Use of rice straw to solve burning problem,Removal of Ni (II) ions from aqueous solutions using modified rice straw in a fixed bed column

This paper investigates the ability of modified rice straw, an agricultural biomaterial, to remove Ni (II) ions from aqueous solution in a fixed-bed column. The experiments were performed with different bed heights (1.5 and 2.0 cm), influent Ni (II)
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  Removal of Ni (II) ions from aqueous solutions using modified rice strawin a fixed bed column Reena Sharma, Baljinder Singh ⇑ Department of Biotechnology, Panjab University, Chandigarh 160014, India h i g h l i g h t s   Removal of Ni (II) ions using modified rice straw in a packed column is proposed.   Experiment relies on different bed height and influent Ni (II) concentration.   The Adams–Bohart, Thomas and Yoon–Nelson models used to predict model parameters. a r t i c l e i n f o  Article history: Received 31 May 2013Received in revised form 18 July 2013Accepted 21 July 2013Available online 6 August 2013 Keywords: Modified rice straw powderFixed bed columnNi (II) ionsAdsorption a b s t r a c t This paper investigates the ability of modified rice straw, an agricultural biomaterial, to remove Ni (II)ions from aqueous solution in a fixed-bed column. The experiments were performed with differentbed heights (1.5 and 2.0 cm), influent Ni (II) concentrations (50, 75 and 100 mg/L) using flow rates(500 l l/min) in order to obtain experimental breakthrough curves. The maximum adsorption capacityof rice straw powder (RSP) was 43 mg/L at 75 mg/L influent concentration of divalent Ni (II) ions at2 cm bed depth. Adams–Bohart model, Thomas model and Yoon and Nelson kinetic models were usedto analyze the column performance. The value of rate constant for Adams–Bohart and Yoon and Nelsonmodel decreased with increase of influent concentration, but increased with increasing bed depth. Therate constant for Thomas model increased with initial influent Ni (II) ions concentration, decreased withincrease in bed depth.   2013 Elsevier Ltd. All rights reserved. 1. Introduction Pollutionby heavy metal ions is one of the majorenvironmentalproblems. This is due to rapid industrialization, which has createda major global concern. Aqueous effluents emanating from manyindustries contain dissolved heavy metals (Keng et al., 2013; Yadavet al., 2013). If these discharges are emitted without treatment,they may have an adverse impact on the environment. Ni (II) ionsare used in large amounts in leather tanning, electroplating, ce-ment, photography and in dye industries (Adedirin et al., 2011;Kadirvelu et al., 2001a). Ni (II) ions can exist as Ni 0 or Ni 2+ Theseare carcinogenic in animals (Sekhon and Gill, 2013) and most fre-quently encountered in waste water effluent released from indus-tries, such as non-ferrousmetal, mineralprocessing, electroplating,copper sulfate manufacturing, and battery and accumulator manu-facturing. Permissible limits for Ni (II) ions in drinking water givenby World Health Organization (WHO) are 0.01 mg/L. Higher con-centration can cause lung cancer, nose and bone related diseases.Acute poisoning of Ni (II) ions leads to severe headache, drycough, shortness of breath, rapid respiration, cyanosis, extremeweakness dizziness, vomiting, chest pain and sickness (Malkoc,2011). The increased awareness of the toxicity of heavy metalshas led to a dramatic increase in research on various strategies thatmay be employed to clean up the environment. The methods cur-rently used for removal of heavy metals includes solvent extrac-tion, ion-exchange, electroflotation, membrane separation,reverseosmosis,chemicalprecipitation,and electro dialysis(Yadavet al., 2013) which are expensive and can result in the generationof toxic sludge that is another serious problem (Pahlavanzadehaet al., 2010). The limitation faced by physical and chemical treat-ment technologies could be overcome with the help of agriculturewastes (Lehmann et al., 1999).The ability of some agricultural wastes to adsorb heavy metalsfrom aqueous effluents has been reported in literature (Shuklaet al., 2002; Kadirvelu et al., 2001b; Yu et al., 2001). Rice ( Oryzasativa  L.) is the world’s second largest cereal crop after wheatand more than 50% of the world’s population used it as staple food.At least 114 countries in the world grow rice and it produces largeamounts of crop residues. Only about 20% of rice straw is used forpurposes such as ethanol, paper, fertilizers and fodders. Rice straw 0960-8524/$ - see front matter    2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.biortech.2013.07.146 ⇑ Corresponding author. Tel.: +91 172 2534076; fax: +91 172 2541409. E-mail addresses:  anji14dec@gmail.com (R. Sharma), gilljwms2@gmail.com(B. Singh).Bioresource Technology 146 (2013) 519–524 Contents lists available at ScienceDirect Bioresource Technology journal homepage: www.elsevier.com/locate/biortech  burning is a common post-harvest practice and cause’s air pollu-tion called the ‘‘Black Cloud’’ (Kögel-Knabner et al., 2010).Rice straw is one of the most abundant lignocellulosic wastematerials overall the world. Rice straw has many characteristicswhich make it a potential adsorbent with binding sites that arecapable to remove metals from aqueous solutions. Chemicalcomposition of rice straw is predominantly contains cellulose(32–47%), hemicellulose (19–27%) and lignin (5–24%) (Tarleyet al., 2004).The objective ofthe present study is toevaluate the efficiencyof rice straw bed column for removing and recovering heavy metalsfrom contaminated industrial effluent through continuous system.The effects of different parameters, such as column bed depth andinfluentNi (II) ions concentration were investigatedusing a labora-tory scale fixed-bed column. The % removal efficiency curves forthe adsorption of Ni (II) were analyzed and Adams–Bohart, Thomasand Yoon–Nelson models were also analyzed to study the dynamicbehavior of the column. 2. Methods  2.1. Preparation of rice straw powder  Rice straw was collected from a cultivated area near Patiala, inPunjab, India. It was washed with de-ionized water several timesand was heated in an oven at 60   C for 72 h to remove all the mois-ture present in the material. Oven-dried straw crushed, groundedand then sieved to desired mesh size (300–500 l m) for use. Thispowder was stored in glass bottle prior to use.  2.2. Modification of the rice straw powder  Rice straw was treated with sodium hydroxide (0.1 M) solutionto increase the adsorption property of the adsorbent (Tarley et al.,2004). Solution of NaOH was taken in a beaker provided with a lid.The resulting mixture (Rice straw powder (RSP) and NaOH solu-tion) was stirred for one hour, later the contents of the beaker werethoroughlywashed withdouble distilledwaterand filtered. Then itwas dried for 48 h at 60   C and stored in a closed glass bottle.  2.3. Reagent preparation A stock solution (1000 mg/L) of the Ni (II) ions under study wasprepared by dissolving an appropriate weight of pure salt (NiSO 4 )in the desired volume of de-ionized water. The stock solutionwas successively diluted with de-ionized water to obtain the de-sired test concentration of metal ion.  2.4. Column Continuous-flow sorption experiments were conducted in aglass column. The column was designed with an internal diameterof 2 and 30 cm in length. At the bottom of the column, a 0.5 mmstainless sieve was attached followed by glass wool. A 1.5 and2 cm high layer of modified RSP was placed at the column basein order to provide a uniform inlet flow of the solution into thecolumn.  2.5. Experimental procedure Columnwas packed with1.5 g and 2 g of modified RSP to obtaina particular bed height of the adsorbent (equal to 1.5, 2.0 cm of beddepth) keeping flow rate constant (500 l l/min) and varied influentconcentration (50 mg/L, 75 mg/L and 100 mg/L). Effluent was col-lected at regular time intervals to determine the concentration of metal in the effluent. Flow to the column continued until therewas no further adsorption. The metal concentration in the effluentwas determined to find out the adsorption capacity of the ricestraw. Samples were collected from the exit of the column at dif-ferent time intervals and analyzed for Ni (II) concentration usingAtomic Absorption Spectrophotometer (AAS) using a GBC AVANTAGF 5000 Model.  2.6. Column data analysis To determine the response of sorption column, time for break-through appearance and the shape of the breakthrough curve wereimportant factors (Kumar and Bandyopadhyay, 2006). Break-through curves showed the performance of fixed-bed column.The breakthrough point was the point where effluent concentra-tion ( C  t ) from the column was about 0.1% of the influent concentra-tion ( C  0 ). The point of column exhaustion was where the effluentconcentration reaches 95% (Kumar and Bandyopadhyay, 2006).The breakthrough curve was represented by  C  t / C  0  as a function of time/volume of the effluent for a given bed depth. The columnwas analyzedfor theadsorption of Ni(II) ions atvarious concentra-tions i.e. 50 mg/L, 75 mg/L, and 100 mg/L. Bed depth was kept 1.5and 2 cm for each concentration. Q   was the volumetric flow rate (mL/min) can be calculated byfollowing equation: V  eff   ¼ Q t  total  ð 1 Þ The value of the total mass of metal adsorbed,  q total  (mg): q total  ¼ Q  = 1000 Z   t  ¼ total t  ¼ 0 C  ad dt  ð 2 Þ Equilibrium metal uptake or maximum capacity of thecolumn: q eq  (mg/g), in the column is calculated as the following: Nomenclature  A  the cross-sectional area of the bed, cm 2 C  ad  the concentration of metal removal, mg/L  C  0  the influent concentration, mg/L  C  t  the effluent concentration, mg/L  k AB  the kinetic constant, L/mg min k Th  the Thomas model constant, mL/min mg k YN  the rate constant, min  1 m total  total amount of metal ion sent to column, g N  0  the saturation concentration, mg/L  q 0  the adsorption capacity, mg/g q eq  equilibrium metal uptake or maximum capacity of thecolumn, mg/g q total  the total mass of metal adsorbed, mg Q   the volumetric flow rate, cm 3 /min t   the total flow time, min t  total  the total flow time, min V  eff   the effluent volume, mL   Z   the bed depth of the fix-bed column, cm Y   the removal percent of Ni (II) ions,% N  0  the saturation concentration, mg/L  520  R. Sharma, B. Singh/Bioresource Technology 146 (2013) 519–524  q eq  ¼ q total = m  ð 3 Þ where,  m  was the dry weight of adsorbent in the column (g). Total amount of metal ion entering column ( m total ) is calculatedusing following equation (Kundu and Gupta, 2005): m total  ¼ C  0 Qt  total = 1000  ð 4 Þ The removal % of Ni (II) ions can be obtained from Eq. Y  % ¼ q total = m total  100  ð 5 Þ The removal efficiency of divalent Ni (II) ions was given by: %  Removal efficiency ¼ C  0  C  t = C  0  100  ð 6 Þ where,  C  0  and  C  t  are the srcinal and residual Ni (II) ion concentra-tions in solution, respectively. 3. Results and discussion  3.1. Effect of influent concentration on % removal efficiency The sorption column data were evaluated and presented inElectronic Supplementary Tables 1 and 2. Parameters in fixed-bed column for adsorption of Ni (II) ion presented in ElectronicSupplementary Tables 1 and 2 for bed depth of 2 and 1.5 cmrespectively. It was detected from the Electronic SupplementaryFigs. 1 and 2 that the% adsorption decreased with increase in initialconcentration of the metal. But the uptake capacity increased withincreaseininitial concentration,whichmaybedueto theavailabil-ity of more number of Ni (II) ions in solution. Moreover, higher ini-tial adsorbate concentration provided higher driving force toovercome all mass transfer resistances of the metal ions from theaqueous to the solid phase resulting in higher probability of colli-sion between Ni (II) ions and the active sites of RSP. This may beattributed to high influent concentration providing higher drivingforce for the transfer process to overcome Ni (II) ions mass transferresistance (Baral et al., 2009). The maximumadsorption capacity of RSP was 43 mg/L at 75 mg/L influent concentration of divalent Ni(II) ions at 2 cm bed depth. These results demonstrated that higherinfluent concentrations led to higher driving force for mass trans-fer, hence the adsorbent achieved saturation more quickly, whichresulted in decrease of exhaust time and adsorption zone length(Malkoc et al., 2006).  3.2. Effect of bed depth on% removal efficiency Effect of contact time on removal of Ni (II) ions by modified ricestraw was studied by variation of contact time for different initialconcentrations. Relationship between contact time and % removalefficiency of Ni (II) ion is shown in Electronic SupplementaryFigs. 1and 2 at a constant bed depth (2 cm and 1.5 cm respec-tively). It is cleared that with increase in time, concentration of effluent decreased and removal efficiency increased (Chen et al.,2012). The increase in Ni (II) ions uptake capacity with increasingcontacttimein thefixed bed columnmay bedue to increasein sur-face area, which provided more binding site for column adsorption(Gupta and Babu, 2009). Kumar et al. (2012) studied removal of  metal ions by cynobacteria and found that % metal removal inde-pendent ofmatthickness(0.2–1.6 mm),in batch system.ElectronicSupplementary Tables 3 and 4 shows the % removal efficiency of Ni(II) ions had an increasing trend in column with the increase in themass of adsorbent (rice straw). The sorption column data wereevaluated and presented in Electronic Supplementary Tables 3and 4. These results reveal that the bed depth of 2 cm providedoptimum % removal efficiency. Therefore, the subsequent experi-ments were carried out with this bed depth.  3.3. Breakthrough curve modeling  A successful design of column adsorption process/methodmainly requires prediction of the breakthrough curve for the efflu-ent (Oguz and Ersoy, 2010). There are several simple mathematicalmodels have been developed for describing and analyzing the lab-scale column studies for the purpose of industrial applications.Adams–Bohart, Thomas and Yoon models were developed to iden-tify the best model for predicting the dynamic behavior of the col-umn (Subbaiah et al., 2011). Chen et al. (2012), have studied adsorption of hexavalent chromium from aqueous solution bymodifiedcornstalkand appliedsimilar breakthroughcurvemodel-ing to evaluate the model parameters of the fixed-bed column.  3.3.1. Adams–Bohart model Bohart and Adams (Bohart and Adams, 1920) describe the rela-tionship between  C  t / C  0  and  t   in a continuous system. It is used fordescribing the initial part of the breakthrough curve. The expres-sion is as follows: ln ð C  t = C  0 Þ¼ k AB C  0 t   k AB N  0 ð  Z  = U  0 Þ ð 7 Þ where  C  0  and  C  t  are the influent and effluent concentration (mg/L), k AB  is the kinetic constant (L/mg/min),  N  0  is the saturation concen-tration (mg/L),  Z   is the bed depth of the fix-bed column(cm) and  U  0 is the superficial velocity (cm/min) defined as the ratio of the volu-metricflow rate  Q   (cm 3 /min) to the cross-sectional area of the bed  A (cm 2 ),  k AB  and  N  0  can be calculated from the linear plot of ln ð C  t = C  0 Þ against time (Fig. 1a and b).As shown in Table 1a and b, the values of   k AB  decreased with increase of influent Ni (II) ions concentration,but increased with increasing bed depth. It was indicated that theoverall system kinetics was dominated by external mass transferin the initial part of adsorption in the column (Ahmad and Hameed,2010; Aksu and Gonen, 2004).  3.3.2. Thomas model Thomas model (Thomas, 1944) assumes plug flow behavior inthe bed. This is most general and widely used to describe the per-formance theory of the sorption process in fixed-bed column. Thelinearized form of this model can be described by the followingexpression: ln ð C  0 = C  t  1 Þ¼ k Th q 0 m = Q    k th C  0 t   ð 8 Þ where  k Th  is the Thomas model constant (mL/min/mg),  q 0  is theadsorption capacity (mg/g), and  t   stands for total flow time (min).The values of   k Th  and  q 0  can be determined from the linear plot of ln ½ð C  0 = C  t   1 Þ  against  t   (Fig. 2a and b). The Thomas model was suit-able for adsorption process, which indicated that the external andinternal diffusions were not the limiting step (Banerjee et al.,2012; Chen et al., 2012). From Table 2a and b, it could be seen that with the bed depthincreasing, the  k Th  values decreased. The value of   k Th  increasedwith initial influent Ni (II) ions concentration increasing. It wasattributed to the driving force for adsorption in the concentrationdifference. Thus the higher influent concentration and bed depthwould increase the adsorption of Ni (II) ions on the RSP column.  3.3.3. The Yoon–Nelson model It was used to describe the column adsorption data. Use of thismodel could minimize the error resulting from the use of the Tho-mas model, especially at lower or higher time periods of the break-through curve. The Yoon–Nelson model (Yoon and Nelson, 1984) isbased on the assumption that the rate of decrease in the probabil-ity of adsorption for each adsorbate molecule is proportional to theprobability of adsorbate adsorption and the probability of adsor-bate breakthrough on the adsorbent. The linearized Yoon–Nelsonmodel for a single component system can be expressed as: R. Sharma, B. Singh/Bioresource Technology 146 (2013) 519–524  521  ln ð C  t = C  0  C  t Þ¼ k YN t   s k YN  ð 9 Þ where  k YN  is the rate constant (min  1 ) and  s  is the time required for50% adsorbate breakthrough (min). A linear plot of ln ½ C  t = ð C  0   C  t Þ against  t   determined the values of   k YN  and  s  from the interceptand slope of the plot (Shown in the Fig. 3a and b). Different statistical parameters of the Yoon–Nelson model werecalculated and given in Table 3a and b . As shown in Table 3a and b the  k YN  values and the 50% breakthrough time  s  both decreasedwith increasing bed depth. The value of   s  significantly increasedat 75 mg/Lfor bed depth 2 cm as the influentNi (II) ions concentra-tion increased, because the saturation of the column occurredmore rapidly (Calero et al., 2009). Thus both the Thomas andYoon–Nelson models can be used to predict adsorption perfor-mance for adsorption of Ni (II) in a fixed-bed column.In general, rice straw is an environmentally friendly potentialbiosorbent for heavy metals. This work examined the efficiencyof this sorbent in removal of Ni (II) ions from aqueous environ-ment. The results indicated that several factors such as contacttime/bed depth, initial concentration affect the biosorption pro-cess. The physicochemical characteristics of waste water fromvarying sources can be much more complex compared to the aque-ous metal solution used in this study. Because of this, the effects of other components of waste water on commercial metal adsorptionprocess should be determined. However, this work can be consid-ered a preliminary study to conclude that rice straw is suitable andefficient material for the adsorption of Ni (II) ions from aqueoussolution. Adam–Bohart, Thomas and Yoon–Nelson models weresuccessfully used to predict the breakthrough curves, indicating Fig. 1.  Adam–Bohart model curve for (a) bed depth 2 cm and (b) bed depth 1.5 cm.  Table 1 Parameters of Adam–Bohart model under different conditions using (a) Fig. 1a (beddepth is 2 cm) and (b) Fig. 1b (bed depth is 1.5 cm). C  0  (mg/L)  Q   (ml/min)  Z   (cm)  K  AB  (L/mg/min)  N  0  (mg/L) a 50 1.071 2 8.81    10  4 4.36    10 3 75 0.875 2 0.23    10  4 4.50    10 3 100 0.583 2 3.90    10  4 1.54    10 3 b 50 0.273 1.5 3.8    10  4 3.9    10 3 75 0.498 1.5 1.2    10  4 0.1    10 3 100 0.183 1.5 5.6    10  4 1.9    10 3 Fig. 2.  Thomas model curve for (a) bed depth 2 cm and (b) bed depth 1.5 cm.  Table 2 Parameters of Thomas model under different conditions using (a) Fig. 2a (bed depth2 cm) and (b) Fig. 2b (bed depth 1.5 cm). C  0  (mg/L)  Q   (mL/min)  Z   (cm)  k Th  (mL/min mg  1 )  q 0  (mg/g) a 50 1.071 2 1.66    10  3 1.92    10 3 75 0.875 2 1.84    10  3 1.35    10 3 100 0.583 2 5.69    10  3 0.22    10 3 b 50 0.273 1.5 6.64    10  3 0.11    10 3 75 0.498 1.5 5.01    10  3 0.19    10 3 100 0.183 1.5 9.4    10  4 0.55    10 3 522  R. Sharma, B. Singh/Bioresource Technology 146 (2013) 519–524  that they were very suitable in RSP column designing. The resultsshowed that the modified rice straw has an excellent adsorptioncapacity for the removal of Ni (II) ions compared to other low-costadsorbents. 4. Conclusion Ni (II) ions biosorption by modified RSP was studied, using afixed bed column. The modified RSP efficiently removed Ni (II) ionsin fixed bed column. Uptake of Ni (II) ions through a fixed-bed col-umn was dependent on the bed depth, influent concentration. Themaximum adsorption capacity was at 75 mg/L influent concentra-tion and 2 cm bed depth. The Thomas, Adam–Bohart and Yoon–Nelson models were successfully used to predict the breakthroughcurves, indicating that they were very suitable for modified RSPcolumn design. It is considered to be a cheapest method for re-moval of Ni (II) ions in industrial effluent treatment method.  Acknowledgements The authors thank the Chairperson, Department of Biotechnol-ogy, and Dr. Kashmir Singh in Biotechnology, Panjab University,for providing necessary facilities.  Appendix A. Supplementary data Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.biortech.2013.07.146. References Adedirin, O., Adamu, U., Eddy, O., 2011. Biosorption of Cr (VI) and Ni (II) fromaqueous solution onto  Bacillus subtillis  immobilsed in Agarose gel. Der Chem.Sinica 2 (5), 173–188.Ahmad, A.A., Hameed, B.H., 2010. Fixed-bed adsorption of reactive azo dye ontogranular activated carbon prepared from waste. J. Hazard. 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Equilibrium, kinetic andthermodynamic studies on biosorption of Pb (II) and Cd (II) from aqueous Fig. 3.  Yoon–Nelson model curve for (a) bed depth 2 cm and (b) bed depth 1.5 cm.  Table 3 Parameters of Yoon–Nelson Model under different conditions using (a) Fig. 3a (beddepth 2 cm) and (b) Fig. 3b (bed depth 1.5 cm). C  0  (mg/L)  Q   (mL/min)  Z   (cm)  k YN , (min  1 )  q 0  (mg/g) a 50 1.071 2 0.049    10  2 8.49    10 3 75 0.875 2 0.010    10  2 4.20    10 3 100 0.583 2 0.057    10  2 7.76    10 3 b 50 0.273 1.5 0.026    10  2 18.6    10 3 75 0.498 1.5 0.044    10  2 10.2    10 3 100 0.183 1.5 0.093    10  2 36.6    10 3 R. Sharma, B. Singh/Bioresource Technology 146 (2013) 519–524  523
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