies (Xu et al. 2013).
Yoon-Nelson model
The Yoon-Nelson model was applied to determine the extent of its suitability to the column biosorption of Ni(II) onto CIF-R and CIF-BC. The model constants (KYN and τ) and correlation coefficient values are presented in Tables 1 and 2, respectively, for all the experimental conditions. The interpolation of the model plot showed that the values of KYN decreased for increasing bed depth and inlet nickel ion concentration, while it increased for increasing flow rate (Foo et al. 2013). The 50% breakthrough time ‘τ’ decreased for the increasing range of flow rate and initial ion concentrations, as the column saturates quickly. The difference in experimental and predicted τ values indicated that the Yoon-Nelson model does not give an appropriate fit to the experimental column data on continuous biosorption of nickel ions.
Column desorption studies
Comparative desorption efficiency of 0.1 M HCl to elute Ni(II) ions from (a) CIF-R and (b) CIF-BC in consecutive cycles.
CONCLUSION
Continuous biosorption studies for the removal of nickel ions using native and biochar forms of C. indicum flower waste were carried out in this study. The study revealed a promising potential of both the biosorbents, viz. CIF-R and CIF-BC, to remove the metal ion with biosorption capacity values of 14.02 and 29.44 mg g−1, respectively, for an initial Ni(II) concentration of 50 mg L−1, 1.0 cm bed height and 1.0 mL min−1 flow rate in a fixed-bed column. The study of influencing factors including bed height, flow rate and initial ion concentration elucidated the interdependence of each factor on the dynamic adsorption of nickel ions from aqueous solution. Breakthrough curve modelling using different models provided the best fit of experimental column data with the Thomas model for both the biosorbents for nickel ion removal. Column desorption studies further elicited the significance of CIF-R and CIF-BC as being efficient and eco-friendly in adsorbing as well as desorbing the adsorbed nickel ions.
ACKNOWLEDGEMENTS
The authors would like to thank VIT University for providing Atomic Absorption Spectroscopy (AAS) facility to perform metal concentration analysis and Department of Chemistry, IIT Madras, Chennai for providing the facility to carry out powder XRD analysis.