Batch and continuous studies for adsorption of anionic dye onto waste tea residue: Kinetic, equilibrium, breakthrough and reusability studies

Journal Pre-proof Batch and continuous studies for adsorption of anionic dye onto waste tea residue: Kinetic, equilibrium, breakthrough and reusability studies Suyog N. Jain, Shahnoor R. Tamboli, Dipak S. Sutar, Sumeet R. Jadhav, Jayant V. Marathe, Ashraf A. Shaikh, Ajay A. Prajapati PII:

S0959-6526(19)34648-7

DOI:

https://doi.org/10.1016/j.jclepro.2019.119778

Reference:

JCLP 119778

To appear in:

Journal of Cleaner Production

Received Date: 7 May 2019 Revised Date:

13 December 2019

Accepted Date: 16 December 2019

Please cite this article as: Jain SN, Tamboli SR, Sutar DS, Jadhav SR, Marathe JV, Shaikh AA, Prajapati AA, Batch and continuous studies for adsorption of anionic dye onto waste tea residue: Kinetic, equilibrium, breakthrough and reusability studies, Journal of Cleaner Production (2020), doi: https:// doi.org/10.1016/j.jclepro.2019.119778. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Waste Tea Residue Before Adsorption

Acid Blue 25 Dye Solution

Waste Tea Residue After Adsorption

Aqueous Solution After Dye Removal

1

1

Batch and Continuous Studies for Adsorption of Anionic Dye onto Waste Tea Residue: Kinetic,

2

Equilibrium, Breakthrough and Reusability Studies

3 4 5 6

Suyog N. Jain*, Shahnoor R. Tamboli, Dipak S. Sutar, Sumeet R. Jadhav, Jayant V. Marathe, Ashraf A.

7

Shaikh, Ajay A. Prajapati

8 9 10 11 12 13 14

Department of Chemical Engineering,

15

K. K. Wagh Institute of Engineering Education & Research, Nashik-422003,

16

Maharashtra, India

17 18 19 20 21

*

22

Tel.: +91 253 2221265,

23

E-mail address: [email protected]; [email protected]

24 25 26 27 28

Corresponding author Fax: +91 253 2515258;

2

29

Abstract

30

In the present article, adsorption of anionic dye (Acid Blue 25) using waste tea residue (WTR) was

31

investigated in batch and continuous operation. Clear insight of functional groups, surface charge,

32

morphology, composition, surface area and particle size of WTR was obtained by the characterization

33

techniques of FTIR, zeta potential, SEM-EDX, BET, and DLS analysis. Influence of operating pH,

34

adsorbent loading, influent concentration, contact duration of adsorption and temperature on dye

35

remediation was investigated in batch studies. Evaluated kinetic data was in better agreement with pseudo

36

2nd order model whereas equilibrium data was in better agreement with Redlich Peterson model. Multiple

37

steps were found to control the mechanism of the studied adsorption. Maximum dye uptake was obtained

38

as 127.14 mg g-1 at optimized pH of 1, loading of 3.5 g L -1 and higher temperature as 318 K. Adsorption

39

process was found to be spontaneous, physical and favored with the rise in temperature. Reusability of

40

WTR in multiple cycles showed a slight drop in dye uptake from 27.95 ± 0.26 mg g-1 at 1st cycle to 26.24

41

± 0.21 mg g-1 at 3rd cycle. Continuous studied were also conducted in packed column and influence of

42

column operating parameters as packing height (3-6 cm), concentration (50-200 mg L-1) and the flow rate

43

of influent (5-9 mL min-1) on the efficacy of dye remediation were investigated. Thomas model was

44

reported to be in better agreement with the evaluated breakthrough data. Maximum uptake in continuous

45

studies was reported as 50.82 mg g-1. The obtained results of batch and continuous studies depicted that

46

WTR could be used effectively for remediation of targeted anionic dye from the aqueous phase.

47

Keywords: Adsorption; Waste Tea Residue; Dye Removal; Regeneration; Nonlinear Regression

48 49

1.

Introduction

50

The availability of sufficient quantity and good quality of water is a major challenge faced nowadays all

51

over the world and it is utmost important to protect this scarce good from the pollution that can be caused

52

by different pollutants. Dyes are one of the major pollutants found in industrial effluents and causing

53

significant water pollution (Stawi et al., 2017). Dyes are available in natural and synthetic forms. The

54

growing population and specific demands of customers have almost replaced natural dyes by a synthetic

55

one. Annually, 1 million tons of dyes are being manufactured worldwide to meet the demand of the

56

industries (Zereshki et al., 2018). Synthetic dyes are applied extensively in different industries like textile,

57

paper, leather, etc. The textile industry generates approximately 54% of the total dye effluent (Katheresan

58

et al., 2018). Dye effluents have drawn great attention nowadays due to their harm to the ecosystem as

59

water pollution in terms of visible nature, carcinogenicity, and accumulation in organisms (Hui et al.,

3

60

2018). The presence of dyes in water occludes penetration of sunlight to the aquatic plants, causes toxicity

61

in water due to the degraded products and increases COD of water bodies (Karimifard et al., 2018).

62

Synthetic origin and complex structure of dyes are making them non-biodegradable, stable and hence it is

63

difficult to treat dye-containing wastewater (Salima et al., 2013). Dye bearing wastewater treatment is of

64

primary concern as even at very low concentrations, dyes decrease water clarity and hence are undesirable

65

(Essandoh and Garcia, 2018).

66

Several physicochemical treatments as membrane separation (Li et al., 2018), catalytic ozonation (Ghuge

67

and Saroha, 2018), coagulation/flocculation (Yeap et al., 2014), electrochemical oxidation (Jager et al.,

68

2018), etc. are available for decontamination of dyes from wastewater. Most of these aforementioned

69

treatment technologies are costlier due to initial capital cost along with operational/running cost

70

associated with labor, maintenance, sludge disposal, etc. (Azha et al., 2018; Jain and Gogate, 2017a).

71

Dyes are not completely removed from colored wastewater by physicochemical methods as dyes offer

72

resistance to fading under different conditions (L. Dai et al., 2018). These limitations can be overcome by

73

the adsorption technique due to its notable advantages as significant flexible nature to a high polluting

74

load coupled with its effectiveness, versatility, availability of a wide range of adsorbents, the possibility

75

of reusability of spent adsorbent, low cost and energy consumption in comparison with other treatment

76

methods (Saleh et al., 2018, 2017). The adsorption method is found to be inexpensive, effective, could be

77

applied at room temperature and does not form any harmful byproducts (Jain and Gogate, 2019; Li et al.,

78

2017; Toumi et al., 2018). The commonly applied adsorbent is activated carbon due to its attractive

79

features as highly porous nature, significant surface area and strong uptake capacities however high cost

80

coupled with the difficulty in desorption of adsorbed pollutant and subsequent reusability issues of

81

activated carbon limit its applications on a commercial scale, which has prompted to search for alternative

82

adsorbents as a possible replacement to activated carbon (Y. Dai et al., 2018; Jain and Gogate, 2017).

83

Different substrates in raw forms as a waste of pine leaves (Deniz and Karaman, 2011), orange peels and

84

peanut hull (Nascimento et al., 2014), peels of Solanum tuberosum and Musa acuminate (Rehman et al.,

85

2019), coffee residue (Kyzas et al., 2012), spent tree leaves (Hameed, 2009), and also in activated forms

86

as ZnCl2 activated kiwi peels (Mahmoodi et al., 2018), KOH activated sunflower piths (Baysal et al.,

87

2018), H3PO4 activated cassava peel (Rajeswarisivaraj et al., 2001), formaldehyde modified Dalbergia

88

sissoo sawdust (Garg et al., 2004) have been utilized by researchers for the decontamination of dyes from

89

wastewater.

90

The present article is novel in terms of effective utilization of waste tea residue (WTR) as an inexpensive

91

adsorbent with significant uptake of targeted dye in batch and continuous studies. Higher efficiency of

92

separation and repetitive use of WTR in multiple cycles without destructing the structure and functional

4

93

groups during elution ensured applicability of WTR as a potential adsorbent towards targeted dye

94

(Albadarin et al., 2017; Alqadami et al., 2018; Daneshvar et al., 2017). The reason behind applying WTR

95

as an adsorbent is its availability on a large scale as waste and again at zero cost. Targeted dye (Acid Blue

96

25) in the present article is an anionic anthraquinone dye used widely for dyeing leather, polyamide, etc.

97

(Guiso et al., 2014). The reason behind selecting targeted dye is its wide applicability in different

98

industries and also the environmental concerns due to its presence in the industrial effluents.

99

2.

Experimental section

100

2.1

Materials and Instrumentation

101

Dye was obtained from Merck India Ltd. All other used chemicals were of analytical grade. WTR was

102

collected from nearby tea shops in Nasik city. WTR was dried in sunlight, crushed into a fine powder and

103

was then used without any physical/chemical treatment for batch and continuous experimentation.

104

Changes in functional groups of WTR during adsorption were studied using FTIR analyses (IRAffinity-

105

1S Shimadzu). Zeta potential measurements to determine the charge on WTR were performed using Zeta-

106

sizer (Malvern). The particle size distribution of WTR was determined using dynamic light scattering.

107

Morphology of WTR before and after adsorption was studied using SEM analysis (Carl Zeiss Model

108

EVO18 UK). Energy-dispersive X-ray (EDX) analysis was performed to determine the elemental

109

composition of fresh WTR and WTR after adsorption. Brunauer–Emmett–Teller (BET) technique

110

(Quantachrome Novae 2200 USA) was applied to determine the surface area of fresh WTR and WTR

111

after adsorption.

112

2.2

113

Batch studies were performed in a thermostatic shaker (Biotechnics India) using 100 mL Erlenmeyer

114

flasks containing 50 mL of working solutions. Influence of operating parameters as pH (1-10), WTR

115

loading (0.5-5 g L-1), concentration (100-300 mg L ), stirring time (0-360 min) and temperature (288-

116

318 K) on dye remediation was investigated in batch studies. During the adsorption experiments, the

117

flasks were agitated at 150 rpm in a thermostatic shaker. At preset time intervals, samples were removed

118

from the shaker, centrifuged and analyzed spectrophotometrically at 602 nm (maximum wavelength of

119

absorption) to determine the concentration of dye left in the solutions. After the adsorption experiments,

120

samples were centrifuged at 8000 rpm and concentrations of dye left in solutions were determined from

121

the measured values of absorbance in the supernatant using UV spectrophotometer (Shimadzu Model UV

122

mini 1240). Reusability of WTR was tested by first desorbing the dye from dye laden WTR using ethanol

123

and subsequently applying the regenerated WTR for adsorption.

Experimental Procedures

-1

5

124

Dye uptake, qt (mg g-1) was estimated using the following equation: =

125

(

)

(1)

126

Where Ci and Ct are influent and effluent concentrations (mg L-1), respectively and W is loading of WTR

127

(g L-1).

128

Desorption (%) was estimated using the following equation (Leon et al., 2018): (%) =

129

× 100

(2)

130

Where Cd and Ca are the concentration of dye desorbed and adsorbed respectively (mg L-1).

131

The performance of WTR was also tested in a column of 2 cm size by packing WTR in the column

132

between glass wool as supporting layers to prevent loss and clogging of the adsorbent during operation.

133

The schematic diagram of a packed column is depicted in Fig. S1. The dye solution was passed through

134

the packing at different flow rates. A peristaltic pump was used to adjust the flow rate of the solution

135

through the packing. Influence of packing height (3-6 cm), influent concentration (50-200 mg L-1) and

136

flow rate (5-9 mL min-1) on the efficacy of removal was studied and samples withdrawn from the top of

137

the packed bed were analyzed.

138

3.

Results and Discussions

139

3.1

Characterization

140

Fig. 1a and 1b illustrate the obtained FTIR results before and after adsorption respectively. The peak at

141

3311.78 cm-1 in Fig. 1a belonging to the strong stretching band of the O-H or N-H group shifted to

142

3296.35 cm-1 after adsorption indicating an involvement of hydrogen bonding in adsorption (L. Dai et al.,

143

2018; Sharma et al., 2019). The peak at 1041.56 cm-1 belonging to the C-O-C band (Abd-Elhamid et al.,

144

2019) in Fig. 1a shifted to 1029.99 cm-1 after adsorption as depicted in Fig. 1b. The appearance of sharp

145

and strong bands at 2920.23 and 2858.51 cm-1 belongs to C-H asymmetric stretching of alkyl groups

146

(Saleh, 2018) and peak located at 1635.64 cm-1 indicates C=O stretching of carbonyl group (Abubakar et

147

al., 2019; Wong et al., 2019). The peaks at 1529.55 cm-1 and 1442.75 cm-1 belonging to aromatic C=C

148

stretch (Değermenc et al., 2019) in Fig. 1a shifted to 1523.76 cm-1 and 1448.54 cm-1, respectively after

149

adsorption as depicted in Fig. 1b. A small peak at 1236.37 cm-1 belonging to CO stretching (Siddiqui et

150

al., 2018) in Fig. 1a shifted to 1228.66 cm-1 after adsorption as depicted in Fig. 1b. The obtained FTIR

151

results showing changes in frequencies in Fig. 1b and 1b confirmed adsorption of dye on WTR.

152

Zeta potential values were determined at the interval of 1 pH units in the pH range from 1 to 10 and

153

obtained results are depicted in Fig. 2. Zeta potential values were decreased from 20.37 mV at pH of 1 to

154

–23.86 mV at a pH of 10 with zero potential at pH of 5.64. This point of zero potential where the charge

6

155

on the surface is zero is called as an isoelectric point (IEP) and the corresponding pH is termed as pHiep.

156

Average size of WTR particles was estimated as 347.5 nm. SEM images of WTR before and after

157

adsorption are illustrated in Fig. 3a and 3b respectively. The image before adsorption revealed the porous

158

and irregular structure of WTR whereas comparatively fewer cavities are observed in image after

159

adsorption, which revealed penetration of the dye into the pores and hence changes in morphology after

160

adsorption. EDX analysis demonstrated the presence of different elements in fresh WTR as C (65.93%),

161

O (31.55%), Na (0.79%), K (0.85%) and Ca (0.88%) whereas WTR after adsorption was found to contain

162

elements as C (69.11%), O (26.53%), N (3.36%), S (0.62%) and Cl (0.38%). Increase in weight % of

163

carbon along with the presence of nitrogen and sulfur in dye loaded WTR affirmed effective adsorption of

164

dye on WTR. The surface area of fresh WTR obtained as 68.82 m g reduced to 23.47 m g for

165

WTR after adsorption, which further affirmed penetration of dye leading to blockage of pores

166

and hence reduction in surface area.

167

3.2

Batch studies

168

3.2.1

Influence of pH

169

The influence of pH on dye uptake and removal was tested in the pH range of 1 to 10 and other operating

170

conditions were kept constant. The obtained results as depicted in Fig. 4a showed that acidic medium

171

favored dye removal. Removal was reported to fall from 97.84 ± 0.92% at a pH of 1 to 15.61 ± 1.04% at a

172

pH of 10. Higher dye uptake, qt as 27.95 ± 0.26 mg g-1 was obtained at a pH of 1, which decreased to 4.46

173

± 0.29 mg g-1 at a pH of 10. In the alkaline medium, where operating pH is more than 5.64 (pHiep), OH-

174

ion concentrations is higher, leading to anionic charge on WTR and thus reducing anionic dye removal

175

whereas in the acidic medium, where operating pH is less than 5.64 (pHiep), there is corresponding

176

increase in hydrogen ions, leading to development of positive charge on WTR and thus enhancing

177

removal of anionic dye-based on electrostatic attraction (Chaari et al., 2019). A similar trend of maximum

178

dye uptake and removal was investigated earlier for anionic dye remediation using activated leaves

179

powder (Jain and Gogate, 2017b).

2

-1

2

-1

180 181

3.2.2

Influence of adsorbent loading (W)

182

Influence of WTR loading on removal efficacy and dye uptake was tested by varying the loading from 0.5

183

to 5 g L-1 and obtained trends are depicted in Fig. 4b. Increase in the loading from 0.5 to 3.5 g L-1 led to a

184

significant increase in removal from 25.58 ± 1.07 to 97.84 ± 0.92% respectively. The obtained trend of

185

maximum removal could be ascribed to significant increase in the adsorption sites at more quantity of

186

WTR available at higher loading. A very little rise in removal from 97.84 ± 0.92 to 98.46 ± 0.84% was

7

187

observed with further increase in loading from 3.5 to 5 g L-1 as aggregates may be formed due to excess

188

amount of suspended adsorbent in the solution leading to improper utilization of sites and lowering

189

amount of adsorbed dye (Zhou et al., 2019). Hence loading of 3.5 g L-1 was finalized for further batch

190

studies. qt value was reported to drop from 51.17 ± 2.13 mg g-1 at 0.5 g L-1 to 27.95 ± 0.26 mg g-1 at 3.5 g

191

L-1 of loading. Similar trend of less dye uptake and maximum removal was reported for Rhodamine B

192

removal using polynanotubes (Wang et al., 2015).

193 194

3.2.3

Influence of stirring time (t) and concentration (Ci)

195

Fig. 4c shows the effect of stirring time and concentration on removal efficacy for the concentrations in

196

the range of 100-300 mg L-1 using WTR. It can be noted from the plot that all the curves of time study are

197

initially steeper indicating faster removal as attributed to abundant availability of free sites and hence

198

more probability of adsorption. All the curves are found to be flatter in later periods, attributed to a

199

decrease in the availability of frees sites as maximum sites were already occupied by the adsorbed dye

200

molecules. Significant removal was observed with an increase in the stirring time up to 210 minute and

201

thereafter till 360 min, very little rise in removal was noted as attributed to the establishment of

202

equilibrium (Alqadami et al., 2017).

203

Dye uptake and removal is significantly affected by concentration. Dye uptake was observed to increase

204

from 30.65 mg g-1 at 100 mg L-1 to 80.10 mg g-1 of 300 mg L-1 of concentration. Naushid et al. (Naushad

205

et al., 2016) reported a similar trend of higher dye uptake at increased Ci values. High concentration

206

gradient at increased Ci values favored the transfer of dye from the bulk to WTR surface due to a decrease

207

in resistance of mass transfer and hence high uptake was obtained at higher Ci values (Sangon et al.,

208

2018). Removal values were obtained as 97.84, 89.23 and 81.45% for 100, 200 and 300 mg L-1 of

209

concentration values, respectively. The observed trend can be explained on the fact that it is not possible

210

to occupy all the molecules of the dye on WTR surface as the amount of adsorbent is same at lower as

211

well as higher Ci values leading to decrease in removal at higher Ci values (Jain and Gogate, 2018).

212 213

3.2.4

Influence of temperature and thermodynamic studies

214

Temperature analysis in the present work was performed in the range of 288-318 K and established

215

results are depicted in Fig. 5. Dye uptake was increased from 111.99 mg g-1 at 288 K to 127.14 mg g-1 at

216

318 K. The obtained trend could be ascribed to fall in the viscosity of the solution and thus favoring better

217

diffusion of dye molecules in the porous structure of the adsorbent at increased temperature (Agarwal et

218

al., 2017), causing significant increase in the dye uptake. A similar trend was also investigated for

219

malachite green adsorption on activated ginger waste (Ahmad and Kumar, 2010).

8

220

Quantification of parameters as free energy change, ∆G0 (kJ mol-1), change in heat of adsorption, ∆H0 (kJ

221

mol-1) and entropy change ∆S0 (kJ mol-1 K-1) has been carried out based on thermodynamic equations.

222

Evaluated thermodynamic parameters of the studied adsorption process are summarized in Table S1.

223

Negative ∆G0 ensured the spontaneity of the studied adsorption process (Ahamad et al., 2019). Adsorption

224

of dye molecules on the adsorbent surface leads to a change in heat of adsorption (∆H0) as a result of

225

various forces. A lower value of ∆H0 (8-25 kJ mol-1) suggests that interaction between dye molecules and

226

adsorbent is weak, which ensures the physisorption process (Zeng et al., 2014). The obtained ∆H0 value is

227

17.59 kJ mol-1, which ensured the physisorption process and thus scope for reusability. ∆H0 value is

228

positive, which confirmed that studied adsorption is endothermic. Positive ∆S0 value indicated enhanced

229

chaos reflecting affinity between the dye and WTR (Ahmad et al., 2014).

230

3.2.5

231

Clear insight in the adsorption kinetics was established based on the fitting of the experiential data to the

232

kinetic models. Pseudo 1st order and 2nd order models are employed for the fitting of experimental data.

233

Pseudo 1st order is described using the following equation (Lagergren, 1898):

234

=

Adsorption mechanism and kinetics

(1 −

)

(3) -1

-1

235

Where, qe is dye uptake at equilibrium (mg g ) and kf is the model rate constant (min ).

236

Pseudo 2nd order is described using the following equation (Ho and Mckay, 1999):

237

=

"# !

!

(4)

238

Where ks is model rate constant (g mg -1 min-1).

239

To support the best fitting of the model to the obtained data, root mean squared error (RMSE) analysis

240

was performed. RMSE values are found as below (Mahmoodi et al., 2018):

241

$%&' = ( ∑+,-.

242

Where ypred is a value calculated using model equation, yexp is an experimental value and n is total

243

experimental points.

244

Obtained experimental data were fitted into pseudo 1st order and 2nd order equations based on nonlinear

245

regression and obtained model parameter values are summarized in Table 1. As illustrated in the Table 1,

246

the regression coefficient (R2) values of 2nd order equation are very close to unity (mean value of 0.9967)

247

in comparison with the 1st order equation. Values of error function as depicted in Table 1, are also very

248

less for 2nd order equation in comparison with 1st order equation. All the obtained findings confirmed

"

)

/

− , 0- 1

2

(5)

9

249

better agreement of 2nd order equation with the kinetic data obtained. Alqadami et al. (Alqadami et al.,

250

2016) reported similar 2nd order fitting for Malachite Green remediation based on nanocomposite

251

adsorbent

252

Weber Morris (WM) model employed to predict steps governing studied adsorption is described as below

253

(Weber and J. Carrell Morris, 1963):

254

= 3/

4.6

+8

(6)

255

Where kd is the WM constant (mg g-1 min-1/2) and I is the intercept (mg g-1).

256

Fig. S2 depicts WM plot (qt versus t0.5) for studied adsorption and model parameters as evaluated from the

257

graph are summarized in Table 1. As depicted in plot, nonzero intercept lines for all Ci values from 100-

258

300 mg L-1 indicated multistep adsorption. Sorption mechanism of dye on WTR was governed by three

259

sequential stages. In the first stage, the fastest adsorption rate was noted, followed by the second stage

260

where the slope was decreased due to internal diffusion and finally in third stage saturation occurred as

261

seen by almost horizontal lines (Shittu et al., 2019). The obtained trends confirmed the sorption

262

mechanism of dye on WTR as a multistep process.

263

3.2.6

264

Langmuir, Redlich Peterson, and Temkin models are employed for fitting of equilibrium data.

265

Langmuir model (Langmuir, 1918) is expressed as follow:

266

=

Isotherm fitting

9 :; "#:;

(7)

267

Where qm is maximum dye uptake (mg g-1) and KL is model constant (L mg-1).

268

Redlich-Peterson, a three-parameter model (Redlich and Peterson, 1959) is expressed as follows:

269

=

:<

"# ><

(8)

?

270

Where KR (L mg-1), αR (L mg-1) β and β (dimensionless) are Redlich-Peterson isotherm constants.

271

Temkin model (Temkin and Pyzhev, 1940) is expressed as follows:

272

= @" ln (C" D )

273

-1

Where K1 (L mg ) and B1 (mg g ) are model constants.

274

Obtained equilibrium data were fitted into Langmuir, Redlich Peterson and Temkin equations based on

275

nonlinear regression and obtained model parameter values are summarized in Table 2. As illustrated in

276

the Table 2, R2 values of Redlich Peterson equation are very close to unity (mean value of 0.9763) in

(9) -1

10

277

comparison with Temkin and Langmuir equations, indicating that Redlich Peterson equation is in better

278

agreement with the equilibrium data obtained. Model constant (KR, KL and K1) values are reported to

279

increase with temperature rise, which confirmed favorable process at increased temperature.

280

3.2.7

281

Table 3 summarizes data of maximum dye uptake (qm) along with operational parameters as reported in

282

the literature for remediation of different anionic dyes based on different adsorbents in raw and modified

283

form. As depicted in table, it can be said that the maximum dye uptake (qm) value of WTR in the present

284

research article as 127.14 mg g-1 is comparatively good in comparison with different adsorbents as

285

reported in the literature for remediation of Acid Blue 25 along with other anionic dyes.

286

3.2.8

287

Reusability of WTR in subsequent cycles was tested by first desorbing the dye from dye laden WTR

288

using ethanol and subsequently applying the regenerated WTR for fresh adsorption of dye. Desorption of

289

dye was carried out in shaker wherein dye laden WTR was mixed with ethanol and stirred in a shaker.

290

After desorption, regenerated WTR was again applied for adsorption. The obtained reusability trends for

291

3 cycles are depicted in Fig. S3. Dye uptake was dropped slightly from 27.95 ± 0.26 mg g-1 at 1st cycle to

292

26.24 ± 0.21 mg g-1 at 3rd cycle and % desorption values were reported as 98.97 ± 0.82% at 1st cycle and

293

92.86 ± 0.83% at 3rd cycle. The obtained trends with slight changes in reported values of dye uptake and

294

desorption in multiple cycles ensured the applicability of WTR for repetitive use in dye remediation.

Comparative study of WTR with other adsorbents

Reusability test

295 296

3.3

Packed bed study

297

3.3.1

Influence of packing height (Z)

298

Influence of different packing heights of WTR as 3, 4.5 and 6 cm on breakthrough profiles was analyzed

299

and depicted in Fig. 6a. Established breakthrough values are summarized in Table 4. At the beginning of

300

the column operation, higher dye uptake was observed till the breakthrough time, tb (Ct = 0.1Ci). After the

301

breakthrough, the concentration of dye in the effluent was noted to increase. The obtained trend is

302

attributed to fresh WTR available initially leading to higher uptake. After the exhaustion time, te (Ct =

303

0.9Ci) (Jayalakshmi and Jeyanthi, 2019), curves have become almost flat indicating very little uptake of

304

dye after exhaustion. As depicted in Table 4, the volume of treated effluent (Ve) was noted to increase

305

significantly with an increase in the packing height. Dye uptake (qm) was also noted to increase

306

significantly from 29.77 to 46.79 mg g-1 with an increase in the packing height from 3 to 6 cm

307

respectively. Increase in uptake is attributed to lengthening of mass transfer zone in the bed as more

11

308

amount of WTR is available at higher packing height, leading to more sorption sites and hence longer

309

times for bed exhaustion (Azzaz et al., 2017).

310

3.3.2

311

Influence of concentration of influent as 50, 100 and 200 mg L-1 on breakthrough profiles was analyzed

312

and depicted in Fig. 6b. Established breakthrough values are summarized in Table 4. Values of te are

313

found to be less at higher Ci values as ascribed to shortening of mass transfer zone due to early exhaustion

314

occurring due to quantification of dye at higher values leading to rapid filling of sorption sites (Liu et al.,

315

2019) whereas qe values are noted to increase significantly from 26.04 mg g-1 at 50 mg L-1 to 50.82 mg g-1

316

at 200 mg L-1 of influent concentration, ascribed to the fact that qm is proportional to the concentration of

317

influent and thus higher qm values are obtained at higher values of influent concentration (Charola et al.,

318

2018).

319

3.3.3

320

Influence of flow rate of influent as 5, 7 and 9 mL min-1 on breakthrough profiles was analyzed and

321

depicted in Fig. 6c. Established breakthrough values are summarized in Table 4. Increase in Q caused

322

corresponding decrease te and qm, as ascribed to shortening of dwelling time of WTR in the packed bed,

323

which is prohibiting proper propagation of the dye into the pores of the WTR (Khadhri et al., 2019) hence

324

the exhaustion occurred early and curves as seen from Fig. 6c are moved to the left at higher Q values.

325

Dye uptake was reported to drop from 43.02 mg g-1 at 5 mL min-1 to 31.61 mg g-1 at 9 mL min-1. A similar

326

finding was investigated earlier for remediation of methylene blue using activated Fox nutshell (Kumar

327

and Jena, 2016).

328

3.3.4

329

Adam Bohart and Thomas models are employed to analyze breakthrough data based on the nonlinear

330

regression approach.

331

The equation describing Adams-Bohart model is as below (Bohart and Adams, 1920):

332

Influence of concentration of influent (Ci)

Influence of the flow rate of influent (Q)

Breakthrough curve modeling

= E F3G DH −

I JK L

MK

N

(10)

333

Where kA is model constant (L mg-1 min-1), N0 is volumetric sorption capacity (mg L-1) and u0 is solution

334

velocity through the packed column (cm min-1).

335

The equation describing Thomas model is as below (Thomas, 1944):

12 =

336

" O Q R "# 0-F P P S

P

(11)

N

337

Where kT is model constant (mL min-1 mg-1), M is WTR packed in column (g) and qT is maximum dye

338

uptake (mg g-1).

339

Obtained model values based on the nonlinear regression approach are depicted in Table 5. R2 values of

340

Thomas model (mean value of 0.9988) are found to approach unity in comparison with R2 values of

341

Adam Bohart model (mean value of 0.8356). The values of qT parameter estimated using Thomas model

342

for all operating column conditions are close to the experimental values (qm). Values of error function

343

calculated using Thomas equation are also very less in comparison with Adam Bohart equation. The

344

obtained results ensured good agreement of Thomas model with the breakthrough data.

345

4.

346

The present research article established the potential of waste tea residue as zero cost adsorbent for

347

decontamination of anionic dye from the aqueous phase in batch and continuous operation. The obtained

348

characterization results ensured the adsorption of anionic dye on waste tea residue. Maximum dye uptake

349

was obtained as 127.14 mg g-1 at optimized pH of 1, loading of 3.5 g L

350

Reusability study confirmed the use of waste tea residue in repetitive cycles as a slight drop in dye uptake

351

from 27.95 ± 0.26 mg g-1 at 1st cycle to 26.24 ± 0.21 mg g-1 at 3rd cycle and desorption values from 98.97

352

± 0.82% at 1st cycle to 92.86 ± 0.83% at 3rd cycle were reported in three cycles. The obtained trends of

353

dye uptake and desorption in multiple cycles ensured the applicability of WTR for repetitive use in dye

354

remediation. Continuous studies conducted in packed bed confirmed the applicability of waste tea residue

355

on a commercial scale. Evaluated breakthrough data were well fitted to Thomas model and maximum

356

uptake in continuous studies was reported as 50.82 mg g-1. In the present work, column study has given

357

satisfactory results and hence the established design of the column in the present work can be scaled up

358

along with the quantity of WTR as an adsorbent depending upon the volume of the targeted anionic dye

359

effluent to be captured. The reusability of spent WTR in multiple cycles for the capture of targeted

360

anionic dye till the saturation of WTR will be taken into account during the scale-up. As tea residue is

361

having the heating value, saturated WTR after drying can then be incinerated and heat generated during

362

incineration can be utilized for steam generation. The overall study confirmed that waste tea residue could

363

be used as a suitable adsorbent for decontamination of targeted anionic dye from the aqueous phase.

364

Acknowledgements

365

The authors are grateful to K.K.W.I.E.E. and R; Nashik for providing support to carry out the present

366

research work.

Conclusions

-1

and temperature as 318 K.

13

367

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Figure Captions Fig.1

FTIR spectra of WTR (a) before adsorption (b) after adsorption

Fig.2

Zeta potential values of WTR

Fig.3

SEM image of WTR (a) before adsorption (b) after adsorption

Fig.4

Influence of operating parameters on Acid Blue 25 remediation using WTR a) pH (Ci = 100 mg L-1, W = 3.5 g L-1, t = 210 min) b) Loading (Ci = 100 mg L-1, pH = 1, t = 210 min) and c) stirring time and concentration (pH = 1, W = 3.5 g L-1, t = 360 min)

Fig.5

Isotherms for Acid Blue 25 remediation using WTR (pH = 1, W = 3.5 g L-1, t = 210 min)

Fig.6

Influence of operating parameters on breakthrough profiles for Acid Blue 25 remediation using WTR a) packing height (pH = 1, Ci = 100 mg L-1, Q = 7 mL min-1) b) influent concentration (pH = 1, Z = 4.5 cm, Q = 7 mL min-1) c) flow rate (pH = 1, Z = 4.5 cm, Ci = 100 mg L-1)

594 595 596 597 598 599 600 601 602 603 604

22

605

Table Captions Table 1

Kinetic parameters for Acid Blue 25 remediation using WTR (pH = 1, W = 3.5 g L-1, t = 360 min)

Table 2

Isotherm parameters for Acid Blue 25 remediation using WTR (pH = 1, W = 3.5 g L-1, t = 210 min)

Table 3

Maximum dye uptake (qm) and operating values for removal different anionic dyes

Table 4

Breakthrough parameters for different packing heights (Z), flow rates (Q) and influent concentrations (Ci)

Table 5

Adam-Bohart and Thomas parameters at different operating parameters for Acid Blue 25 remediation dye using WTR

606 607 608 609 610 611 612 613 614 615 616 617 618

23

619 620

Fig. 1a.FTIR spectra of WTR before adsorption

621

Fig. 1b.FTIR spectra of WTR after adsorption

24

30

Zeta Potential (mV)

20 10 0 0 -10

2

4

6

8

pH

-20

622 623 624 625 626 627 628 629 630 631

-30 Fig. 2. Zeta potential values of WTR

10

25

632

633 634

Fig. 3. SEM image of WTR (a) before adsorption (b) after adsorption

26

635

a)

100

% Removal

80

60

40

20

0 0

2

4

6

8

10

pH 636 637

b)

100

% Removal

80

60

40

20 0

2 Loading (g L-1)

638

4

6

27

639

c)

100

% Removal

80

60

40 Cᵢ = 100 mg L¯¹ Cᵢ = 200 mg L¯¹ Cᵢ = 300 mg L¯¹

20

0 0

60

120

180

240

300

360

Time (min) 640 641 642

Fig. 4. Influence of operating parameters on Acid Blue 25 remediation using WTR a) pH (Ci = 100 mg L-1, W = 3.5 g L-1, t = 210 min) b) Loading (Ci = 100 mg L-1, pH = 1, t = 210 min) and c) stirring time and concentration (pH = 1, W = 3.5 g L-1, t = 360 min)

643 644

28

120

qe (mg g-1)

90

T = 288 K

60

T = 303 K T = 318 K 30 0

648 649 650 651 652 653 654 655 656 657 658

450

L-1)

Fig. 5. Isotherms for Acid Blue 25 remediation using WTR (pH = 1, W = 3.5 g L-1, t = 210 min)

647

300 Ce (mg

645 646

150

29

659

a)

1 0.8

Ct/Ci

0.6 0.4 Z = 3 cm Z = 4.5 cm

0.2

Z = 6 cm 0 0

200

600

800

1000

Time (min)

660 661

400

b)

1

0.8

Ct/Ci

0.6

0.4 Cᵢ = 50 mg L¯¹ 0.2

Cᵢ = 100 mg L¯¹ Cᵢ = 200 mg L¯¹

0 0 662

200

400

600

Time (min)

800

1000

30

663

c)

1

0.8

Ct/Ci

0.6

0.4 Q = 5 mL min¯¹ 0.2

Q = 7 mL min¯¹ Q = 9 mL min¯¹

0 0 664

200

400

600

800

1000

Time (min)

665

Fig. 6. Influence of operating parameters on breakthrough profiles for Acid Blue 25 remediation 666 667 668 669 670 671 672 673 674 675

using WTR a) packing height (pH = 1, Ci = 100 mg L-1, Q = 7 mL min-1) b) influent concentration (pH = 1, Z = 4.5 cm, Q = 7 mL min-1) c) flow rate (pH = 1, Z = 4.5 cm, Ci = 100 mg L-1)

31

676

Table 1

677

Kinetic parameters for Acid Blue 25 remediation using WTR (pH = 1, W = 3.5 g L-1, t = 360 min) Model

Parameter st

Pseudo 1 order

Values

-1

Ci (mg L )

100

200

300

qcal (mg g-1)

27.67

50.92

70.19

kf (min-1)

0.0302

0.0239

0.0222

0.9890

0.9869

0.9896

0.8005

1.6494

1.8047

100

200

300

30.65

57.57

80.10

ks (g mg min )

0.0015

0.0006

0.0004

R2

0.9975

0.9972

0.9961

R

2

Error

Pseudo 2nd order

Ci (mg L-1) -1

qcal (mg g ) -1

-1

0.3777

0.7629

1.1092

Intra particle

-1

Ci (mg L )

100

200

300

First stage

Kd (mg g-1 min -1/2)

2.59

4.10

5.54

2.55

5.05

5.82

0.9931

0.9916

0.9846

0.71

1.02

1.08

18.11

36.03

53.97

0.9451

0.9323

0.9705

0.01

0.23

0.39

I (mg g )

27.92

47.73

64.02

R2

0.964

0.9682

0.9835

Error

-1

I (mg g ) R

Second stage

2

Kd (mg g-1 min -1/2) -1

I (mg g ) R

Third stage

2

Kd (mg g-1 min -1/2) -1

678 679 680 681 682 683

32

684

Table 2

685

Isotherm parameters for Acid Blue 25 remediation using WTR (pH = 1, W = 3.5 g L-1, t = 210 min) Isotherm

Parameter

Values

Temkin

T (K)

288

303

318

B1 (mg g-1)

16.74

17.46

17.94

K1 (L mg-1)

1.24

1.51

1.98

R2

0.9590

0.9599

0.9535

T (K)

288

303

318

qm (mg g-1)

111.99

118.91

127.14

KL (L mg-1)

0.029

0.032

0.034

R2

0.9197

0.9267

0.9309

T (K)

288

303

318

KR (L mg-1)

38.44

46.03

77.36

αR (L mg-1)β

1.44

1.56

2.51

β

0.7650

0.7685

0.7627

R2

0.9762

0.9770

0.9755

Langmuir

Redlich Peterson

686 687 688 689 690

33

691

Table 3

692

Maximum dye uptake (qm) and operating values for removal different anionic dyes Anionic dye

Adsorbent

pH

qm -1

Loading

Reference

-1

(mg g )

(g L )

Acid Blue 25

Waste tea residue

127.14

1

3.5

Present study

Acid Orange 7

Waste tea residue

5.73

2

4

(Khosla et al., 2013)

Acid Blue 25

Bagasse pith

17.50

-

5

(Chen et al., 2001)

Acid Blue 25

Water lettuce

24.50

2

-

(Kooh et al., 2018)

Acid Blue 25

Amine modified Populus

69.44

4

1

(Tka et al., 2018)

tremula

Acid Blue 113

Waste of potato peel

11.71

2

2.4

(Hoseinzadeh et al., 2014)

Acid Black 1

Waste of potato peel

1.79

3

2.4

(Hoseinzadeh et al., 2014)

Orange 2

Raw custard apple

2.15

4

8

(Sonawane and Shrivastava, 2011)

Congo red

Bengal gram seed husk

41.66

5.85

6

(Reddy et al., 2017)

Congo red

Cashew nut shells

5.18

3

20

(Kumar et al., 2010)

Reactive orange-

Strychnos

9.00

2

2

(Sankar et al., 2015)

M2R

potatorum Linn seeds

Acid Blue 129

Almond shell

11.95

2

16

(Fat’hi et al., 2014)

Reactive red 141

Sesame waste

27.55

1.1

4

(Sohrabi and Ameri, 2016)

Acid Orange 52

Paulownia tomentosa

10.50

2

0.5

Steud. leaf powder

(Deniz and Saygideger, 2010)

Reactive Blue 19

Corn silk

71.60

2

5

(Değermenc et al., 2019)

Reactive Red 218

Corn silk

63.30

2

5

(Değermenc et al., 2019)

Acid Yellow 220

Pine leaves

40.00

2

1

(Deniz and Karaman, 2011)

693

34

694

Table 4

695

Breakthrough parameters for different packing heights (Z), flow rates (Q) and influent concentrations (Ci)

696 697 698 699 700 701 702 703 704 705 706 707 708 709

Z

Q

Ci

tb

te

Ve

qm

(cm)

(mL min-1)

(mg L-1)

(min)

(min)

(L)

(mg g-1)

3

7

100

105

280

1.96

29.77

4.5

7

100

225

515

3.61

38.20

6

7

100

420

775

5.43

46.79

4.5

9

100

140

330

2.31

31.61

4.5

5

100

420

680

4.76

43.02

4.5

7

50

330

690

4.83

26.04

4.5

7

200

165

345

2.42

50.82

35

710

Table 5

711

Adam-Bohart and Thomas parameters at different operating parameters for Acid Blue 25 remediation dye

712

using WTR 3

4.5

6

4.5

4.5

4.5

4.5

7

7

7

9

5

7

7

100

100

100

100

100

50

200

5.71

4.55

4.04

5.42

2.71

5.38

2.74

26892

30398

33587

27838

32984

21314

41986

R2

0.8306

0.8449

0.8664

0.8182

0.8335

0.8310

0.8245

Error

0.1540

0.1562

0.1361

0.1614

0.1707

0.1718

0.1647

0.2512

0.1520

0.1226

0.2384

0.1377

0.2675

0.1263

qT (mg g-1)

30.70

38.69

47.46

31.95

42.74

26.02

51.18

qm (mg g-1)

29.77

38.20

46.79

31.61

43.02

26.04

50.82

R2

0.9988

0.9986

0.9986

0.9990

0.9985

0.9994

0.9990

Error

0.0130

0.0149

0.0139

0.0024

0.0023

0.0102

0.0122

Column

Z

operating

(cm)

parameters

Q

(mL min-1) Ci -1

(mg L ) Adams-

kA × 105 -1

-1

Bohart

(L mg min )

parameters

N0

(mg L-1)

Thomas parameters

713 714 715

kT -1

-1

(mL min mg )

Highlights •

Acid Blue 25 remediation using waste tea residue in batch and continuous study.

•

Maximum dye uptake obtained as 127.14 mg g-1 using waste tea residue.

•

Kinetic data fitted to pseudo second order model and equilibrium data fitted to Redlich Peterson model.

•

Breakthrough data of column study fitted to Thomas model.

•

Desorption and reusability confirmed effectiveness of waste tea residue in Acid Blue 25 remediation.

AUTHORSHIP STATEMENT

Manuscript Title: Batch and Continuous Studies for Adsorption of Anionic Dye onto Waste Tea Residue: Kinetic, Equilibrium, Breakthrough and Reusability Studies

All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript, etc.

Authorship’s contributions: Suyog N. Jain: Conceptualization, Methodology, Resources, Investigation, Writing-Original Draft, Writing-Review & Editing, Supervision and Proof reading Shahnoor R. Tamboli: Conceptualization, Methodology, Resources, Validation, Formal analysis, Investigation, Supervision and Proof reading Dipak S. Sutar: Conceptualization, Methodology, Resources, Validation, Formal analysis, Investigation, Supervision and Proof reading Sumeet R. Jadhav: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation and Proof reading Jayant

V.

Marathe:

Conceptualization,

Methodology,

Validation,

Formal

analysis,

Investigation, Data Curation and Proof reading Ashraf A. Shaikh: Conceptualization, Methodology, Validation, Formal analysis, Investigation and Proof reading Ajay A. Prajapati: Conceptualization, Methodology, Validation, Formal analysis, Investigation and Proof reading

Thanking you with regards.

Dr. Suyog N. Jain Assistant Professor Department of Chemical Engineering

K. K. Wagh Institute of Engineering Education & Research, Nashik-422003, Maharashtra, India Phone: 91-253-2221265 E-mail address: [email protected]; [email protected]

To, Editor Journal of Cleaner Production

Subject: Declaration of Interest statement

Dear Editor, I am submitting the revised manuscript entitled, “Batch and Continuous Studies for Adsorption of Anionic Dye onto Waste Tea Residue: Kinetic, Equilibrium, Breakthrough and Reusability Studies” for possible publication in Journal of Cleaner Production. On behalf of all authors, I declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Thanking you with regards.

Dr. Suyog N. Jain Assistant Professor Department of Chemical Engineering K. K. Wagh Institute of Engineering Education & Research, Nashik-422003, Maharashtra, India Phone: 91-253-2221265 E-mail address: [email protected]; [email protected]