1-s2.0-S2213343713002431-main

Journal of Environmental Chemical Engineering 2 (2014) 46–55

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Journal of Environmental Chemical Engineering

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Recovering urea from human urine by bio-sorption onto Microwave ActivatedCarbonized Coconut Shells: Equilibrium, kinetics, optimization and field studies

Mahesh Ganesa Pillai *, Prithvi Simha, Ashita Gugalia

Mass Transfer Laboratory, Chemical Engineering Division, School of Mechanical and Building Sciences (SMBS), VIT University, Vellore 632014, Tamil Nadu, India

A R T I C L E I N F O

Article history:

Received 28 August 2013

Accepted 26 November 2013

Keywords:

Coconut shell

Human urine

Activated carbon

Response surface

Methodology

Urea

A B S T R A C T

Microwave Activated Carbonized Coconut Shell (MACCS) was used to recover urea from human urine.

Batch adsorption studies were conducted to evaluate the effect of initial adsorbate concentration (25%–

100%), contact time, carbon loading (1–3 g) and shaking speed (150–200 rpm) on the removal of urea at

30 8C. Microwave activation was performed at 180 W (microwave output power) for 10 min. The

sorption data were fitted to Langmuir, Freundlich, Tempkin, Flory–Huggins and Dubinin–Radushkevich

isotherm models. Results showed that the maximum monolayer adsorption capacity of the MACCS

powder was 256.41 mg g�1. The Flory–Huggins model was found to best describe the urea uptake

process since it demonstrated the minimum deviations from the experimental data. The kinetic data was

fitted to pseudo-first-order, pseudo-second-order and intra-particle diffusion models, and was found to

follow closely the pseudo-first order kinetic model. Based on the Central Composite Rotary Design, a five

factor interaction model and a quadratic model were respectively developed to correlate the adsorption

variables to the adsorption capacity. Field studies were conducted to determine the percentage biomass

increase and relative agronomic effectiveness for soil treated with the urea adsorbed MACCS powder.

Microwave activated carbonized coconut shell was shown to be a promising adsorbent for recovery and

removal of urea from human urine solutions.

� 2013 Elsevier Ltd. All rights reserved.

Introduction

The objective of any sanitation system is to protect and promotehuman health by breaking the cycle of disease. Most of the existingsanitary systems prevent exposure of humans to harmfulpathogens found in the excrement. These systems carry the waste,remove pathogens and pollutants, and finally release the contentsback into the nature, often in large volumes of diluted wastes,which leads to eutrophication. By shifting away from today’sparadigm which focuses on what must be removed fromwastewater, to a new paradigm focusing on what can be recoveredsanitation systems may begin to be described as ResourceRecovery Systems [1].

The key role of inorganic fertilizers (NPK) in the phenomenalgrowth of food grain production is well established [2]. However,majority of the nitrogen is made from natural gas which is subjectto price change and availability of methane, whilst, the globalpotassium and phosphorous mines are set to run out in less than acentury [3]. Besides, the regular usage of inorganic fertilizerscontributes to a progressive increase in soil acidity. These factors

* Corresponding author. Tel.: +91 97902 99447; fax: +91 4162243092.

E-mail addresses: [email protected], [email protected]

(M.G. Pillai).

2213-3437/$ – see front matter � 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.jece.2013.11.027

have made many farmers switch over to organic farming. However,the progress of organic farming has been very slow due to rapiddecline in organic raw materials such as animal wastes, cropresidues and green manure [4]. The application of fresh humanurine as a source of plant nutrient is rapidly growing in agriculturalpractices, and has already been successfully exploited in manycountries for cultivating a wide variety of crops [5–7]. The resultsvalidated that the crops grown using human urine, recorded ahigher yield with greater nutritional value and taste similar tocrops grown in normal soil. In addition, they do not pose anysignificant hygienic threat or leave any distinct flavor in foodproducts.

In particular, the use of human urine can help achieve a ‘‘closedloop fertility system’’ that can re-circulate nutrients from humanbeings back to agricultural fields. The initial phase of this systeminvolves separating urine from the waste stream, keeping theexcrement dry and speeding up the decomposition of pathogens,while the latter phase involves recovery of valuable nutrients fromthe source separated urine [8]. Though extensive research hasalready been carried out on urine diversion systems, studies on therecovery of nutrients (urea) are relatively few; particularlyseparation of urea from urine has not been adequately investigated[9]. Although conventional methods such as reverse osmosis,chemical precipitation, electro-chemical process and ion exchangecan be used for the removal of urea from urine, strict operating


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Nomenclature

A Tempkin isotherm equilibrium binding constant

(L g�1)

B Tempkin isotherm constant (J mol�1)

Bac biomass of plants in absolute control (g)

BDAP biomass of plants in di-ammonium phosphate (g)

Bt biomass of plants in treatment (g)

Ce equilibrium liquid-phase concentration of urea

(mg L�1)

C0 initial liquid-phase concentration of urea (mg L�1)

Es mean sorption energy (kJ mol�1)

kad Dubinin–Radushkevich isotherm constant

(mol2 kJ�1)

kid intra-particle diffusion rate constant

(mg g�1 min�1/2)

k1 first order rate constant (min�1)

k2 second order rate constant (g mg�1 min�1)

Ka equilibrium constant of adsorption

Kf Freundlich isotherm constant related to adsorption

capacity (mg g�1) (L g�1)n

KL Langmuir isotherm constant (L mg�1)

LoD limit of detection (mg L�1)

LoQ limit of quantitation (mg L�1)

n adsorption intensity

qe amount of urea adsorbed at equilibrium (mg g�1)

qm maximum adsorption capacity for the solid phase

loading (mg g�1)

qs theoretical maximum capacity (mg g�1)

q, qt amount of urea adsorbed at time t (mg g�1)

R universal gas constant (8.314 J mol�1 K�1)

RL separation factor

t adsorption time (min)

T absolute temperature (K)

V volume of the urine solution (L)

W mass of dry adsorbent used (g)

u degree of surface coverage

e Polanyi potential

DG8 Gibbs free energy of sorption (kJ mol�1)

Table 1Major constituents and their concentration in the urine samples.

S. No. Constituents in urine Concentration (mg L�1)

1 Urea 19,800

2 Creatinine 980

3 Chlorides 5945

4 Sodium 3195

5 Potassium 1480

6 Sulfates 810

7 Phophates 685

8 Ammonium 512

Total solutes 33,407

M.G. Pillai et al. / Journal of Environmental Chemical Engineering 2 (2014) 46–55 47

conditions, high cost, long periods and bad impact from shockloads make them undesirable to be used in practical industrialapplications [10]. Compared to these methods, adsorption hasdrawn more attention by researchers due to its feasibility, highsafety and low cost [11].

Biomaterials have the potential to be used as low cost eco-friendly adsorbents because of the unused resources that arewidely available [12]. Coconut (Cocos nucifera) generates a hugeamount of solid waste, mostly in the form of fiber and shell. It hasbeen reported that the processing of coconut shells into granularactivated carbon of sufficient density, hardness and porosity, canpotentially provide an inexpensive and renewable adsorbent[13,14]. Hence, the feasibility of employing activated carbonprepared from coconut shell toward the removal of urea fromhuman urine was analyzed. The study attempts to: (i) enhance thesurface area of coconut shell under microwave irradiation beforecarbonization; (ii) investigate the effect of contact time, initialconcentration of adsorbate and adsorbent dosage on ureaadsorption capacity; (iii) analyze the adsorption equilibrium andkinetics; (iv) characterize the Microwave Activated CarbonizedCoconut Shell (MACCS) powder; and (v) optimize the process

variables, using Response Surface Methodology (RSM) for theparticular batch adsorption system.

Materials and methods

Raw materials

The adsorbate used for the studies was urine obtained from tenhealthy young male volunteers (early twenties) with a well-balanced diet. Fresh urine samples collected in air-tight containerswere immediately put on ice and stored at �20 8C for two daysuntil the start of batch tests. The urine samples were thawed justbefore the investigation. Urine samples were characterized tocalculate its major constituents and results have been summarizedin Table 1. The coconut shells utilized for the preparation ofactivated carbon were obtained locally. The shells of the fruit werewashed with distilled water to remove dirt from its surface anddried at 105 8C for 24 h. Subsequently, the shells were crushed to asize of 1–2 cm. Proximate analysis of the raw coconut shells used inthis study by ASTM-1762 standards revealed the following;moisture content (5.64%), volatile matter (73.44%), fixed carboncontent (20.29%) and ash content (0.63%). All chemicals used in thestudy were purchased from Nice Chemicals Pvt. Ltd, Cochin, Indiaand were used without further purification.

Preparation and characterization of MACCS powder

The sieved coconut shells were exposed to microwaveirradiation at an output power of 180 W for 10 min (selected asthe heating period, based on preliminary runs) before carboniza-tion. The samples were placed in porcelain boats and heated in afurnace at a rate of 24 8C per min from room temperature to 500 8C,and maintained at this temperature for 1 h. The carbon thusobtained was cooled to room temperature, ground using a mortar,sieved to 100 mesh size (0.149 mm) and stored in tightly closedbottles for further analysis. The surface texture and the develop-ment of porosity for the precursor and the prepared activatedcarbon were analyzed using Scanning Electron Microscopy (FE-SEM, SUPRA 55, Carl Zeiss) with a 20 kV electron source. Thesurface organic structures of the samples were detected usingFourier Transform Infra-Red spectroscope (IRAffinity-1, Shimadzu)recorded at 4 cm�1 of resolution and 60 scans min�1 between 4000and 400 cm�1. The pore texture was characterized by N2

adsorption at 77 K using a BET surface area analyzer (Micro-meritics ASAP 2020). Prior to adsorption, the carbon was degassedat 300 8C in a vacuum condition for 2 h. The BET surface area wascalculated from the N2 adsorption isotherm using the Brunauer–Emmett–Teller (BET) equation [15]. The samples were character-ized for their total pore volume, pore size, and specific surface area[16]. Iodine Number (mg iodine/g carbon) was determined byusing a 0.1 N standardized Iodine solution and by extrapolating to0.02 N by an assumed isotherm slope using the standard method.

M.G. Pillai et al. / Journal of Environmental Chemical Engineering 2 (2014) 46–5548

Apparent Density was determined using ASTM D2854 standardmethod.

Batch adsorption studies

25 ml of urine solutions with different initial concentrations ofurea (25%: 4950 mg L�1, 50%: 9900 mg L�1, 75%: 14,850 mg L�1

and 100%: 19,800 mg L�1) were prepared in Erlenmeyer flasks(250 ml). 1–3 g of MACCS powder was added to 25 ml of urinesolutions, and kept in an incubator shaker, where shaking speed(125–200 rpm) and time (0–360 min) were varied continuously.The flasks were then removed from the shaker and sample waswithdrawn to be analyzed using a UV–visible spectrophotometer(Shimadzu UV-1601, Japan) at 430 nm [17]. The amount of ureaadsorbed at equilibrium, qe (mg g�1), was calculated using Eq. (1).

qe ¼ðC0 � CeÞV

W

� �(1)

where C0 and Ce (mg L�1) are the initial and equilibrium liquid-phase concentrations of urea, V is the volume of the solution (L)and W is the mass of dry adsorbent used (g). Measurement ofabsorbance for different urea solutions was performed with thenumber of scans, fixed at 10, to yield a Photometric accuracy of�0.002 Abs; Photometric repeatability of �0.001 Abs with a noiselevel of 0.002 Abs. The LoD (Limit of Detection) and LoQ (Limit ofQuantitation) were calculated based on standard deviation of theresponses (s) and the slope (S) of three independent analytical curvesas defined by ICH. LoD and LoQ were calculated as 3.3(s/S) and 10(s/S) and were found to be 0.1002 and 0.303 mg L�1, respectively.

Design of experiments

Central Composite Rotary Design

The Response Surface Methodology (RSM) is a collection ofmathematical and statistical techniques used for modeling andanalysis of problems, in which a response of interest is influencedby several variables and its objective is to optimize this response[18]. Central Composite Rotary Design (CCRD), a standard RSMdesign was applied for seeking the optimum conditions for theadsorption of urea from human urine on MACCS powder. CCRD wasemployed due to its advantages of rotability and ability to analyzethe complex interaction between process parameters. It is wellsuited for fitting a quadratic surface, which usually works well forprocess optimization. It consists of ‘F’ factorial points, ‘2k’ axialpoints and ‘nc’ center points (six replicates), where k is the numberof independent variables. These points are used to estimate thequadratic terms. Repetition of center points provides an estimateof pure error. The axial points (�a, 0, 0), (0, �a, 0) and (0, 0, �a)define the rotary design, where variance of the predicted response isconstant at all points equidistant from the center of the design. A fivelevel four factor CCRD was performed using the software – DesignExpert 8.0.7.1 to find interactive effects of the four variables whereeach variable was set at 5 levels: �a, �1, 0, +1, +a, where a = 1.682(Table 2) [19]. Since k = 4, Factorial points F = 24 = 16 points, Axialpoints 2k = (2 � 4) = 8 and center points nc = 6, the total number ofexperiments, n = 16 + 8 + 6 = 30. The experimental sequence was

Table 2The variables and their levels for the Central Composite Rotary Design.

Independent variable Symbol Coded levels

�1.682 (�a)

Initial adsorbate concentration (ml) X1 1.987

Adsorbent loading (g) X2 1.159

Rotations in shaker (rpm) X3 132.9

Adsorption time (min) X4 139.1

randomized in order to minimize the effects of the uncontrolledfactors. The level of variables considered for CCRD and the set ofexperiments in terms of coded and actual values are given in Table 3.

Statistical method

A second-order model is useful in approximating a portion ofthe true response surface with a parabolic curvature [20]. Itincludes quadratic terms and all cross product terms along withthe usual first order terms and is expressed as (Eq. (2)):

Y ¼ bo þ b1x1 þ b2x2 þ b3x3 þ b4x4 þ b11x12 þ b22x2

2

þ b33x32 þ b44x4

2 þ b12x1x2 þ b13x1x3 þ b14x1x4

þ b23x2x3 þ b24x2x4 þ b34x3x4 (2)

where Y is the predicted response, b0 is the intercept coefficient, bi

is the linear terms, bii is the squared terms, bij is the interactionterms and Xi and Xj represent the coded independent variables.Analysis of variance (ANOVA) was performed based on theproposed model to find out the interaction between the variablesand the response. The statistical significance was checked by the F-test and quality of the fit for the regression model was expressed bythe coefficient of determination (R2). Model terms were selected orrejected based on the probability value with 95% confidence level.Finally, the three dimensional response surfaces were drawn inorder to visualize the individual and the interactive effects of theindependent variables on adsorption of urea.

Field studies

The application of MACCS powder for plant (Vigna mungo andVigna radiate) growth was performed at Vellore Institute ofTechnology, Vellore, India, which is located at 12855012.7900 N and7987059.900 E at an altitude of 216 m above from sea level. The meanannual maximum and minimum temperatures of the site are33.5 8C and 23.6 8C with relative humilities of 47%–65%, respec-tively and mean annual rainfall of 954 mm. The effect of MACCSaddition on plant growth was studied with four replications. Theurea adsorbed MACCS powder used for the pot trials was preparedusing the parameters obtained from optimization studies (RSM).For each set, a pot of 0.2 m diameter and 0.2 m height were filledwith 2 kg of 0.002 m sieved dry soil. 20 seeds were sowed in fivepots named as control (contained no MACCS), 0.1% (w/w) of ureaadsorbed MACCS, 0.2% (w/w) of urea adsorbed MACCS, 0.3% (w/w)of urea adsorbed MACCS, and di-ammonium phosphate (DAP) at60 kg P2O5/ha, respectively. All pots were watered regularly.

The relative agronomic efficiencies of the treatments were thenestimated to indicate their agronomic effectiveness relative toDAP. The percentage biomass increase for various treatments wascomputed using absolute control as the standard while DAP wasconsidered as the standard for determining relative agronomicefficiencies (Eqs. (3) and (4)).

Percentage Biomass Increase ¼ Bt � Bac

Bac

� �� 100 (3)

Relative Agronomic Efficiency ¼ Bt � Bac

BDAP � Bac

� �� 100 (4)

�1 0 +1 +1.682 (+a)

6.25 12.5 18.75 23.01

1.50 2.00 2.50 2.841

150 175 200 217.0

180 240 300 340.9

Table 3Independent variables and result for the adsorption of urea from human urine onto MACCS using Central Composite Rotary Design (CCRD).

Run Coded variables Real variables Response – Y (mg g�1)

x1 x2 x3 x4 X1 X2 X3 X4 Predicted Observed

1 0 0 0 0 12.5 2 175 240 176.1 177.04

2 1 �1 �1 �1 18.75 1.5 150 180 220.07 219.99

3 1 1 �1 1 18.75 2.5 150 300 172.92 174.42

4 0 0 0 0 12.5 2 175 240 174.15 177.04

5 �1 �1 1 �1 6.25 1.5 200 180 87.538 92.72

6 0 0 0 �1.682 12.5 2 175 139.08 154.25 152.4

7 �1 1 1 �1 6.25 2.5 200 180 72.963 75.884

8 1 1 1 �1 18.75 2.5 200 180 200.52 199.24

9 0 0 0 0 12.5 2 175 240 175.34 177.04

10 1 �1 �1 1 18.75 1.5 150 300 222.43 224.06

11 0 0 0 0 12.5 2 175 240 172.11 177.04

12 0 1.682 0 0 12.5 2.84 175 240 157.41 161.38

13 �1 �1 1 1 6.25 1.5 200 300 89.657 97.583

14 1.682 0 0 0 23.01 2 175 240 236.26 233.42

15 �1 1 1 1 6.25 2.5 200 300 72.495 79.259

16 0 0 �1.682 0 12.5 2 132.95 240 130.27 130.94

17 �1 1 �1 1 6.25 2.5 150 300 62.84 60.864

18 1 �1 1 1 18.75 1.5 200 300 237.5 246.38

19 0 �1.682 0 0 12.5 1.16 175 240 237.15 217.28

20 �1 1 �1 �1 6.25 2.5 150 180 60.22 58.026

21 1 1 1 1 18.75 2.5 200 300 202.31 202.36

22 0 0 1.682 0 12.5 2 217.05 240 181.31 164.73

23 1 �1 1 �1 18.75 1.5 200 180 235.25 241.77

24 0 0 0 1.682 12.5 2 175 340.92 172.72 158.66

25 �1.682 0 0 0 1.987 2 175 240 25.62 12.56

26 �1 �1 �1 �1 6.25 1.5 150 180 75.985 80.481

27 0 0 0 0 12.5 2 175 240 173.1 177.04

28 1 1 �1 �1 18.75 2.5 150 180 175.22 171.84

30 �1 �1 �1 1 6.25 1.5 150 300 76.847 84.807


where Bt refers to the biomass of plants in treatment (g), while Bac

and BDAP represents the biomass of plants in absolute control (g)and in di-ammonium phosphate (g) respectively.

Results and discussion

Characterization

The microstructures of the raw coconut shell and the MACCSsurface were analyzed through SEM. The development of grainstructure was poor with no visible pores in the precursor (Fig. 1(a)).The cellular structure of coconut shell was noticeable aftermicrowave activation and carbonization (Fig. 1(b)). This mightbe due to the movement of moisture and volatile matter, leavingvoids that may be later transformed in the final activated carbonproduct. Microwave heating led to heat generation in the bulkwhich is transported to the surface due to temperature gradient.Since pressure is directly related to temperature, the moisturetransfer from bulk to surface takes place, catalyzing poreformation. In addition, the thermal stress resulted in developmentof cracks, crevices and slits in the matrix of the ultimate carbonmaterial. All these effects are clearly visible in Fig. 1(b) whereincreased surface area resulted in sorption of urea molecules on theMACCS surface, unlike Fig. 1(a). The N2 adsorption isotherms wereanalyzed by BET method. The MACCS was found to have a well-developed porosity with BET specific area of 700 m2 g�1. Due to theloss of some macropores during microwave pretreatment, thespecific area was less than that of commercially available activatedcarbon. However, microwave pretreatment caused a sharp rise intemperature lead to the contraction of the carbon skeleton and thedevelopment of a rich pore structure in the carbon (Table 4). Inaddition a high iodine number indicated a high sorption capacityfor the MACCS.

The vibrational spectrum of a molecule is considered to be aunique physical property and is characteristic of the molecule [21].The obtained FTIR spectrum of raw coconut shells powder

(Fig. 2(a)) revealed the peaks at 3442.9, 1629.8, 1400.3 and1066.6 cm�1, which corresponds to the presence of dimeric –OHstretch, C55C stretch, tertiary alcohol bend and aromatic C–H placenew bend in functional groups. It is clear that the adsorbentdisplays a number of absorption peaks, reflecting the complexnature of the adsorbent. Fig. 2(b) shows the shifting or disappear-ance of some peaks (3132.40, 1593.20, 1396.46 and 1066.64 cm�1)with simultaneous addition of new peaks at lower wave numbers.These changes indicate the involvement of the disappearedfunctional groups present on MACCS powder in sorption process.The surface chemistry of MACCS after adsorption illustratedintensive peaks, at 1587.42, 1402.25, 1136, 1066.64 cm�1 showingremoval of –OH stretch while retaining the aromatic C–H placenew bend (Fig. 2(c)).

Batch adsorption studies

Effect of initial adsorbate concentration on adsorption equilibrium

Initial concentration provides an important driving force toovercome all the mass transfer resistance of urea between the urinesolution and the MACCS surface. The effect of initial adsorbateconcentration in the range of 25% (4950 mg g�1 urea) to 100%(19,800 mg g�1 urea) on adsorption (investigated under thespecified conditions; carbon loading: 2 g, shaking speed: 130 rpmand temperature: 30 8C) is shown in Fig. 3. It is depicted from thefigure that the amount of urea adsorbed increased from 60 to225 mg g�1, as the initial concentration increased from 25% to 100%.The adsorption was rapid in the early stages but attained anasymptotic range for larger adsorption time. Adsorption took placeuntil the surface functional sites were fully occupied, later; the ureamolecules diffused into the pores of the adsorbent for furtheradsorption [22]. Urea removal efficiency was greater than 90% for allthe four initial concentrations studied, suggesting that initialconcentration only affects urea uptake capacity (mg g�1) and notthe percentage adsorption, as higher the amount of urea in theadsorbate more is the capacity given a constant adsorbent loading.

[(Fig._1)TD$FIG]

Fig. 1. SEM images of coconut shells. (a) Raw (20 kV, 25.43 K X); (b) MACCS surface

after urea adsorption (20 kV, 5.64 K X).

[(Fig._2)TD$FIG]

Fig. 2. FT-IR images of coconut shells. (a) Raw, (b) after microwave activation and

carbonization and (c) MACCS after urea adsorption.[(Fig._3)TD$FIG]

0

75

150

225

300

0 100 200 300 400

Adsorption time (min)

Ads

orpt

ion

capa

city

( m

g / g

)25%

50%

75%

100%

Fig. 3. Effect of initial concentration on adsorption kinetics of urea on MACCS

powder (Carbon loading: 2 g, Shaking speed: 130 rpm and 30 8C).


Effect of adsorbent loading on adsorption equilibrium

Adsorbent dosage is an essential factor in the adsorption of ureamolecules from aqueous solutions owing to its effect on theamount of urea adsorbed per unit mass of the adsorbent [23]. Theexperiments were carried out by varying adsorbent dosage at fixedadsorbate concentration (100% urine), speed of shaker (150 rpm)and temperature (30 8C). The results of the dependence of ureaadsorption on the amount of MACCS powder are shown in Fig. 4. Itwas found that with an increase in adsorbent loading from 1 g to2 g, though the percentage adsorption of urea increased, there wasa significant decrease in urea uptake capacity from the maximumof 680–210 mg g�1. The higher percentage of adsorption in initialstages may be due to the availability of more surface area whiledecrease in uptake capacity may be attributed to significantincrease in active sites per gram. Thus, the driving force for masstransfer between the bulk liquid phase and the solid phasedecreases with the passage of time [24].

Table 4Textural characteristics and pore structures of the carbon samples.

Parameter Test method

Apparent density (g cm�3) ASTM D2854

Iodine number (mg g�1) ASTM D4607-86

Pore volume (cm3 g�1) BET

Surface area (m2 g�1) BET

Pore diameter (nm) 4�pore volume/surface area

Moisture content (%) ASTM 1762

Effect of shaker speed on adsorption equilibrium

Shaking speed can influence the distribution of the solute in thebulk solution and the formation of the external boundary film [25].It was observed from Fig. 5, that an increase in shaker speed from150 to 175 rpm leads to an increase in adsorption capacity from 75to 80 mg g�1. This gradual increase in the adsorption capacity maybe due to decrease in the boundary layer resistance leading to

CCS MACCS CAC

0.451 0.397 0.352

825 911 1000

0.1883 0.2018 –

625.4 700.3 1050

0.301 0.288 –

5.18 4.27 8

[(Fig._4)TD$FIG]

0

200

400

600

800

0 100 200 300 400


Ads

orpt

ion

capa

city

( m

g / g

)

1.0 g

1.5 g

2.0 g

Fig. 4. Effect of carbon loading on adsorption kinetics of urea on MACCS powder

(Initial concentration: 100%, Shaking speed: 125 rpm and 30 8C).


increase in mobility, thus forcing the urea molecules toward theMACCS surface [26]. However, it was also noticed that, theadsorption time has a direct effect on uptake capacity given aconstant agitation speed. At low shaking speed, the adsorbent getsaccumulated in the sample, hiding the active sites present in thelower layers and urea gets adsorbed only on the upper layeradsorbent active sites. This implies that the rate of adsorptionshould be sufficient to ensure that, all the surface binding sites arereadily available for uptake of urea. Moreover, an increase inadsorption time leads to strong interaction between the adsorbateand adsorbent, thus, lowering the concentration of vacant sites.Finally the system reaches a point where all the vacant sites areused up and no further adsorption takes place.

Batch equilibrium studies

Adsorption isotherms are mathematical models that describethe distribution of adsorbate in the adsorbent and solution. They
[(Fig._5)TD$FIG]
0

15

30

45

60

75

90

0 100 200 300 400

Ads

orpt

ion

capa

city

( m

g / g

)


150 rpm

175 rpm

200 rpm

Fig. 5. Effect of shaking speed on adsorption kinetics of urea on MACCS powder

(Carbon loading: 1.5 g, Initial concentration: 25% and 30 8C).

are based on a set of assumptions that are related to thehomogeneity or heterogeneity of the adsorbent, the type ofcoverage, and possibility of interaction between adsorbate species.In this study, the equilibrium sorption data obtained wereanalyzed in terms of Langmuir, Freundlich, Tempkin, Flory–Huggins, and Dubinin–Radushkevich isotherms [27–30]. Table 5enlists all isotherm model equations, parameters and theirrespective values.

The normalized deviation and normalized standard deviationbetween experimental and predicted values for each isothermmodel was calculated (Eqs. (5) and (6)).

Normalized Deviation ¼ 100

N

X qeðexpÞ � qeðpredÞqeðexpÞ

�� (5)

Normalized Std Deviation ¼ 100

�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPðqeðexpÞ � qeðpredÞ=qeðexpÞÞ2

N

s

(6)

The Langmuir model fits the data, only at low concentration. Amethod to assess the goodness of fit of the experimental data to theisotherm models is to determine the correlation coefficient R2.Upon fitting the sorption data it was observed that, four isothermshad R2 value greater than 0.90 while, it was 0.79 for the Freundlichmodel demonstrating its poor agreement with the experimentaldata. R2 values were greater than 0.97 for both the Dubinin–Radushkevich and Flory–Huggins model making it very difficult todecide the best model to represent the experimental data based onthe correlation coefficient. A better criterion to test the goodness offit is by evaluating the normalized deviation and normalizedstandard deviation [31]. For MACCS powder, the minimum value ofnormalized deviation and normalized standard deviation arefound for the Flory Huggins model (Table 5). It is noteworthy thatthese values are very high for the Freundlich model furtherconfirming its unsuitability and confirming the aforementionedresult based on the correlation coefficients.

The value of qm (maximum adsorption capacity) was found tobe 256.41 mg g�1 which is sufficiently close to the measured valueof 227.75 mg g�1. The Langmuir isotherm can be expressed interms of a dimensionless constant called separation factor (RL)(Eq. (7)) which is an indicator of the isotherm being favorable [32]if 0 < RL < 1. The separation factor was found to lie within thefavorable limit indicating that the chosen operating conditionsfavor the urea sorption process.

RL ¼1

1þ KLC0(7)

The Freundlich isotherm constant ‘n’ is also an indicator of thefavorability of adsorption. For beneficial adsorption n should liebetween 0 and 1. A value of 3.931 for the present study indicatedeasy separation of urea from the solution and favorable adsorption.The apparent Gibbs free energy of sorption [33] DG8 which is afundamental criterion of spontaneity was evaluated (Eq. (8)). Theequilibrium constant (Ka) obtained from the Flory–Huggins modelwas used to compute the apparent Gibbs free energy change.

DG� ¼ �RTlnðKaÞ (8)

The negative value of DG8 in Table 5 confirms the feasibility ofthe process and the spontaneous nature of the sorption. The meansorption energy Es (kJ mol�1) (Energy required to remove amolecule from its location in the sorption space to infinity) wascomputed by using the Dubinin–Radushkevich model constant(Eq. (9)) and found to be 7.071 kJ mol�1, which indicated that, the

Table 5Isotherm model parameters, correlation coefficients, and comparison of deviations for various isotherm models.

Isotherm Parameter Parameter values

Langmuir

qe ¼qmKLCe1þKLCe

Coefficient of determination (R2) 0.94

Maximum adsorption capacity for the solid phase loading (qm) 256.41 (mg g�1)

Energy constant related to heat of adsorption (KL) 0.017295 (L mg�1)

Separation factor (RL) 0.002912

Normalized deviation 11.535

Normalized standard deviation 12.465

Freundlich

logðqeÞ ¼ logK f þ 1n

� �log Ce


Adsorption intensity (n) 3.931

Isotherm constant related to adsorption capacity (Kf) 38.69 (mg g�1) (g�1)3.931



Tempkin

qe = Bln(A) + Bln(Ce)


Constant related to heat of sorption (B) 34.175 (J mol�1)

Equilibrium binding constant (A) 0.5989



Flory–Huggins

log uCe¼ logðKaÞ þmlogð1� uÞ


Equilibrium constant of adsorption (Ka) 1.7546�10�5

Gibbs free energy of sorption (DG8) �27.856 (kJ mol�1)



Dubinin–Radushkevich

ln(qe) = ln(qs)�kade2


Theoretical maximum capacity (qs) 222.294 (mg g�1)

Dubinin–Radushkevich model constant (kad) 0.01 (mol2 kJ�1)

Mean sorption energy (Es) 7.071 (kJ mol�1)




process was physisorption [34].

Es ¼1ffiffiffiffiffiffiffiffiffiffi2kad

p !

(9)

Further, a positive value of the mean sorption energy indicatedthat the sorption was endothermic with higher solution tempera-ture favoring the adsorption.

Adsorption kinetics

To examine the controlling mechanism of sorption process, thepseudo-first order rate model, pseudo-second order rate model,and intra-particle diffusion model were tested on the equilibriumdata. The pseudo-first order equation proposed by Langergren andSvenska [35] was used as in Eq. (10).

logðqe � qtÞ ¼ logðqeÞ � k1t (10)

where qe and qt are the amounts of urea adsorbed (mg g�1) atequilibrium and at time t (min), respectively and k1 is the rateconstant of adsorption (min�1). The plot of log (qe � qt) versus timeindicated that the sorption data fits the first order rate expressionfor different urea concentrations.

The second-order kinetic model (Eq. (11)) can be expressed as

t

q¼ 1

k2q2e

þ t

qe

(11)

where k2 (g mg�1 min) is the second order rate constant. The linearplots of t/q versus time indicated that the data does not fit themodel well. Though the values of qe calculated from equationsdiffer from the experimental values for both pseudo first andsecond order model, the magnitude of correlation coefficient forpseudo first order is comparatively higher which makes it anadequate model to define the given system. Moreover, values for k1

(�0.0575 min�1) are quite close to each other, for the entireconcentration range further confirming the suitability of thepseudo-first order kinetic model. ‘k1

0values lie between 0.0543 and

0.0603 min�1 and are independent of initial concentration. Intra-particle diffusion model based on the theory proposed by Weberand Morris was tested to identify the diffusion mechanism [36]. Itis an empirically found functional relationship, common to mostadsorption processes, where uptake varies almost proportionallywith t1/2 rather than the contact time ‘t’ (Eq. (12)).

q� t ¼ kidt1=2 þ C (12)

A plot of qt versus t1/2 was generated to calculate kid

(mg g�1 min1/2), the intra-particle diffusion rate constant and C,the intercept (Fig. 6). The linearity of the plot suggested that intra-particle diffusion was involved in the adsorption process but sincethe regression lines did not pass through the origin, it indicatedsome degree of boundary layer control and that intra-particlediffusion was not the only rate limiting step. Table 6 enlists thevalues of constants calculated for all the kinetic models tested.

Optimization studies

Effect of process variables on the urea adsorption

RSM was employed to determine the complex interactiveeffects of the operating variables on urea adsorption. To study theinteraction factors, experiments were performed varying physicalparameters, using experimental design. By applying multipleregression analysis in Table 3 data, the experimental results of thefull factorial central composite design were fitted to the quadraticpolynomial Eq. (2). The adjusted model obtained for ureaadsorption, as a function of the coded variables is shown in Eq. (13).

Y ¼ 177:04þ 65:65x1 � 16:62x2 þ 10:04x3 þ 1:86x4

� 19:11x21 þ 4:34x2

2 � 10:32x23 � 7:60x2

4 � 6:42x1x2

þ 2:39x1x3 � 0:065x1x4 þ 1:4x2x3 � 0:37x2x4

þ 0:13x3x4 (13)

Adequacy check of the proposed model is a significantcomponent of the analysis procedure. Good adequacy ensures

[(Fig._6)TD$FIG]

-25000

0

25000

50000

75000

100000

0 5 10 15 20

t 0.5 (min)0.5

q.t (

mg.

min

/ g

)

25 %

50 %

75 %

100 %

Fig. 6. Intra-particle diffusion plot for adsorption of urea onto MACCS powder at

30 8C.

[(Fig._7)TD$FIG]

Fig. 7. Perturbation graph showing the effect of each of the independent variables

on urea adsorption while keeping other variables at their respective midpoint

levels.


approximation to the real system [37]. The adequacy check wasperformed by the test of significance of individual and regressionmodel coefficients and test for lack-of-fit. The significant factorscan be ranked based on the F-value or P value. The larger themagnitude of F-value and correspondingly the smaller the‘‘Prob. > F’’ value, the more significant is the correspondingcoefficient. An F-value of 79.13 and corresponding P-value<0.001 indicates that the model is statistically significant.Moreover, P-values (<0.05) shown in Table 5 suggested that thesignificant model terms are initial adsorbate concentration (X1),adsorbent loading (X2), agitation speed (X3), the two levelinteraction of initial adsorbate concentration and adsorbentloading (X1X2) and the quadratic effect of initial adsorbateconcentration, adsorbent loading and agitation speed X1

2, X22

and X32 respectively. The equation in terms of significant factors is

Table 6Comparison of adsorption rate constant, qe, and correlation coefficients for pseudo-firs

Model Model constants Initial

25%

Pseudo-first order model qe (mg g�1) 83.023

k1 (min�1) 0.0543

R2 0.9328

Pseudo-second order model qe (mg g�1) 84.033

k2 (g mg�1 min�1) 3.77�R2 0.8207

Intra-particle diffusion kid�103 (mg g�1 min�1/2) 1.2279

R2 0.9317

Table 7Biomass per plant, % biomass increase and relative agronomic efficiency for direct applic

Ttm. no. Treatment Biomass per plant (g)

Proof of concept Residual

1 Absolute control 2.27 2.79

2 Di-ammonium phosphate at 60 kg P2O5/ha 4.45 5.09

3 0.1% (w/w) of urea adsorbed MACCS 3.18 3.84



given as in Eq. (14).

Y ¼ 177:04þ 65:65x1 � 16:62x2 þ 10:04x3 � 19:11x21

þ 4:34x22 � 10:32x2

3 � 6:42x1x2 (14)

The actual adsorption capacity is the measured value for aparticular run and the predicted value is evaluated from the model.The accuracy and precision of the model was verified by thecoefficient of determination (R2 = 0.9866). The predicted R2 of0.9329 was in reasonable agreement with the adjusted R2 of0.9742, which advocates that the proposed model has adequateapproximation to the actual value.

Interaction effects of adsorption variables

The steep curvature in the perturbation plot between initialconcentration and adsorbent loading shows the response ofadsorption capacity was very sensitive (Fig. 7). The relatively flat

t order, pseudo-second order and intra-particle diffusion models.

concentration of adsorbate

50% 75% 100%

162.36 248.31 274.09

0.0587 0.0603 0.0568

0.9268 0.9204 0.9582

158.73 227.27 270.27

10�4 2.485�10�4 1.987�10�4 1.950�10�4

0.8676 0.8933 0.9205

2.4526 3.6351 4.4915

0.9322 0.9319 0.9327

ation of urea adsorbed MACCS powder: study with Vigna mungo and Vigna radiate.

% Biomass increase Relative agronomic efficiency

effect Proof of concept Residual effect Proof of concept Residual effect

– – – –

96.03 82.43 – –

40.01 37.63 0.41 0.45

74.01 70.61 0.77 0.85

83.25 77.42 0.86 0.93

[(Fig._8)TD$FIG]

Fig. 8. 3D response surfaces: (a) Interactive effects of initial adsorbent loading and

initial adsorbate concentration on adsorption capacity at adsorption time 240 min,

agitation speed 175 rpm and adsorption temperature 30 8C. (b) Interactive effects of

adsorption time and agitation speed on the adsorption capacity at initial adsorbate

concentration 12.5 mg L�1 adsorbent loading 2.00 g and adsorption temperature

30 8C. (c) Interactive effects of initial adsorbent concentration and agitation speed

on adsorption capacity at adsorbent loading 2.00 g, adsorption time 240 min and

adsorption temperature 30 8C.


lines of adsorption time and shaking speed show insensitivityof the response to change with these two parameters. Theinteraction effects between all four adsorption variables weredetermined from the plotted three dimensional curves. The 3D

plots for urea adsorption represented different combinations ofany two test variables at a time, while other variables weremaintained at zero level. It can be observed that there is a strongand significant influence of adsorbate concentration on the ureauptake capacity. The trend is that, higher the adsorbateconcentration, higher is the urea uptake capacity. This indicatesthat the MACCS powder adsorbs more of the urea at higheradsorbate concentration. Fig. 8(a–c) shows the dependency ofurea uptake capacity on MACCS. Though the adsorbate andadsorbent concentration affected the uptake capacity, it wasfound that the latter was slightly affected by adsorptiontime and shaking speed. It was also observed from the initialstudies that the adsorption capacity of urea increases with timeup to 60 min, where the surface sites are almost occupied.Further, increase in time has a significant effect on ureaadsorption.

The study revealed that the optimal conditions required toachieve the highest urea adsorption from human urine ontoMACCS powder were initial adsorbate concentration of 16.69 ml,adsorbent loading of 1.5 g, shaking speed of 188.8 rpm andadsorption time of 211.99 min. As such, the prediction capabilityof the proposed model was verified by additional batch experi-ments conducted in the experimental scale of the CCRD. Thevalidation results clearly confirmed with 95% certainty that a fivelevel four factor Central Composite Rotary Design is an effectivetool for mathematical modeling of urea adsorption onto MACCSpowder.

Field studies

To compare the plant growth, fresh biomass (excludingthe roots) was measured in each pot. Table 7 clearly indicatesthat the addition of MACCS to the soil had a noteworthy effecton the plant growth seen with an increase in biomass due toincrease in addition of MACCS to the soil. Also, the use of MACCSresulted in agronomic efficiency values (0.86 and 0.93 for 0.3%(w/w) of MACCS) very close to 1, indicating it was almost aseffective as di-ammonium phosphate. With reference to alltreatments, the plants subjected to the treatment of absolutecontrol had the least values for all the growth parameters. In thetest for residual effects, increase in nitrogen content of soilresulted in an increase in the biomass of the plant in all thetreatments.

Conclusion

The present research revealed that the microwave activatedcarbon developed from coconut shell has the potential to adsorburea from human urine. The urea uptake capacity was found todepend on the initial adsorbate concentration and MACCS loadingsignificantly. Further, the SEM analysis confirmed that microwaveactivation process induced well-developed porous structure inthe coconut shells. Although the adsorption process follows thepseudo-first order model, the linear regression and high correla-tion coefficient suggested that intra-particle diffusion was alsoinvolved. Based on error analysis, Flory–Huggins isothermshowed the minimum deviation from experimental data. More-over, a negative value of DG8 indicated the feasibility andspontaneous nature of the process. The development of amathematical model for urea adsorption process optimizationusing statistical design of experiments appears to be appropriatefor prediction of interaction effects between various adsorptionvariables. The validation results clearly confirmed that the urearecovery from source separated urine using MACCS powder couldplay an important role in sustainable agricultural practices andfuture sanitary systems.


Acknowledgement

The authors acknowledge the financial support provided by VITUniversity, Vellore, India under the VIT-SMBS research grantscheme (2040/VP-A-31082012) for conducting the initial experi-ments.

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