- Email: [email protected]
Fixed-bed adsorption of Reactive Orange 84 dye onto activated carbon prepared from empty cotton flower agro-waste
Accepted Manuscript Fixed-bed adsorption of Reactive Orange 84 dye onto activated carbon prepared from empty cotton flower agro-waste Samir Charola, Rahul Yadav, Prasanta Das, Subarna Maiti PII:
S2468-2039(18)30062-1
DOI:
https://doi.org/10.1016/j.serj.2018.09.003
Reference:
SERJ 150
To appear in:
Sustainable Environment Research
Received Date: 5 February 2018 Revised Date:
23 May 2018
Accepted Date: 4 September 2018
Please cite this article as: Charola S, Yadav R, Das P, Maiti S, Fixed-bed adsorption of Reactive Orange 84 dye onto activated carbon prepared from empty cotton flower agro-waste, Sustainable Environment Research (2018), doi: https://doi.org/10.1016/j.serj.2018.09.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
ACCEPTED MANUSCRIPT
Received 20 February 2018 Received in revised form 13 June 2018 Accepted 4 September 2018
SC
empty cotton flower agro-waste
RI PT
Fixed-bed adsorption of Reactive Orange 84 dye onto activated carbon prepared from
a
TE D
M AN U
Samir Charola, Rahul Yadav, Prasanta Das, Subarna Maiti*
Process Design and Engineering Cell, Central Salt and Marine Chemicals Research Institute,
AC C
EP
Bhavnagar 364002, India
* Corresponding author E-mail Address: [email protected] 1
ACCEPTED MANUSCRIPT
ABSTRACT The adsorption potential of empty cotton flower agro-residue based activated carbon (CFAC) to adsorb Reactive Orange 84 (RO84) dye from aqueous solution was studied using
RI PT
packed- bed adsorption column. The breakthrough curve characteristics were highly influenced by process variables like influent flowrate, inlet RO84 dye concentration and CFAC bed height. The findings indicated that higher value of dye concentration and bed height were favorable but
SC
high influent flowrates was unfavorable for adsorptive removal of RO84 dye. The maximum adsorption capacity of column was found to be about 720 mg of RO84 per 4.67 g of CFAC
M AN U
adsorbent for initial concentration, flowrate and bed height of 200 mg L-1, 15 mL min-1 and 5 cm, respectively. Thomas, Yoon-Nelson and Bed Depth Service Time (BDST) models were applied to calculate kinetic parameters of the laboratory fixed-bed adsorption column. Based on error analyses, Thomas model and BDST model fitted well than Yoon-Nelson model. The study
TE D
concluded that CFAC is an effective adsorbent for adsorption of RO84 dye using fixed-bed adsorption column.
EP
Keywords: Empty cotton flower waste, Reactive Orange 84, Fixed-bed adsorption, Breakthrough
AC C
curve, Kinetic models
1. Introduction
Azo dyes are complex aromatic substances generally applied in dyeing of textile fiber,
inking of paper and other commercial processes like manufacturing of toys, foods and bulk drugs [1]. These dyes cover approximately 70% of total global market of dyestuffs [2]. The azo or reactive dyes contain minimum one azo group (-N≡N-) having aromatic rings, with advantages
2
ACCEPTED MANUSCRIPT
like bright color and excellent color fixation on fibers [3–5]. Aquatic living organisms are affected by the toxicity of these dyes in water [6]. Also, since the solubility of reactive dyes in water is high, conventional biological and physico-chemical processes are not effective for dye
RI PT
effluent treatment [7,8]. Batch studies using adsorbents are usually carried out to determine maximum adsorbate removal capacity from effluent and find efficiency of adsorption to remove particular adsorbates. In most cases, continuous fixed-bed adsorption columns are used in
SC
industries for dye containing waste water treatment [9]. A continuous fixed bed adsorption column operates in dynamic condition and the flow condition (hydrodynamics) in the entire
M AN U
column has high impact on downstream flow performance [10]. After a certain ratio of length to diameter, the flow performance and mass transfer characteristics of the column become unusual [11]. For successful design and operation of fixed bed adsorption column, predictions of the breakthrough curves (BTC) at particular operating conditions are necessary, and mass and heat
TE D
transfer processes in the column and adsorbent affect the nature of BTC [12]. The value of optimum bed height, bed exhaustion time and regeneration time are determined using BTC. Commercial processes for the adsorption of dyes from textile effluents mostly use activated
EP
carbon as an adsorbent in fixed-bed columns [13], as it is one of the most suitable options relative to other physico-chemical processes like coagulation, flocculation, ozonation and
AC C
precipitation. These processes have specific limitations like higher operating cost, generation of hazardous waste and requirements of more energy [14]. However, commercial activated carbons are prepared from non-renewable sources like coal, coke, bone etc. and therefore their use in pollution control application remains unjustified [15,16]. This has motivated activated carbon preparation from cheaper and non-conventional raw materials like agricultural and industrial wastes. There are two methods of activation: Physical and chemical activation. Physical
3
ACCEPTED MANUSCRIPT
activation comprises pyrolysis (below 600 °C) and then gasification using suitable oxidizing agents such as steam, CO2, air or mixture of them at elevated temperature (below 1100 °C). In chemical activation, chemicals like KOH, H3PO4, ZnCl2, etc. commonly used as an oxidizing
RI PT
agents are thoroughly mixed with the precursor and carbonized (up to 700 °C) followed by washing. KOH activation involves only one step heat treatment and completes at comparatively lower temperatures (500-700 °C). Also, better yield and good recovery of potassium hydroxide
SC
at the end of the process can be achieved [17].
Creation of porous structure [18–21] and degradation of biomass are important [22,23]
M AN U
for the study of chemical activation of agricultural based biomass using KOH, which impregnates into the inner surface of the biomass particle, restricts formation of tar, methanol and acetic acid and prevents the shrinkage of particle during heating. It also helps to modify the surface chemistry of the carbons by formation of hydroxide and ketone based complex via strong
TE D
oxidization [22].
The precursor of the activated carbon in this study is dried empty cotton flower agrowaste. Globally, Cotton fibers are widely used with total production of about 25 Mt per year
EP
which covers about 2.5% of the world's agricultural land. China and India are the first and second cotton producer countries in the world. [24,25]. The sowing of cotton crop starts in
AC C
March or April and harvesting in September. Usually, cotton harvesting is completed in nearly three months. In some countries, like India, hand harvesting is carried out to pick mature cotton flowers from the green plant. Generally, each cotton plant has 40-45 flowers and each cotton flower has nearly 1 g cotton. Weight of an empty cotton flower is roughly 3-4 g. Empty cotton flowers are left unused in fields after removal of cotton. They are usually burnt in the field for disposal. This biomass burning releases soot and smoke which affects the properties of soil [26]
4
ACCEPTED MANUSCRIPT
as well ambient air quality. Huge availability of this waste biomass gives opportunity to valorize this agro-waste for conversion to activated carbon. Some previous studies on preparation of activated carbon from cotton stalk residue have been reported [27–30].
RI PT
We report, preparation of activated carbon from the empty cotton flower (CFAC) through a single step process. One of the reasons behind the selection of this biomass is that, it’s non-use as animal fodder due to the presence of gossypol in the plant. Hence natural food chain is not
SC
affected. Furthermore, we report the study of CFAC as an adsorbent to adsorb Reactive Orange 84 (RO84) dye using fixed-bed adsorption column and study of various operating parameters
M AN U
affecting column performance. BTC for adsorption of RO84 dye using various kinetic models is also studied. There are many reported work on removal of different class of reactive dyes by activated carbons but literature on removal of the important RO84 dye using fixed bed
2. Materials and methods 2.1. Adsorbate
TE D
adsorption is scant.
EP
RO84 (Reactive Orange HE4R, C.I. 292810) dye used for experimental work was obtained from a local dye industry based at Ahmedabad, Gujarat in Western India. The
AC C
molecular formula of RO84 is C58H30Cl2N14Na8O26S8 (Mol. Wt. 1850.3 g mol-1) having maximum absorption wavelength (λmax) of 489 nm. The stock solution with concentration of 1000 mg L-1 was prepared by adding calculated amount of RO84dye in deionized water. The stock solution was diluted in appropriate proportion to prepare the required experimental dye solutions. Fig. 1 represents the chemical structure of RO84 dye. 2.2. Preparation of CFAC
5
ACCEPTED MANUSCRIPT
Empty cotton flowers were collected from a cotton farm in western India to prepare CFAC. Clay and dirt were removed by washing with distilled water and then cleansed biomass was dried in laboratory oven at temperature of 105 °C for 1 d. The dried biomass was ground and
RI PT
screened to particle size of 0.5 to 1 mm. KOH of 99.5%, purity from Renkem India was used to activate the precursor. 50 g of biomass was impregnated by calculated quantity of KOH and water with intermittent stirring. Impregnation ratio, i.e., weight of KOH to weight of precursor,
SC
was set at 2:1 by adjusting the quantity of KOH based on preliminary work. The impregnated precursor was placed in the oven at 105 °C overnight and the dried impregnated precursor was
M AN U
activated at 600 °C at a constant rate of heating (10 °C min-1) for 90 min using nitrogen flow (120 cm3 min-1). The sample was then cooled to ambient temperature, thoroughly washed until the neutral pH of the filtrate was obtained. The carbon formed was then dried in laboratory oven at 105 °C for 12 h, and kept in suitable containers. The schematic diagram of preparation of
TE D
CFAC from empty cotton flower is shown in Fig. 2.
2.3. Physico-chemical and textural characterization of CFAC All physico-chemical properties of CFAC were measured by standard methods. pH
EP
meter, Model Orion STAR A111 (Thermo Fisher Scientific, India) was used to determine the pH of CFAC by ASTMD 3838-80. Bulk density, defined as mass per unit volume of substance was
AC C
determined according to ASTM E-873-06. Methylene blue number of CFAC was calculated based on JIS K 1470-1991. BET surface area and pore size distribution of CFAC was determined by surface area analyzer, Model 3Flex (Micromeritics, USA) with the accuracy of 0.01 m2 g-1. An automatic proximate analyzer, Model APA 2 (Advance Research Instruments, India) was used to carry out proximate analysis. The minimum detection limit and the temperature accuracy of furnace was 0.2% and ± 5 °C, respectively. The ultimate analysis of CFAC was performed
6
ACCEPTED MANUSCRIPT
using vario MICRO cube (Elementar, Germany). The surface morphology of porous CFAC was characterized using Field Emission Scanning Electron Microscope, Model JSM-7100F (JEOL, USA). The resolution range was 1.2 to 3.0 nm and magnification range was between 10 to 1
RI PT
million times. The Fourier transform-infrared (FTIR) spectrum of CFAC was carried out using a Perkin-Elmer spectroscope (Spectrum GX, Germany) at a resolution of 4 cm-1. For FTIR analysis, the sample was mixed with KBr at a ratio of 1:100, pressed, palletized and dried in an
SC
oven at 110 °C for 6 h. The recording was done between 4000 and 400 cm-1. The burn-off of the CFAC was also determined. The burn-off is defined as the weight difference between the
M AN U
precursor and the activated carbon divided by the weight of precursor at dry basis. The important physico-chemical properties of CFAC are given in Table 1. 2.4. Experimental set up
A pyrex glass column was used to investigate dynamic behavior of RO84 dye adsorption
TE D
onto CFAC as shown in Fig. 3. The measurements of inside diameter and length of the column were 2.2 and 18 cm, respectively. The known quantity of adsorbent was fed to the column and it was supported using two layers of cotton wool. The column was filled with calculated quantity
EP
of CFAC to achieve a specific bed height. The flowrate of influent solution was maintained by variable speed peristaltic pump. The experimental work was carried out at room temperature and
AC C
at pH 5.9, which was that of the dye solution. Influent flow rate can be low, medium or high. At very low influent flowrate, the
exhaustion time is high while at higher influent flowrate, there are chances of flooding and leakage. In industrial practice, the operating bed height is usually in the range of 0.35 to 0.50 of the total column height to avoid pressure drop in the column and maintain steady state flowrate. The concentration of dye in the effluent of textile industries is usually in the range between 100-
7
ACCEPTED MANUSCRIPT
250 mg L-1. Hence, the effects of initial influent flowrate (10, 15 and 18 mL min-1), initial RO84 dye concentration (100, 150 and 200 mg L-1) and bed height (3, 5 and 7 cm) on BTC were studied. UV-Vis spectrophotometer (Orion Aquamate 8000, Thermo Scientific, India) was used
RI PT
to measure residual concentration of RO84 dye in outlet samples at regular time interval. The various kinetic models of column were used to analyze all the experimental BTCs and their parameters were evaluated.
SC
2.5. Analysis of column adsorption process
The plot of exit concentration vs. volume throughput or lapse time for a specific bed
M AN U
height of column is BTC. The evaluation of BTC trend and time required for breakpoint is necessary for proper designing of a fixed bed column [31,32]. Also these are important parameters to check feasibility of using the adsorbent in industrial applications [31]. The BTC concept requires analyzing the operating characteristics of a packed bed. The specific shape of
TE D
concentration-time profile of BTC together with time axis depends on inlet concentrations, flowrates and remaining parameters like bed height and diameter of column. Therefore, BTC analysis is important for the successful design of fixed-bed adsorption column [33].
EP
Generally, a plot of Ceffluent (Ct) or Ceffluent/Cinfluent (Co) vs. treated volume (V) or service time (t) for a specific bed height is used to describe BTC. The lower value of concentration, Cb is
AC C
selected at the breakthrough point which is a random value. In this study, when outlet concentration of dye reached upto 0.1 Co, Cb was obtained. The adsorbent is accounted to be exhausted when the value of outlet concentration of dye and inlet concentration of dye becomes almost same.
The following equations are used to compute parameters of fixed-bed column. Time proportionate to stoichiometric or total capacity is:
8
ACCEPTED MANUSCRIPT
∞
= 1 − = +
(1)
RI PT
where, tt (min) is the total time, Ci and Ct (mg L-1) are the initial and outlet concentrations of
respectively.
1 − = =
M AN U
Time equivalent of usable capacity of bed can be given by:
SC
RO84, respectively, A1 and A2 (cm2) are the used and unused bed area of BTC curve
(2)
Calculation of area, A2 and convenient capacity of bed in line for the breakthrough time,
TE D
tb gives unused bed height, or ≈
The value of total time (tt) and breakthrough time (tu) are obtained from Eqs. (1) and (2). tu/tt gives the fraction of the total length of the bed used up to the breakthrough point. Graphical
EP
and numerical integration methods are used to compute area under the curve. Theoretically, tt can be defined as the total time required where adsorbent in the column is exhausted which
AC C
means no further adsorption takes place. In this study, when outlet concentration of dye reached up to 90% of inlet concentration of dye, tt was obtained. As adsorption occurs, there is formation of mass transfer zone (MTZ) in the bed of the column and MTZ depth is controlled by various factors such as characteristics of adsorbent, nature of adsorbent, weight and particle size of adsorbent, solution pH, inlet concentration of adsorbate and solution flowrate. Out of all these factors, the lifespan of the column is mainly influenced by flow rate, adsorbate concentration and weight of adsorbent (bed-depth). 9
ACCEPTED MANUSCRIPT
The following equation is used to compute the MTZ or unused bed length (HUNB) (cm) [34].
RI PT
= 1 − = 1 −
SC
where, HT is the total bed length, cm.
(4)
M AN U
=
(3)
The utilized bed length (HB) (cm) for the breakthrough point is estimated as [34]:
=
(5)
TE D
(6)
AC C
!"## = $
EP
The total treated volume (Veff) (mL) is estimated as:
where, Q (mL min-1) is the volumetric flowrate and tt (min) is the total flow time. The total adsorbed RO84 dye quantity is computed as:
%&'( =
)*
=
) - ./ +',
(7)
10
ACCEPTED MANUSCRIPT
where, qtotal (mg) is the amount of RO84 adsorbed; A is the area under the BTC, Cad (Ci-Ct) (mg L-1) is the adsorbed concentration and t (min) is the time which can be tt or tsat (min) showing either total flow time or the saturation time.
RI PT
The following equation can be used to compute the total quantity of RO84 dye fed to the column (mtotal) (mg) [34].
)
SC
M AN U
0&'( =
(8)
Total removal (%) of RO84 dye is estimated as [34].
8 - ./
9 - ./
× 100
(9)
TE D
% 2304567 =
2.6. Modeling of fixed-bed column break-through An appropriate model satisfies the need of explaining the behavior of adsorption column
EP
and applying it to industrial level. Various mathematical models have been established which explains and probably anticipates the dynamic trend of the column bed. Thomas, Yoon-Nelson
AC C
and Bed Depth Service Time (BDST) models are widely used to study the kinetics of adsorption. This study was focused on finding the most suitable model which describes adsorption kinetics; determining behavior of BTC; and estimating adsorption capacity of the column for removal of RO84 dye and computes kinetic parameters. 2.6.1. Thomas model It is an extensively used model for prediction of BTCs and describing fixed-bed column performance. It assumes that there is no axial dispersion inside the column and the behavior of 11
ACCEPTED MANUSCRIPT
the flow is plug flow. It also obeys reversible second order and Langmuir kinetics of adsorption and considers that adsorption is controlled by the interface mass transfer as well as chemical
The following equation is the linear from of Thomas model.
=>? 8@ A
7< − 1 =
)
=>? - BCDD )
(10)
SC
−
RI PT
reaction [35].
M AN U
where, kTh (mL mg-1 min-1) is the Thomas model constant; qs (mg g-1) is the equilibrium uptake of RO84 per g of CFAC; w (g) is the quantity of CFAC in the column; Ci and Ct (mg L-1) are the concentration of RO84 at influent and effluent streams respectively; and Veff (mL) is the total effluent volume.
qs is determined. 2.6.2. Yoon-Nelson model
TE D
From the intercept and slope of linear plot ln[(Ci/Ct) - 1] vs. time (t), the values of kTh and
This model is less complicated as it does not depend on detailed data regarding the nature
EP
of adsorbent, physical parameters of adsorption bed and characteristics of adsorbate [36].
AC C
The linear from of Yoon-Nelson model can be written as,
7<
E
= FG ( − I)
(11)
where, kYN (min-1) is Yoon-Nelson model constant; t (min) is the sampling time and τ (min) is the required time for 50% adsorbate breakthrough.
12
ACCEPTED MANUSCRIPT
From the intercept and slope of linear plot ln[Ct/(Ci-Ct)] vs. time (t), the values of kYN and τ are determined. 2.6.3. BDST model
RI PT
This model shows linear relation between bed depth of column and service time with adsorption parameters and process concentration [37]. The model was originally derived from the Adam-Bohart equation but it was revised by Hutchins. The assumption of this model is that
SC
external mass transfer resistance and intra-particle diffusion type forces are negligible. So the surface chemisorption among the unused adsorbent and the solute in liquid phase can control the
M AN U
adsorption kinetics [38]. The model gives a comparison between adsorption capacities of the columns operated under various process conditions.
The following equation is the linear from of BDST model.
- K>
−
= -
7< − 1
(12)
TE D
=
where, U (cm min-1) is the linear velocity of influent; ko (L mg-1 min-1) is the BDST model
AC C
column.
EP
constant; No (mg L-1) is the maximum adsorption capacity and HT (cm) is the total bed height of
From the intercept and slope of the plot of service time (t) vs. HT, the values of ko and No
are determined.
2.7. Error analysis
Error analysis is a study about changes in the output of any model as the model parameters fluctuate around an average value and is necessary to assess the experimental data. In this study, error analysis combined with determined coefficient (R2) from regression analysis
13
ACCEPTED MANUSCRIPT
was used to determine the best fitted model for adsorption. Two error analyses techniques were used as follows:
2L = S
∑[(UV EUC )/UC ]Y RE
M AN U
(2) The average relative standard error (ARS)
(13)
SC
LLM = ∑RQ (OP − O" )Q
RI PT
(1) The sum of the squares of the errors (SSE)
(14)
where, yc and ye are the calculated and experimental data respectively and n is the number of
3. Results and discussion
TE D
experimental data points
3.1. Physical characterization of biomass and CFAC
EP
Basic important information on the adsorption phenomena and on the porosity of activated carbon is obtained from the shape of adsorption isotherm. The nitrogen adsorption-
AC C
desorption isotherms at 77 K of CFAC is shown in Fig. 4a. The isotherm is a mixture of type I and type IV as per IUPAC classification. According to the classification, type I isotherm is shown by microporous solids while type IV isotherm can be associated with mixture of micro and mesoporous solids. It was clearly indicated that even at very low relative pressure, the quantity of adsorbed nitrogen increased rapidly and as the relative pressure increased to 0.5, the isotherm showed apparent hysteresis loop. This behavior of adsorption indicated type IV, which is characteristic of mesoporous structure. 14
ACCEPTED MANUSCRIPT
One of the most important physical properties of adsorbents is pore size distribution as it gives information regarding surface heterogeneity. Pores of adsorbent are classified into three categories: Micropore (pore size < 2 nm), mesopore (2 to 50 nm) and macropore (> 50 nm).
RI PT
Barrett, Joyner & Halenda method was used to calculate pore size distribution of CFAC and is shown in Fig. 4b. It indicates that the CFAC contains a mixture of micropores and mesopores,
SC
and the micropore volume contributes about 30% of the total volume.
Fig. 5a indicates the result of absorption bands for FTIR spectrum of precursor biomass
M AN U
and CFAC. The spectrum of the empty cotton flower biomass exhibits presence of various functional groups. But after activation, there is reduction, elimination and broadening of the peaks in the spectra [27,30]. The major peak around 3399 cm-1 shows that the primary functional group on precursor is O-H stretching vibration of hydroxyl groups which includes hydrogen bonding. Other significant peaks at 2925, 2365, 1640 and 1248 cm-1 represent C-H stretching of
TE D
hydrocarbon bond, C≡C stretching of alkyne group, C=C stretching of weak alkene group and CO-C stretching of esters and carboxylic anhydrides. A noticeable change is observed in the spectra of CFAC compared to spectra of precursor in the bands of two regions between 1000 to
EP
1750 cm-1 and 2800 to 3500 cm-1. In these two regions, the intensities of bands reduce sharply
AC C
which suggests disappearance of many weak bonds during activation process. The surface morphology of precursor and CFAC are presented in Figs. 5b and 5c,
respectively. The surface structure of precursor biomass is rough with few pores. Noticeable pore structures with lot of cavities are noticed at the surface of CFAC. This is probably because of the vaporization of volatile impurities and tarry matters present in the biomass leading to a wellestablished porous structure. Also during activation, the enhancement of C-KOH reaction causes some carbon to be “burnt off” thereby creating well-structured pores on the surface of the CFAC. 15
ACCEPTED MANUSCRIPT
3.2. Effect of initial flowrate Influent flowrate is one of the key factors for determining the effectiveness of adsorbents in large scale continuous fixed-bed adsorption. The effect of influent flowrate for the adsorption of
RI PT
RO84 dye onto CFAC was studied by changing the flowrate (10, 15 and 18 mL min-1) while the initial RO84 dye concentration and the bed height were kept constant at 150 mg L-1 and 5 cm, respectively. Fig. 6a shows the plot between normalized dye concentration (Ct/Ci) and time (min)
SC
at varying flowrates. Rapid occurrence of the BTC was observed at higher flowrates. As the velocity increased, dye particle eluted faster. The time of breakthrough decreased from 240 to 64
M AN U
min when flowrate was altered from 10 to 18 mL min-1 (Table 2). The important BTC parameters are shown in Table 2. Speed of adsorption zone increased at higher flowrate, hence the time required to attain the specific breakthrough concentration was less compared to lower flowrate. The data from Table 2 indicated that the dye removal percentage and adsorption
TE D
capacity (qtotal) of column decreased with increasing flowrate. Many researchers have also reported similar observations for different other adsorption systems [38–40]. As the velocity increased, the mass transfer rate increased which intensified the rate of adsorption. Therefore, an
EP
early breakthrough was obtained at higher flowrates [41]. Also at higher flowrates, residence time of dye molecules in the column was less and contact time with adsorbents was also less, so
AC C
intra-particle diffusion phenomena between RO84 dye molecules and CFAC was not properly executed. As a result, adsorption capacity (qtotal) decreased and equilibrium was not attained. This is also supported by HB or MTZ shown in Table 2. Low flowrates provided sufficient contact time between RO84 dye and CFAC resulting in higher removal of RO84 dye as shown in Table 2. 3.3. Effect of initial concentration
16
ACCEPTED MANUSCRIPT
Fig. 6b represents the effect of initial RO84 dye concentration on the BTC at constant flowrate of 15 mL min-1 and bed height of 5 cm. The shape of BTCs is same for different RO84 dye concentrations, giving shrinkage in MTZ. A moderate BTC was obtained when the initial
RI PT
concentration of RO84 dye decreased. At lower concentration, the mass transfer coefficient was low because of slower transport phenomena. Calculated BTC parameters of adsorption column for removal of RO84 dye by CFAC at various RO84 dye solution concentrations are shown in
SC
Table 3. The driving force for mass transfer operation is the concentration gradient and therefore increasing the initial concentration of dye gave high equilibrium adsorption capacity and total
M AN U
percentage removal of dye as presented in Table 3. The treated volume increased with decreased RO84 dye concentration, and so the BTC shifted to the right. When the RO84 dye concentration was high, the shape of BTC was relatively steeper giving lesser breakthrough time because the diffusion process mostly depended on concentration. An extended BTC was obtained with
TE D
decreased initial RO84 dye concentration because of the lower concentration gradient resulting in slower transport [37]. Available vacant adsorbent sites were rapidly filled at high initial concentration of RO84; hence time required for breakthrough was reduced. As per Table 3, it
EP
was found that more adsorption of dye and more percentage removal of RO84 (35%) were obtained for higher RO84 dye concentration. Therefore, high initial concentration of dye and
AC C
high dye loading rates gave an improved column performance. Similar type of trends are found for removal of methylene blue using cedar sawdust and crushed brick, pine cone biomass and use of granular activated carbon for removal of Reactive Black 5 [30,38,39]. 3.4. Effect of bed height
The BTCs obtained from RO84 dye adsorption on CFAC at different bed heights of 3, 5 and 7 cm with initial RO84 dye concentration of 150 mg L-1 and flowrate of 15 mL min-1 are
17
ACCEPTED MANUSCRIPT
shown in Fig. 6c. It is observed that breakthrough time increased as the bed height increased. The removal percentage of dye increased from 22 to 34% (Table 4) when the bed height was raised from 3 to 7 cm. The shape of BTC observed for the bed height of 3 cm was steeper than
RI PT
that of 5 and 7 cm. This was because of the shorter mass transfer zone developed in column. As the bed height decreased, the load of adsorbent in the column was less, so there was less capacity for bed to adsorb dye from solution. Therefore, rate of adsorption increased faster. Once the
SC
adsorbent was close to saturate, there was rise in concentration of RO84 dye in effluent stream as time proceeded. As the quantity of loaded adsorbent in column was high, the contact time
M AN U
between adsorbate-adsorbent increased resulting in enhanced “sweep efficiency” [41]. With increasing bed height, the enhancement of binding sites for adsorption of dye was possible as the surface area of adsorbent increased. The used bed height also increased from 0.33 to 1.25 cm. Process of intra-particle diffusion and column adsorption capacity could be improved with the
TE D
increased bed height and hence, more quantity of effluent could possibly be handled with better RO84 removal efficiency with raising bed height which is better for column adsorption [38]. This observation is supported by work of other researchers [44,45].
EP
3.5. Dynamic modeling of fixed-bed column 3.5.1. Thomas model
AC C
The maximum solid-phase concentration (qs) and the rate constant (kTh) of the Thomas
model was determined by using the adsorption data. A linear regression technique as shown in Eq. (10) was used to obtain the determined coefficients and relative constants. Fig. 7 represents the linear plots between ln[(Ci/Ct) – 1] and t at specified experimental conditions. From the Table 5, the high value of linear regression coefficient (R2 > 0.9) with comparatively lower values of SSE (< 1.71) and ARS (< 0.25) gave conformation that this model fitted better with the
18
ACCEPTED MANUSCRIPT
experimental data. Furthermore, the calculated values of qs were close to the experimental values at different experimental conditions. Also as observed from Table 5, lower value of kTh and the higher value of qs was obtained as initial concentration of RO84 dye increased. This was due to
RI PT
the concentration gradient of the dye between two phases (solid and liquid) which acted as a driving force for adsorption. Hence, better performance of column required higher initial RO84 dye concentration which was the driving force. As the flow rate increased, the value of qs
SC
drastically fell, and kTh increased. As the bed depth increased, kTh decreased but qs improved. So higher initial RO84 dye concentration, higher bed depth and lower flowrate were favorable for
M AN U
adsorption of RO84 onto CFAC column [34,43,45–47]. 3.5.2. Yoon-Nelson model
Yoon-Nelson model is a less complicated theoretical model to study behavior of the breakthrough of RO84 dye on CFAC. A straight line was obtained against the plot between
TE D
ln[Ct/(Ci-Ct)] and t as shown in Fig. 8. From Table 6 it can be observed that, the value of linear regression coefficient is high (R2 > 0.9) and the values of SSE (2.94-7.91) and ARS (0.14-0.69) were more than Thomas model and the calculated values of τ (min) did not match the
EP
experimental values. It was observed that higher value of kYN was achieved with both higher flowrate and initial concentration of RO84 dye, but the value declined with higher bed height.
AC C
Also, the value of τ decreased with increasing flowrate and initial RO84 dye concentration but increased with increasing bed height. This might be because of the fact that increasing initial concentration of RO84 raised the competition among dye molecules for the location of adsorption, which finally gave higher uptake rate [42,44]. 3.5.3. BDST model
19
ACCEPTED MANUSCRIPT
The column experimental data of adsorption of RO84 dye on to CFAC closely fits the BDST model. A straight line was obtained against the plot between service time (t) vs. total bed height. The values of BDST parameters ko and No can be calculated from the slope and intercept
RI PT
of line. This model is applicable to explain the characteristics of fixed bed and to take pilot level experiments to commercial applications. This model provides an estimation of the column effectiveness which operates at constant conditions for attaining a required level of
SC
breakthrough. From Fig. 9, the calculated values of No, ko and R2 were 22273 mg L-1, 0.52 mL mg-1 min-1 and 0.99, respectively. From calculated No value, the maximum adsorption capacity
M AN U
of the CFAC can be computed as 49.49 mg g-1 considering present experimental conditions. The high value of R2 shows better suitability of this model for the present study. The high value of ko conveys that a short bed will escape breakthrough, but if the value of ko is low, a moderately longer bed will be needed to escape breakthrough [32-34]. The parameter of BDST model is
4. Conclusions
TE D
supportive to extend process for various flowrates.
This study affirms that the prepared activated carbon from locally, freely and amply
EP
available agricultural waste of empty cotton flower biomass can be an appropriate option of activated carbon obtained from non-renewable source for the adsorption of RO84 dye. As per the
AC C
column experimental results, the adsorption of dye is dependent on the flowrate, influent RO84 dye concentration and the bed height. When the flowrate increased from 10 to 18 mL min-1, the removal percentage was increased by 5%. On the other hand, when the influent concentration increased from 100 to 200 mg L-1, the removal percentage increased by about 8%, and as bed depth increased against 3 to 7 cm, the removal percentage enhanced by 12%. The maximum adsorption capacity of column was found to be about 720 mg of RO84 per 4.67 g of CFAC
20
ACCEPTED MANUSCRIPT
adsorbent for the values of initial concentration, flowrate and bed height of 200 mg L-1, 15 mL min-1 and 5 cm, respectively. The value of errors calculated through SSE and ARS were lower in Thomas model than Yoon-Nelson model so adsorption of RO84 on to CFAC was better
RI PT
described by Thomas model. The high value of correlation coefficients (R2 > 0.99) was obtained in BDST model which suggested suitability of this model for the prediction of breakthrough curve. The model constants of BDST model can be useful to operate the adsorption process at
SC
different concentrations and flowrates without more experimental run. The calculated value of No and ko from the BDST model were 22273 mg L-1 and 0.52 mL mg-1 min-1, respectively.
M AN U
Acknowledgements
We gratefully acknowledge CSIR-India. ADCIF is acknowledged for analysis of BET surface area, CHNS, SEM and FTIR. A part of this work has been carried out by Mr. R. Yadav, B. Tech student of Chemical Engineering Department of IIT-BHU, Varanasi during his summer
References*47
TE D
internship. This is CSIR-CSMCRI PRIS. NO. 182/2017.
[1] Osma JF, Saravia V, Toca-Herrera JL, Couto SR. Sunflower seed shells: a novel and
EP
effective low-cost adsorbent for the removal of the diazo dye Reactive Black 5 from aqueous solutions. J Hazard Mater 2007;147:900-5.
AC C
[2] Soares GMB, Amorim MTP, Hrdina R, Costa-Ferreira M. Studies on the biotransformation of novel disazo dyes by laccase. Process Biochem 2002;37:581-7. [3] O'Mahony T, Guibal E, Tobin JM. Reactive dye biosorption by Rhizopus arrhizus biomass. Enzyme Microb Tech 2002;31:456-63. [4] Yang XY, Al-Duri B. Application of branched pore diffusion model in the adsorption of reactive dyes on activated carbon. Chem Eng J 2001;83:15-23.
21
ACCEPTED MANUSCRIPT
[5] Kirk RE, Othmer DF, Grayson M, Eckroth D. Encyclopedia of Chemical Technology. 3rd ed. New York: Wiley-Interscience Publication; 1978. [6] Papic S, Koprivanac N, Bozic AL, Metes A. Removal of some reactive dyes from synthetic
2004;62:291-8.
RI PT
wastewater by combined Al(III) coagulation/carbon adsorption process. Dyes Pigments
activated carbon. Ind Eng Chem Res 1998;37:253-7.
SC
[7] Chern JM, Huang SN. Study of nonlinear wave propagation theory. 1. Dye adsorption by
J Hazard Mater 2003;98:211-24.
M AN U
[8] Ozacar M, Sengil IA. Adsorption of reactive dyes on calcined alunite from aqueous solutions.
[9] Chern JM, Chien YW. Adsorption of nitrophenol onto activated carbon: isotherms and breakthrough curves. Water Res 2002;36:647-55.
[10] Singh S, Srivastava VC, Mall ID. Fixed-bed study for adsorptive removal of furfural by
TE D
activated carbon. Colloid Surface A 2009;332:50-6.
[11] Vazquez G, Alonso R, Freire S, Gonzalez-Alvarez J, Antorrena G. Uptake of phenol from aqueous solutions by adsorption in a Pinus pinaster bark packed bed. J Hazard Mater
EP
2006;133:61-7.
[12] Walker GM, Weatherley LR. Adsorption of acid dyes on to granular activated carbon in
AC C
fixed beds. Water Res 1997;31:2093-101. [13] Chern JM, Chien YW. Competitive adsorption of benzoic acid and p-nitrophenol onto activated carbon: isotherm and breakthrough curves. Water Res 2003;37:2347-56. [14] Padmesh TVN, Vijayaraghavan K, Sekaran G, Velan M. Biosorption of Acid Blue 15 using fresh water macroalga Azolla filiculoides: batch and column studies. Dyes Pigments 2006;71:7782.
22
ACCEPTED MANUSCRIPT
[15] Martin MJ, Artola A, Balaguer MD, Rigola M. Activated carbons developed from surplus sewage sludge for the removal of dyes from dilute aqueous solutions. Chem Eng J 2003;94:2319.
experimental and modeling. Chemosphere 2005;58:1079-86.
RI PT
[16] Chakraborty S, De S, DasGupta S, Basu JK. Adsorption study for the removal of a basic dye:
[17] Girgis BS, El-Hendawy ANA. Porosity development in activated carbons obtained from
SC
date pits under chemical activation with phosphoric acid. Micropor Mesopor Mat 2002;52:10517.
M AN U
[18] Diao YL, Walawender WP, Fan LT. Activated carbons prepared from phosphoric acid activation of grain sorghum. Bioresource Technol 2002;81:45-52.
[19] Jagtoyen M, Derbyshire F. Activated carbons from yellow poplar and white oak by H3PO4 activation. Carbon 1998;36:1085-97.
TE D
[20] Suarez-Garcia F, Martinez-Alonso A, Tascon JMD. Pyrolysis of apple pulp: chemical activation with phosphoric acid. J Anal Appl Pyrol 2002;63:283-301. [21] Vernersson T, Bonelli PR, Cerrella EG, Cukierman AL. Arundo donax cane as a precursor
2002;83:95-104.
EP
for activated carbons preparation by phosphoric acid activation. Bioresource Technol
AC C
[22] Guo J, Lua AC. Textural and chemical properties of adsorbent prepared from palm shell by phosphoric acid activation. Mater Chem Phys 2003;80:114-9. [23] Solum MS, Pugmire RJ, Jagtoyen M, Derbyshire F. Evolution of carbon structure in chemically activated wood. Carbon 1995;33:1247-54. [24] Wikipedia contributors. Cotton. Wikipedia, The Free Encyclopedia; 2018 [25] Sheth K. Top cotton producing countries in the world. Quebec, Canada: World Atlas; 2018
23
ACCEPTED MANUSCRIPT
[26] Das P, Charola S, Dinda M, Patel H, Maiti S. Hydrogen from empty cotton boll agro-waste via thermochemical route and, feasibility study of operating an IC engine in continuous mode. Int J Hydrogen Energ 2017;42:14471-84.
RI PT
[27] Deng H, Li GX, Yang HB, Tang JP, Tang JY. Preparation of activated carbons from cotton stalk by microwave assisted KOH and K2CO3 activation. Chem Eng J 2010;163:373-81.
[28] Deng H, Yang L, Tao GH, Dai JL. Preparation and characterization of activated carbon
SC
from cotton stalk by microwave assisted chemical activation - application in methylene blue adsorption from aqueous solution. J Hazard Mater 2009;166:1514-21.
M AN U
[29] Deng H, Zhang GL, Xu XL, Tao GH, Dai JL. Optimization of preparation of activated carbon from cotton stalk by microwave assisted phosphoric acid-chemical activation. J Hazard Mater 2010;182:217-24.
[30] El-Hendawy ANA, Alexander AJ, Andrews RJ, Forrest G. Effects of activation schemes on
Appl Pyrol 2008;82:272-8.
TE D
porous, surface and thermal properties of activated carbons prepared from cotton stalks. J Anal
[31] Agrawal P, Bajpai AK. Dynamic column adsorption studies of toxic Cr(VI) ions onto iron
EP
oxide loaded gelatin nanoparticles. J Disper Sci Technol 2011;32:1353-62. [32] Nwabanne JT, Igbokwe PK. Adsorption performance of packed bed column for the removal
AC C
of lead (II) using oil palm fibre. Int J Appl Sci Technol 2012;2:106–15. [33] Aksu Z, Cagatay SS, Gonen F. Continuous fixed bed biosorption of reactive dyes by dried Rhizopus arrhizus: determination of column capacity. J Hazard Mater 2007;143:362-71. [34] Yagub MT, Sen TK, Afroze S, Ang HM. Fixed-bed dynamic column adsorption study of methylene blue (MB) onto pine cone. Desalin Water Treat 2015;55:1026-39.
24
ACCEPTED MANUSCRIPT
[35] Thomas HC. Heterogeneous ion exchange in a flowing system. J Am Chem Soc 1944;66:1664-6. [36] Yoon YH, Nelson JH. Application of gas adsorption kinetics .1. A theoretical model for
RI PT
respirator cartridge service life. Am Ind Hyg Assoc J 1984;45:509-16.
[37] El-Kamash AM. Evaluation of zeolite A for the sorptive removal of Cs+ and Sr2+ ions from aqueous solutions using batch and fixed bed column operations. J Hazard Mater 2008;151:432-
SC
45.
[38] Sadaf S, Bhatti HN. Evaluation of peanut husk as a novel, low cost biosorbent for the
Technol Envir 2014;16:527-44.
M AN U
removal of Indosol Orange RSN dye from aqueous solutions: batch and fixed bed studies. Clean
[39] Cruz-Olivares J, Perez-Alonso C, Barrera-Diaz A, Urena-Nunez F, Chaparro-Mercado MC, Bilyeu B. Modeling of lead (II) biosorption by residue of allspice in a fixed-bed column. Chem
TE D
Eng J 2013;228:21-7.
[40] Kahraman S, Yalcin P, Kahraman H. The evaluation of low-cost biosorbents for removal of an azo dye from aqueous solution. Water Environ J 2012;26:399-404.
EP
[41] Sen TK, Mahajan SP, Khilar KC. Colloid-associated contaminant transport in porous media: 1. Experimental studies. AIChE J 2002;48:2366-74.
AC C
[42] Hamdaoui O. Dynamic sorption of methylene blue by cedar sawdust and crushed brick in fixed bed columns. J Hazard Mater 2006;138:293-303. [43] Ahmad AA, Hameed BH. Fixed-bed adsorption of reactive azo dye onto granular activated carbon prepared from waste. J Hazard Mater 2010;175:298-303.
25
ACCEPTED MANUSCRIPT
[44] Han RP, Wang Y, Zhao X, Wang YF, Xie FL, Cheng JM, et al. Adsorption of methylene blue by phoenix tree leaf powder in a fixed-bed column: experiments and prediction of breakthrough curves. Desalination 2009;245:284-97.
RI PT
[45] Tan IAW, Ahmad AL, Hameed BH. Adsorption of basic dye using activated carbon prepared from oil palm shell: batch and fixed bed studies. Desalination 2008;225:13-28.
[46] Han RP, Wang YF, Yu WH, Zou WH, Shi H, Liu HM. Biosorption of methylene blue from
SC
aqueous solution by rice husk in a fixed-bed column. J Hazard Mater 2007;141:713-8.
[47] Aksu Z, Gonen F. Biosorption of phenol by immobilized activated sludge in a continuous
AC C
EP
TE D
M AN U
packed bed: prediction of breakthrough curves. Process Biochem 2004;39:599-613.
26
ACCEPTED MANUSCRIPT
2
Some of selected physic-chemical properties of CFAC.
7 3 4 5 6 7 8
6.95 0.45 1058 417 0.69 0.21 26.5 360
Applied Method ASTMD 3838-80 ASTM E-873-06
M AN U
pH Bulk Density, g mL-1 Surface Area (BET), m2 g-1 t-plot micropore area, m2 g-1 Total pore volume, cm3 g-1 t-plot micropore volume, cm3 g-1 Average pore diameter, Å Methylene Blue Value, mg g-1 Proximate Analysis: Moisture (%) Volatile matter (%) Ash (%) Fixed Carbon (%) (By Difference) Ultimate Analysis: C (%) H (%) N (%) S (%) O (%) Burn off (%)
5.1 12.9 6.9 75.1
TE D
6
Value
82.78 5.88 0.26 0.03 12.05 83.8
EP
4 5
Properties
AC C
Sr. No. 1 2 3
RI PT
Table 1
SC
1
9 10 11
27
JIS K 1470-1991
ASTM D3173 ASTM D3175 ASTM D3174 ASTM E1131-08 ASTM Standard
ACCEPTED MANUSCRIPT
Table 2 Calculated parameters derived from BTCs of column for adsorption of RO84 on to CFAC at different influent flowrate. Q (mL min-1) 10 15 18
t
V
b
(min) 240 90 65
eff
(mL) 12400 12300 10350
t
total
(min) 1240 820 585
m
total
(mg) 1860 1845 1575
q
total
(mg) 563 531 492
HB (cm)
4.03 4.45 4.48
0.97 0.55 0.52
Table 3 Calculated parameters derived from BTCs of column for adsorption of RO84 on to CFAC at different initial concentration. Concentration (mg L-1) 100 150 200
tb
Veff
ttotal
mtotal
qtotal
% Removal
(min) 240 90 30
(mL) 16500 12300 10300
(min) 1100 820 690
(mg) 1650 1845 2070
(mg) 449 531 720
MTZ (cm)
HB (cm)
27 29 35
3.91 4.45 4.78
1.09 0.55 0.22
EP
TE D
23
Table 4 Calculated parameters derived from BTCs of column for adsorption of RO84 on to CFAC at different bed height.
AC C
24 25 26 27 28 29 30 31 32 33
30 29 25
M AN U
17 18 19 20 21 22
MTZ (cm)
SC
16
% Removal
RI PT
12 13 14 15
Bed Height (cm) 3 5 7
t
b
(min) 55 90 200
V
eff
(mL) 7500 12300 16800
t
m
total
(min) 500 820 1120
total
(mg) 1125 1845 3360
34 35 28
q
total
(mg) 247 531 1142
% Removal
MTZ (cm)
HB (cm)
22 29 34
2.67 4.45 5.75
0.33 0.55 1.25
ACCEPTED MANUSCRIPT
Table 5 Calculated constants of Thomas kinetic model at different experimental condition. Flowrate (mL min-1) 10 15 18
Initial Dye Conc. (mg L-1) 100 150 200
Bed Height (cm) 3 5 7
0.032
0.040
0.078
0.045
0.040
0.035
0.076
0.040
0.028
212 208 0.90 0.95 0.14
161 162 0.92 0.84 0.093
121 120 0.93 0.42 0.084
142 148 0.91 0.39 0.25
161 162 0.92 0.84 0.093
186 189 0.97 0.41 0.094
128 130 0.90 1.05 0.11
161 162 0.92 0.84 0.093
182 181 0.91 1.71 0.09
TE D
M AN U
39
Table 6 Calculated constants of Yoon-Nelson kinetic model at different experimental condition. Flowrate (mL min-1) 10 15 18
Initial Dye Conc. (mg L-1) Bed Height (cm) 100 150 200 3 5
7
kYN (min )
0.046
0.063
0.107
0.047
0.063
0.071
0.094
0.063
0.042
τ (min) (exp) τ (min) (cal) R2 SSE ARS
525 650 0.90 7.91 0.65
309 351 0.91 4.57 0.35
205 239 0.93 4.44 0.14
530 613 0.91 6.92 0.63
309 351 0.91 4.57 0.35
268 287 0.97 2.94 0.28
194 2465 0.91 5.32 0.58
309 351 0.91 4.57 0.35
336 453 0.91 7.86 0.69
Yoon-Nelson Parameters
AC C
-1
EP
40 41 42 43 44 45 46 47 48 49 50 51 52 53
RI PT
Thomas Parameters kTh (mL min-1 mg-1) qs (mg g-1) (exp) qs (mg g-1) (cal) R2 SSE ARS
SC
36 37 38
54 55 56
29
SC
RI PT
ACCEPTED MANUSCRIPT
58
Fig. 1. Chemical Structure of RO84.
59 60 61
65 66 67 68 69 70
EP
64
AC C
63
TE D
62
M AN U
57
71 72
30
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
73 74
Fig. 2. Schematic diagram of preparation of CFAC.
78 79 80 81 82 83
EP
77
AC C
76
TE D
75
84 85 86
31
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
87
EP
TE D
Fig. 3. Schematic Diagram of the adsorption column.
AC C
88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
32
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
109
Fig. 4. (a) Adsorption-desorption isotherm of N2 at 77 K on the CFAC; (b) Pore size
111
distributions of the CFAC by BJH method.
EP
113 114 115 116 117 118 119 120 121
AC C
112
TE D
110
33
126 127 128 129
EP
125
Fig. 5. (a) FTIR spectra of empty cotton flower and CFAC; (b) SEM image of empty cotton flower and (c) SEM image of CFAC.
AC C
122 123 124
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
130 131 132 34
137 138 139
Fig. 6. Effect of different parameters on shape of BTCs of RO84 adsorption on CFAC (a) Flowrate; (b) Inlet RO84 dye concentration; (c) bed height.
EP
134 135 136
AC C
133
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
35
AC C
140 141 142 143 144 145 146 147 148 149 150
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 7. Linear plots of Thomas kinetic model for the adsorption of RO84 dye on CFAC with (a) different flowrate; (b) different initial concentration; (c) different bed height.
36
151 152 153 154 155 156 157
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 8. Linear plots of Yoon-Nelson kinetic model for the adsorption of RO84 dye on CFAC with (a) different flowrate, (b) different initial concentration and(c) different bed height.
37
162 163 164 165
170 171 172
EP
169
AC C
168
TE D
166 167
SC
Fig. 9. Bed depth versus service time plot
M AN U
158 159 160 161
RI PT
ACCEPTED MANUSCRIPT
38
S2468-2039(18)30062-1
DOI:
https://doi.org/10.1016/j.serj.2018.09.003
Reference:
SERJ 150
To appear in:
Sustainable Environment Research
Received Date: 5 February 2018 Revised Date:
23 May 2018
Accepted Date: 4 September 2018
Please cite this article as: Charola S, Yadav R, Das P, Maiti S, Fixed-bed adsorption of Reactive Orange 84 dye onto activated carbon prepared from empty cotton flower agro-waste, Sustainable Environment Research (2018), doi: https://doi.org/10.1016/j.serj.2018.09.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
ACCEPTED MANUSCRIPT
Received 20 February 2018 Received in revised form 13 June 2018 Accepted 4 September 2018
SC
empty cotton flower agro-waste
RI PT
Fixed-bed adsorption of Reactive Orange 84 dye onto activated carbon prepared from
a
TE D
M AN U
Samir Charola, Rahul Yadav, Prasanta Das, Subarna Maiti*
Process Design and Engineering Cell, Central Salt and Marine Chemicals Research Institute,
AC C
EP
Bhavnagar 364002, India
* Corresponding author E-mail Address: [email protected] 1
ACCEPTED MANUSCRIPT
ABSTRACT The adsorption potential of empty cotton flower agro-residue based activated carbon (CFAC) to adsorb Reactive Orange 84 (RO84) dye from aqueous solution was studied using
RI PT
packed- bed adsorption column. The breakthrough curve characteristics were highly influenced by process variables like influent flowrate, inlet RO84 dye concentration and CFAC bed height. The findings indicated that higher value of dye concentration and bed height were favorable but
SC
high influent flowrates was unfavorable for adsorptive removal of RO84 dye. The maximum adsorption capacity of column was found to be about 720 mg of RO84 per 4.67 g of CFAC
M AN U
adsorbent for initial concentration, flowrate and bed height of 200 mg L-1, 15 mL min-1 and 5 cm, respectively. Thomas, Yoon-Nelson and Bed Depth Service Time (BDST) models were applied to calculate kinetic parameters of the laboratory fixed-bed adsorption column. Based on error analyses, Thomas model and BDST model fitted well than Yoon-Nelson model. The study
TE D
concluded that CFAC is an effective adsorbent for adsorption of RO84 dye using fixed-bed adsorption column.
EP
Keywords: Empty cotton flower waste, Reactive Orange 84, Fixed-bed adsorption, Breakthrough
AC C
curve, Kinetic models
1. Introduction
Azo dyes are complex aromatic substances generally applied in dyeing of textile fiber,
inking of paper and other commercial processes like manufacturing of toys, foods and bulk drugs [1]. These dyes cover approximately 70% of total global market of dyestuffs [2]. The azo or reactive dyes contain minimum one azo group (-N≡N-) having aromatic rings, with advantages
2
ACCEPTED MANUSCRIPT
like bright color and excellent color fixation on fibers [3–5]. Aquatic living organisms are affected by the toxicity of these dyes in water [6]. Also, since the solubility of reactive dyes in water is high, conventional biological and physico-chemical processes are not effective for dye
RI PT
effluent treatment [7,8]. Batch studies using adsorbents are usually carried out to determine maximum adsorbate removal capacity from effluent and find efficiency of adsorption to remove particular adsorbates. In most cases, continuous fixed-bed adsorption columns are used in
SC
industries for dye containing waste water treatment [9]. A continuous fixed bed adsorption column operates in dynamic condition and the flow condition (hydrodynamics) in the entire
M AN U
column has high impact on downstream flow performance [10]. After a certain ratio of length to diameter, the flow performance and mass transfer characteristics of the column become unusual [11]. For successful design and operation of fixed bed adsorption column, predictions of the breakthrough curves (BTC) at particular operating conditions are necessary, and mass and heat
TE D
transfer processes in the column and adsorbent affect the nature of BTC [12]. The value of optimum bed height, bed exhaustion time and regeneration time are determined using BTC. Commercial processes for the adsorption of dyes from textile effluents mostly use activated
EP
carbon as an adsorbent in fixed-bed columns [13], as it is one of the most suitable options relative to other physico-chemical processes like coagulation, flocculation, ozonation and
AC C
precipitation. These processes have specific limitations like higher operating cost, generation of hazardous waste and requirements of more energy [14]. However, commercial activated carbons are prepared from non-renewable sources like coal, coke, bone etc. and therefore their use in pollution control application remains unjustified [15,16]. This has motivated activated carbon preparation from cheaper and non-conventional raw materials like agricultural and industrial wastes. There are two methods of activation: Physical and chemical activation. Physical
3
ACCEPTED MANUSCRIPT
activation comprises pyrolysis (below 600 °C) and then gasification using suitable oxidizing agents such as steam, CO2, air or mixture of them at elevated temperature (below 1100 °C). In chemical activation, chemicals like KOH, H3PO4, ZnCl2, etc. commonly used as an oxidizing
RI PT
agents are thoroughly mixed with the precursor and carbonized (up to 700 °C) followed by washing. KOH activation involves only one step heat treatment and completes at comparatively lower temperatures (500-700 °C). Also, better yield and good recovery of potassium hydroxide
SC
at the end of the process can be achieved [17].
Creation of porous structure [18–21] and degradation of biomass are important [22,23]
M AN U
for the study of chemical activation of agricultural based biomass using KOH, which impregnates into the inner surface of the biomass particle, restricts formation of tar, methanol and acetic acid and prevents the shrinkage of particle during heating. It also helps to modify the surface chemistry of the carbons by formation of hydroxide and ketone based complex via strong
TE D
oxidization [22].
The precursor of the activated carbon in this study is dried empty cotton flower agrowaste. Globally, Cotton fibers are widely used with total production of about 25 Mt per year
EP
which covers about 2.5% of the world's agricultural land. China and India are the first and second cotton producer countries in the world. [24,25]. The sowing of cotton crop starts in
AC C
March or April and harvesting in September. Usually, cotton harvesting is completed in nearly three months. In some countries, like India, hand harvesting is carried out to pick mature cotton flowers from the green plant. Generally, each cotton plant has 40-45 flowers and each cotton flower has nearly 1 g cotton. Weight of an empty cotton flower is roughly 3-4 g. Empty cotton flowers are left unused in fields after removal of cotton. They are usually burnt in the field for disposal. This biomass burning releases soot and smoke which affects the properties of soil [26]
4
ACCEPTED MANUSCRIPT
as well ambient air quality. Huge availability of this waste biomass gives opportunity to valorize this agro-waste for conversion to activated carbon. Some previous studies on preparation of activated carbon from cotton stalk residue have been reported [27–30].
RI PT
We report, preparation of activated carbon from the empty cotton flower (CFAC) through a single step process. One of the reasons behind the selection of this biomass is that, it’s non-use as animal fodder due to the presence of gossypol in the plant. Hence natural food chain is not
SC
affected. Furthermore, we report the study of CFAC as an adsorbent to adsorb Reactive Orange 84 (RO84) dye using fixed-bed adsorption column and study of various operating parameters
M AN U
affecting column performance. BTC for adsorption of RO84 dye using various kinetic models is also studied. There are many reported work on removal of different class of reactive dyes by activated carbons but literature on removal of the important RO84 dye using fixed bed
2. Materials and methods 2.1. Adsorbate
TE D
adsorption is scant.
EP
RO84 (Reactive Orange HE4R, C.I. 292810) dye used for experimental work was obtained from a local dye industry based at Ahmedabad, Gujarat in Western India. The
AC C
molecular formula of RO84 is C58H30Cl2N14Na8O26S8 (Mol. Wt. 1850.3 g mol-1) having maximum absorption wavelength (λmax) of 489 nm. The stock solution with concentration of 1000 mg L-1 was prepared by adding calculated amount of RO84dye in deionized water. The stock solution was diluted in appropriate proportion to prepare the required experimental dye solutions. Fig. 1 represents the chemical structure of RO84 dye. 2.2. Preparation of CFAC
5
ACCEPTED MANUSCRIPT
Empty cotton flowers were collected from a cotton farm in western India to prepare CFAC. Clay and dirt were removed by washing with distilled water and then cleansed biomass was dried in laboratory oven at temperature of 105 °C for 1 d. The dried biomass was ground and
RI PT
screened to particle size of 0.5 to 1 mm. KOH of 99.5%, purity from Renkem India was used to activate the precursor. 50 g of biomass was impregnated by calculated quantity of KOH and water with intermittent stirring. Impregnation ratio, i.e., weight of KOH to weight of precursor,
SC
was set at 2:1 by adjusting the quantity of KOH based on preliminary work. The impregnated precursor was placed in the oven at 105 °C overnight and the dried impregnated precursor was
M AN U
activated at 600 °C at a constant rate of heating (10 °C min-1) for 90 min using nitrogen flow (120 cm3 min-1). The sample was then cooled to ambient temperature, thoroughly washed until the neutral pH of the filtrate was obtained. The carbon formed was then dried in laboratory oven at 105 °C for 12 h, and kept in suitable containers. The schematic diagram of preparation of
TE D
CFAC from empty cotton flower is shown in Fig. 2.
2.3. Physico-chemical and textural characterization of CFAC All physico-chemical properties of CFAC were measured by standard methods. pH
EP
meter, Model Orion STAR A111 (Thermo Fisher Scientific, India) was used to determine the pH of CFAC by ASTMD 3838-80. Bulk density, defined as mass per unit volume of substance was
AC C
determined according to ASTM E-873-06. Methylene blue number of CFAC was calculated based on JIS K 1470-1991. BET surface area and pore size distribution of CFAC was determined by surface area analyzer, Model 3Flex (Micromeritics, USA) with the accuracy of 0.01 m2 g-1. An automatic proximate analyzer, Model APA 2 (Advance Research Instruments, India) was used to carry out proximate analysis. The minimum detection limit and the temperature accuracy of furnace was 0.2% and ± 5 °C, respectively. The ultimate analysis of CFAC was performed
6
ACCEPTED MANUSCRIPT
using vario MICRO cube (Elementar, Germany). The surface morphology of porous CFAC was characterized using Field Emission Scanning Electron Microscope, Model JSM-7100F (JEOL, USA). The resolution range was 1.2 to 3.0 nm and magnification range was between 10 to 1
RI PT
million times. The Fourier transform-infrared (FTIR) spectrum of CFAC was carried out using a Perkin-Elmer spectroscope (Spectrum GX, Germany) at a resolution of 4 cm-1. For FTIR analysis, the sample was mixed with KBr at a ratio of 1:100, pressed, palletized and dried in an
SC
oven at 110 °C for 6 h. The recording was done between 4000 and 400 cm-1. The burn-off of the CFAC was also determined. The burn-off is defined as the weight difference between the
M AN U
precursor and the activated carbon divided by the weight of precursor at dry basis. The important physico-chemical properties of CFAC are given in Table 1. 2.4. Experimental set up
A pyrex glass column was used to investigate dynamic behavior of RO84 dye adsorption
TE D
onto CFAC as shown in Fig. 3. The measurements of inside diameter and length of the column were 2.2 and 18 cm, respectively. The known quantity of adsorbent was fed to the column and it was supported using two layers of cotton wool. The column was filled with calculated quantity
EP
of CFAC to achieve a specific bed height. The flowrate of influent solution was maintained by variable speed peristaltic pump. The experimental work was carried out at room temperature and
AC C
at pH 5.9, which was that of the dye solution. Influent flow rate can be low, medium or high. At very low influent flowrate, the
exhaustion time is high while at higher influent flowrate, there are chances of flooding and leakage. In industrial practice, the operating bed height is usually in the range of 0.35 to 0.50 of the total column height to avoid pressure drop in the column and maintain steady state flowrate. The concentration of dye in the effluent of textile industries is usually in the range between 100-
7
ACCEPTED MANUSCRIPT
250 mg L-1. Hence, the effects of initial influent flowrate (10, 15 and 18 mL min-1), initial RO84 dye concentration (100, 150 and 200 mg L-1) and bed height (3, 5 and 7 cm) on BTC were studied. UV-Vis spectrophotometer (Orion Aquamate 8000, Thermo Scientific, India) was used
RI PT
to measure residual concentration of RO84 dye in outlet samples at regular time interval. The various kinetic models of column were used to analyze all the experimental BTCs and their parameters were evaluated.
SC
2.5. Analysis of column adsorption process
The plot of exit concentration vs. volume throughput or lapse time for a specific bed
M AN U
height of column is BTC. The evaluation of BTC trend and time required for breakpoint is necessary for proper designing of a fixed bed column [31,32]. Also these are important parameters to check feasibility of using the adsorbent in industrial applications [31]. The BTC concept requires analyzing the operating characteristics of a packed bed. The specific shape of
TE D
concentration-time profile of BTC together with time axis depends on inlet concentrations, flowrates and remaining parameters like bed height and diameter of column. Therefore, BTC analysis is important for the successful design of fixed-bed adsorption column [33].
EP
Generally, a plot of Ceffluent (Ct) or Ceffluent/Cinfluent (Co) vs. treated volume (V) or service time (t) for a specific bed height is used to describe BTC. The lower value of concentration, Cb is
AC C
selected at the breakthrough point which is a random value. In this study, when outlet concentration of dye reached upto 0.1 Co, Cb was obtained. The adsorbent is accounted to be exhausted when the value of outlet concentration of dye and inlet concentration of dye becomes almost same.
The following equations are used to compute parameters of fixed-bed column. Time proportionate to stoichiometric or total capacity is:
8
ACCEPTED MANUSCRIPT
∞
= 1 − = +
(1)
RI PT
where, tt (min) is the total time, Ci and Ct (mg L-1) are the initial and outlet concentrations of
respectively.
1 − = =
M AN U
Time equivalent of usable capacity of bed can be given by:
SC
RO84, respectively, A1 and A2 (cm2) are the used and unused bed area of BTC curve
(2)
Calculation of area, A2 and convenient capacity of bed in line for the breakthrough time,
TE D
tb gives unused bed height, or ≈
The value of total time (tt) and breakthrough time (tu) are obtained from Eqs. (1) and (2). tu/tt gives the fraction of the total length of the bed used up to the breakthrough point. Graphical
EP
and numerical integration methods are used to compute area under the curve. Theoretically, tt can be defined as the total time required where adsorbent in the column is exhausted which
AC C
means no further adsorption takes place. In this study, when outlet concentration of dye reached up to 90% of inlet concentration of dye, tt was obtained. As adsorption occurs, there is formation of mass transfer zone (MTZ) in the bed of the column and MTZ depth is controlled by various factors such as characteristics of adsorbent, nature of adsorbent, weight and particle size of adsorbent, solution pH, inlet concentration of adsorbate and solution flowrate. Out of all these factors, the lifespan of the column is mainly influenced by flow rate, adsorbate concentration and weight of adsorbent (bed-depth). 9
ACCEPTED MANUSCRIPT
The following equation is used to compute the MTZ or unused bed length (HUNB) (cm) [34].
RI PT
= 1 − = 1 −
SC
where, HT is the total bed length, cm.
(4)
M AN U
=
(3)
The utilized bed length (HB) (cm) for the breakthrough point is estimated as [34]:
=
(5)
TE D
(6)
AC C
!"## = $
EP
The total treated volume (Veff) (mL) is estimated as:
where, Q (mL min-1) is the volumetric flowrate and tt (min) is the total flow time. The total adsorbed RO84 dye quantity is computed as:
%&'( =
)*
=
) - ./ +',
(7)
10
ACCEPTED MANUSCRIPT
where, qtotal (mg) is the amount of RO84 adsorbed; A is the area under the BTC, Cad (Ci-Ct) (mg L-1) is the adsorbed concentration and t (min) is the time which can be tt or tsat (min) showing either total flow time or the saturation time.
RI PT
The following equation can be used to compute the total quantity of RO84 dye fed to the column (mtotal) (mg) [34].
)
SC
M AN U
0&'( =
(8)
Total removal (%) of RO84 dye is estimated as [34].
8 - ./
9 - ./
× 100
(9)
TE D
% 2304567 =
2.6. Modeling of fixed-bed column break-through An appropriate model satisfies the need of explaining the behavior of adsorption column
EP
and applying it to industrial level. Various mathematical models have been established which explains and probably anticipates the dynamic trend of the column bed. Thomas, Yoon-Nelson
AC C
and Bed Depth Service Time (BDST) models are widely used to study the kinetics of adsorption. This study was focused on finding the most suitable model which describes adsorption kinetics; determining behavior of BTC; and estimating adsorption capacity of the column for removal of RO84 dye and computes kinetic parameters. 2.6.1. Thomas model It is an extensively used model for prediction of BTCs and describing fixed-bed column performance. It assumes that there is no axial dispersion inside the column and the behavior of 11
ACCEPTED MANUSCRIPT
the flow is plug flow. It also obeys reversible second order and Langmuir kinetics of adsorption and considers that adsorption is controlled by the interface mass transfer as well as chemical
The following equation is the linear from of Thomas model.
=>? 8@ A
7< − 1 =
)
=>? - BCDD )
(10)
SC
−
RI PT
reaction [35].
M AN U
where, kTh (mL mg-1 min-1) is the Thomas model constant; qs (mg g-1) is the equilibrium uptake of RO84 per g of CFAC; w (g) is the quantity of CFAC in the column; Ci and Ct (mg L-1) are the concentration of RO84 at influent and effluent streams respectively; and Veff (mL) is the total effluent volume.
qs is determined. 2.6.2. Yoon-Nelson model
TE D
From the intercept and slope of linear plot ln[(Ci/Ct) - 1] vs. time (t), the values of kTh and
This model is less complicated as it does not depend on detailed data regarding the nature
EP
of adsorbent, physical parameters of adsorption bed and characteristics of adsorbate [36].
AC C
The linear from of Yoon-Nelson model can be written as,
7<
E
= FG ( − I)
(11)
where, kYN (min-1) is Yoon-Nelson model constant; t (min) is the sampling time and τ (min) is the required time for 50% adsorbate breakthrough.
12
ACCEPTED MANUSCRIPT
From the intercept and slope of linear plot ln[Ct/(Ci-Ct)] vs. time (t), the values of kYN and τ are determined. 2.6.3. BDST model
RI PT
This model shows linear relation between bed depth of column and service time with adsorption parameters and process concentration [37]. The model was originally derived from the Adam-Bohart equation but it was revised by Hutchins. The assumption of this model is that
SC
external mass transfer resistance and intra-particle diffusion type forces are negligible. So the surface chemisorption among the unused adsorbent and the solute in liquid phase can control the
M AN U
adsorption kinetics [38]. The model gives a comparison between adsorption capacities of the columns operated under various process conditions.
The following equation is the linear from of BDST model.
- K>
−
= -
7< − 1
(12)
TE D
=
where, U (cm min-1) is the linear velocity of influent; ko (L mg-1 min-1) is the BDST model
AC C
column.
EP
constant; No (mg L-1) is the maximum adsorption capacity and HT (cm) is the total bed height of
From the intercept and slope of the plot of service time (t) vs. HT, the values of ko and No
are determined.
2.7. Error analysis
Error analysis is a study about changes in the output of any model as the model parameters fluctuate around an average value and is necessary to assess the experimental data. In this study, error analysis combined with determined coefficient (R2) from regression analysis
13
ACCEPTED MANUSCRIPT
was used to determine the best fitted model for adsorption. Two error analyses techniques were used as follows:
2L = S
∑[(UV EUC )/UC ]Y RE
M AN U
(2) The average relative standard error (ARS)
(13)
SC
LLM = ∑RQ (OP − O" )Q
RI PT
(1) The sum of the squares of the errors (SSE)
(14)
where, yc and ye are the calculated and experimental data respectively and n is the number of
3. Results and discussion
TE D
experimental data points
3.1. Physical characterization of biomass and CFAC
EP
Basic important information on the adsorption phenomena and on the porosity of activated carbon is obtained from the shape of adsorption isotherm. The nitrogen adsorption-
AC C
desorption isotherms at 77 K of CFAC is shown in Fig. 4a. The isotherm is a mixture of type I and type IV as per IUPAC classification. According to the classification, type I isotherm is shown by microporous solids while type IV isotherm can be associated with mixture of micro and mesoporous solids. It was clearly indicated that even at very low relative pressure, the quantity of adsorbed nitrogen increased rapidly and as the relative pressure increased to 0.5, the isotherm showed apparent hysteresis loop. This behavior of adsorption indicated type IV, which is characteristic of mesoporous structure. 14
ACCEPTED MANUSCRIPT
One of the most important physical properties of adsorbents is pore size distribution as it gives information regarding surface heterogeneity. Pores of adsorbent are classified into three categories: Micropore (pore size < 2 nm), mesopore (2 to 50 nm) and macropore (> 50 nm).
RI PT
Barrett, Joyner & Halenda method was used to calculate pore size distribution of CFAC and is shown in Fig. 4b. It indicates that the CFAC contains a mixture of micropores and mesopores,
SC
and the micropore volume contributes about 30% of the total volume.
Fig. 5a indicates the result of absorption bands for FTIR spectrum of precursor biomass
M AN U
and CFAC. The spectrum of the empty cotton flower biomass exhibits presence of various functional groups. But after activation, there is reduction, elimination and broadening of the peaks in the spectra [27,30]. The major peak around 3399 cm-1 shows that the primary functional group on precursor is O-H stretching vibration of hydroxyl groups which includes hydrogen bonding. Other significant peaks at 2925, 2365, 1640 and 1248 cm-1 represent C-H stretching of
TE D
hydrocarbon bond, C≡C stretching of alkyne group, C=C stretching of weak alkene group and CO-C stretching of esters and carboxylic anhydrides. A noticeable change is observed in the spectra of CFAC compared to spectra of precursor in the bands of two regions between 1000 to
EP
1750 cm-1 and 2800 to 3500 cm-1. In these two regions, the intensities of bands reduce sharply
AC C
which suggests disappearance of many weak bonds during activation process. The surface morphology of precursor and CFAC are presented in Figs. 5b and 5c,
respectively. The surface structure of precursor biomass is rough with few pores. Noticeable pore structures with lot of cavities are noticed at the surface of CFAC. This is probably because of the vaporization of volatile impurities and tarry matters present in the biomass leading to a wellestablished porous structure. Also during activation, the enhancement of C-KOH reaction causes some carbon to be “burnt off” thereby creating well-structured pores on the surface of the CFAC. 15
ACCEPTED MANUSCRIPT
3.2. Effect of initial flowrate Influent flowrate is one of the key factors for determining the effectiveness of adsorbents in large scale continuous fixed-bed adsorption. The effect of influent flowrate for the adsorption of
RI PT
RO84 dye onto CFAC was studied by changing the flowrate (10, 15 and 18 mL min-1) while the initial RO84 dye concentration and the bed height were kept constant at 150 mg L-1 and 5 cm, respectively. Fig. 6a shows the plot between normalized dye concentration (Ct/Ci) and time (min)
SC
at varying flowrates. Rapid occurrence of the BTC was observed at higher flowrates. As the velocity increased, dye particle eluted faster. The time of breakthrough decreased from 240 to 64
M AN U
min when flowrate was altered from 10 to 18 mL min-1 (Table 2). The important BTC parameters are shown in Table 2. Speed of adsorption zone increased at higher flowrate, hence the time required to attain the specific breakthrough concentration was less compared to lower flowrate. The data from Table 2 indicated that the dye removal percentage and adsorption
TE D
capacity (qtotal) of column decreased with increasing flowrate. Many researchers have also reported similar observations for different other adsorption systems [38–40]. As the velocity increased, the mass transfer rate increased which intensified the rate of adsorption. Therefore, an
EP
early breakthrough was obtained at higher flowrates [41]. Also at higher flowrates, residence time of dye molecules in the column was less and contact time with adsorbents was also less, so
AC C
intra-particle diffusion phenomena between RO84 dye molecules and CFAC was not properly executed. As a result, adsorption capacity (qtotal) decreased and equilibrium was not attained. This is also supported by HB or MTZ shown in Table 2. Low flowrates provided sufficient contact time between RO84 dye and CFAC resulting in higher removal of RO84 dye as shown in Table 2. 3.3. Effect of initial concentration
16
ACCEPTED MANUSCRIPT
Fig. 6b represents the effect of initial RO84 dye concentration on the BTC at constant flowrate of 15 mL min-1 and bed height of 5 cm. The shape of BTCs is same for different RO84 dye concentrations, giving shrinkage in MTZ. A moderate BTC was obtained when the initial
RI PT
concentration of RO84 dye decreased. At lower concentration, the mass transfer coefficient was low because of slower transport phenomena. Calculated BTC parameters of adsorption column for removal of RO84 dye by CFAC at various RO84 dye solution concentrations are shown in
SC
Table 3. The driving force for mass transfer operation is the concentration gradient and therefore increasing the initial concentration of dye gave high equilibrium adsorption capacity and total
M AN U
percentage removal of dye as presented in Table 3. The treated volume increased with decreased RO84 dye concentration, and so the BTC shifted to the right. When the RO84 dye concentration was high, the shape of BTC was relatively steeper giving lesser breakthrough time because the diffusion process mostly depended on concentration. An extended BTC was obtained with
TE D
decreased initial RO84 dye concentration because of the lower concentration gradient resulting in slower transport [37]. Available vacant adsorbent sites were rapidly filled at high initial concentration of RO84; hence time required for breakthrough was reduced. As per Table 3, it
EP
was found that more adsorption of dye and more percentage removal of RO84 (35%) were obtained for higher RO84 dye concentration. Therefore, high initial concentration of dye and
AC C
high dye loading rates gave an improved column performance. Similar type of trends are found for removal of methylene blue using cedar sawdust and crushed brick, pine cone biomass and use of granular activated carbon for removal of Reactive Black 5 [30,38,39]. 3.4. Effect of bed height
The BTCs obtained from RO84 dye adsorption on CFAC at different bed heights of 3, 5 and 7 cm with initial RO84 dye concentration of 150 mg L-1 and flowrate of 15 mL min-1 are
17
ACCEPTED MANUSCRIPT
shown in Fig. 6c. It is observed that breakthrough time increased as the bed height increased. The removal percentage of dye increased from 22 to 34% (Table 4) when the bed height was raised from 3 to 7 cm. The shape of BTC observed for the bed height of 3 cm was steeper than
RI PT
that of 5 and 7 cm. This was because of the shorter mass transfer zone developed in column. As the bed height decreased, the load of adsorbent in the column was less, so there was less capacity for bed to adsorb dye from solution. Therefore, rate of adsorption increased faster. Once the
SC
adsorbent was close to saturate, there was rise in concentration of RO84 dye in effluent stream as time proceeded. As the quantity of loaded adsorbent in column was high, the contact time
M AN U
between adsorbate-adsorbent increased resulting in enhanced “sweep efficiency” [41]. With increasing bed height, the enhancement of binding sites for adsorption of dye was possible as the surface area of adsorbent increased. The used bed height also increased from 0.33 to 1.25 cm. Process of intra-particle diffusion and column adsorption capacity could be improved with the
TE D
increased bed height and hence, more quantity of effluent could possibly be handled with better RO84 removal efficiency with raising bed height which is better for column adsorption [38]. This observation is supported by work of other researchers [44,45].
EP
3.5. Dynamic modeling of fixed-bed column 3.5.1. Thomas model
AC C
The maximum solid-phase concentration (qs) and the rate constant (kTh) of the Thomas
model was determined by using the adsorption data. A linear regression technique as shown in Eq. (10) was used to obtain the determined coefficients and relative constants. Fig. 7 represents the linear plots between ln[(Ci/Ct) – 1] and t at specified experimental conditions. From the Table 5, the high value of linear regression coefficient (R2 > 0.9) with comparatively lower values of SSE (< 1.71) and ARS (< 0.25) gave conformation that this model fitted better with the
18
ACCEPTED MANUSCRIPT
experimental data. Furthermore, the calculated values of qs were close to the experimental values at different experimental conditions. Also as observed from Table 5, lower value of kTh and the higher value of qs was obtained as initial concentration of RO84 dye increased. This was due to
RI PT
the concentration gradient of the dye between two phases (solid and liquid) which acted as a driving force for adsorption. Hence, better performance of column required higher initial RO84 dye concentration which was the driving force. As the flow rate increased, the value of qs
SC
drastically fell, and kTh increased. As the bed depth increased, kTh decreased but qs improved. So higher initial RO84 dye concentration, higher bed depth and lower flowrate were favorable for
M AN U
adsorption of RO84 onto CFAC column [34,43,45–47]. 3.5.2. Yoon-Nelson model
Yoon-Nelson model is a less complicated theoretical model to study behavior of the breakthrough of RO84 dye on CFAC. A straight line was obtained against the plot between
TE D
ln[Ct/(Ci-Ct)] and t as shown in Fig. 8. From Table 6 it can be observed that, the value of linear regression coefficient is high (R2 > 0.9) and the values of SSE (2.94-7.91) and ARS (0.14-0.69) were more than Thomas model and the calculated values of τ (min) did not match the
EP
experimental values. It was observed that higher value of kYN was achieved with both higher flowrate and initial concentration of RO84 dye, but the value declined with higher bed height.
AC C
Also, the value of τ decreased with increasing flowrate and initial RO84 dye concentration but increased with increasing bed height. This might be because of the fact that increasing initial concentration of RO84 raised the competition among dye molecules for the location of adsorption, which finally gave higher uptake rate [42,44]. 3.5.3. BDST model
19
ACCEPTED MANUSCRIPT
The column experimental data of adsorption of RO84 dye on to CFAC closely fits the BDST model. A straight line was obtained against the plot between service time (t) vs. total bed height. The values of BDST parameters ko and No can be calculated from the slope and intercept
RI PT
of line. This model is applicable to explain the characteristics of fixed bed and to take pilot level experiments to commercial applications. This model provides an estimation of the column effectiveness which operates at constant conditions for attaining a required level of
SC
breakthrough. From Fig. 9, the calculated values of No, ko and R2 were 22273 mg L-1, 0.52 mL mg-1 min-1 and 0.99, respectively. From calculated No value, the maximum adsorption capacity
M AN U
of the CFAC can be computed as 49.49 mg g-1 considering present experimental conditions. The high value of R2 shows better suitability of this model for the present study. The high value of ko conveys that a short bed will escape breakthrough, but if the value of ko is low, a moderately longer bed will be needed to escape breakthrough [32-34]. The parameter of BDST model is
4. Conclusions
TE D
supportive to extend process for various flowrates.
This study affirms that the prepared activated carbon from locally, freely and amply
EP
available agricultural waste of empty cotton flower biomass can be an appropriate option of activated carbon obtained from non-renewable source for the adsorption of RO84 dye. As per the
AC C
column experimental results, the adsorption of dye is dependent on the flowrate, influent RO84 dye concentration and the bed height. When the flowrate increased from 10 to 18 mL min-1, the removal percentage was increased by 5%. On the other hand, when the influent concentration increased from 100 to 200 mg L-1, the removal percentage increased by about 8%, and as bed depth increased against 3 to 7 cm, the removal percentage enhanced by 12%. The maximum adsorption capacity of column was found to be about 720 mg of RO84 per 4.67 g of CFAC
20
ACCEPTED MANUSCRIPT
adsorbent for the values of initial concentration, flowrate and bed height of 200 mg L-1, 15 mL min-1 and 5 cm, respectively. The value of errors calculated through SSE and ARS were lower in Thomas model than Yoon-Nelson model so adsorption of RO84 on to CFAC was better
RI PT
described by Thomas model. The high value of correlation coefficients (R2 > 0.99) was obtained in BDST model which suggested suitability of this model for the prediction of breakthrough curve. The model constants of BDST model can be useful to operate the adsorption process at
SC
different concentrations and flowrates without more experimental run. The calculated value of No and ko from the BDST model were 22273 mg L-1 and 0.52 mL mg-1 min-1, respectively.
M AN U
Acknowledgements
We gratefully acknowledge CSIR-India. ADCIF is acknowledged for analysis of BET surface area, CHNS, SEM and FTIR. A part of this work has been carried out by Mr. R. Yadav, B. Tech student of Chemical Engineering Department of IIT-BHU, Varanasi during his summer
References*47
TE D
internship. This is CSIR-CSMCRI PRIS. NO. 182/2017.
[1] Osma JF, Saravia V, Toca-Herrera JL, Couto SR. Sunflower seed shells: a novel and
EP
effective low-cost adsorbent for the removal of the diazo dye Reactive Black 5 from aqueous solutions. J Hazard Mater 2007;147:900-5.
AC C
[2] Soares GMB, Amorim MTP, Hrdina R, Costa-Ferreira M. Studies on the biotransformation of novel disazo dyes by laccase. Process Biochem 2002;37:581-7. [3] O'Mahony T, Guibal E, Tobin JM. Reactive dye biosorption by Rhizopus arrhizus biomass. Enzyme Microb Tech 2002;31:456-63. [4] Yang XY, Al-Duri B. Application of branched pore diffusion model in the adsorption of reactive dyes on activated carbon. Chem Eng J 2001;83:15-23.
21
ACCEPTED MANUSCRIPT
[5] Kirk RE, Othmer DF, Grayson M, Eckroth D. Encyclopedia of Chemical Technology. 3rd ed. New York: Wiley-Interscience Publication; 1978. [6] Papic S, Koprivanac N, Bozic AL, Metes A. Removal of some reactive dyes from synthetic
2004;62:291-8.
RI PT
wastewater by combined Al(III) coagulation/carbon adsorption process. Dyes Pigments
activated carbon. Ind Eng Chem Res 1998;37:253-7.
SC
[7] Chern JM, Huang SN. Study of nonlinear wave propagation theory. 1. Dye adsorption by
J Hazard Mater 2003;98:211-24.
M AN U
[8] Ozacar M, Sengil IA. Adsorption of reactive dyes on calcined alunite from aqueous solutions.
[9] Chern JM, Chien YW. Adsorption of nitrophenol onto activated carbon: isotherms and breakthrough curves. Water Res 2002;36:647-55.
[10] Singh S, Srivastava VC, Mall ID. Fixed-bed study for adsorptive removal of furfural by
TE D
activated carbon. Colloid Surface A 2009;332:50-6.
[11] Vazquez G, Alonso R, Freire S, Gonzalez-Alvarez J, Antorrena G. Uptake of phenol from aqueous solutions by adsorption in a Pinus pinaster bark packed bed. J Hazard Mater
EP
2006;133:61-7.
[12] Walker GM, Weatherley LR. Adsorption of acid dyes on to granular activated carbon in
AC C
fixed beds. Water Res 1997;31:2093-101. [13] Chern JM, Chien YW. Competitive adsorption of benzoic acid and p-nitrophenol onto activated carbon: isotherm and breakthrough curves. Water Res 2003;37:2347-56. [14] Padmesh TVN, Vijayaraghavan K, Sekaran G, Velan M. Biosorption of Acid Blue 15 using fresh water macroalga Azolla filiculoides: batch and column studies. Dyes Pigments 2006;71:7782.
22
ACCEPTED MANUSCRIPT
[15] Martin MJ, Artola A, Balaguer MD, Rigola M. Activated carbons developed from surplus sewage sludge for the removal of dyes from dilute aqueous solutions. Chem Eng J 2003;94:2319.
experimental and modeling. Chemosphere 2005;58:1079-86.
RI PT
[16] Chakraborty S, De S, DasGupta S, Basu JK. Adsorption study for the removal of a basic dye:
[17] Girgis BS, El-Hendawy ANA. Porosity development in activated carbons obtained from
SC
date pits under chemical activation with phosphoric acid. Micropor Mesopor Mat 2002;52:10517.
M AN U
[18] Diao YL, Walawender WP, Fan LT. Activated carbons prepared from phosphoric acid activation of grain sorghum. Bioresource Technol 2002;81:45-52.
[19] Jagtoyen M, Derbyshire F. Activated carbons from yellow poplar and white oak by H3PO4 activation. Carbon 1998;36:1085-97.
TE D
[20] Suarez-Garcia F, Martinez-Alonso A, Tascon JMD. Pyrolysis of apple pulp: chemical activation with phosphoric acid. J Anal Appl Pyrol 2002;63:283-301. [21] Vernersson T, Bonelli PR, Cerrella EG, Cukierman AL. Arundo donax cane as a precursor
2002;83:95-104.
EP
for activated carbons preparation by phosphoric acid activation. Bioresource Technol
AC C
[22] Guo J, Lua AC. Textural and chemical properties of adsorbent prepared from palm shell by phosphoric acid activation. Mater Chem Phys 2003;80:114-9. [23] Solum MS, Pugmire RJ, Jagtoyen M, Derbyshire F. Evolution of carbon structure in chemically activated wood. Carbon 1995;33:1247-54. [24] Wikipedia contributors. Cotton. Wikipedia, The Free Encyclopedia; 2018 [25] Sheth K. Top cotton producing countries in the world. Quebec, Canada: World Atlas; 2018
23
ACCEPTED MANUSCRIPT
[26] Das P, Charola S, Dinda M, Patel H, Maiti S. Hydrogen from empty cotton boll agro-waste via thermochemical route and, feasibility study of operating an IC engine in continuous mode. Int J Hydrogen Energ 2017;42:14471-84.
RI PT
[27] Deng H, Li GX, Yang HB, Tang JP, Tang JY. Preparation of activated carbons from cotton stalk by microwave assisted KOH and K2CO3 activation. Chem Eng J 2010;163:373-81.
[28] Deng H, Yang L, Tao GH, Dai JL. Preparation and characterization of activated carbon
SC
from cotton stalk by microwave assisted chemical activation - application in methylene blue adsorption from aqueous solution. J Hazard Mater 2009;166:1514-21.
M AN U
[29] Deng H, Zhang GL, Xu XL, Tao GH, Dai JL. Optimization of preparation of activated carbon from cotton stalk by microwave assisted phosphoric acid-chemical activation. J Hazard Mater 2010;182:217-24.
[30] El-Hendawy ANA, Alexander AJ, Andrews RJ, Forrest G. Effects of activation schemes on
Appl Pyrol 2008;82:272-8.
TE D
porous, surface and thermal properties of activated carbons prepared from cotton stalks. J Anal
[31] Agrawal P, Bajpai AK. Dynamic column adsorption studies of toxic Cr(VI) ions onto iron
EP
oxide loaded gelatin nanoparticles. J Disper Sci Technol 2011;32:1353-62. [32] Nwabanne JT, Igbokwe PK. Adsorption performance of packed bed column for the removal
AC C
of lead (II) using oil palm fibre. Int J Appl Sci Technol 2012;2:106–15. [33] Aksu Z, Cagatay SS, Gonen F. Continuous fixed bed biosorption of reactive dyes by dried Rhizopus arrhizus: determination of column capacity. J Hazard Mater 2007;143:362-71. [34] Yagub MT, Sen TK, Afroze S, Ang HM. Fixed-bed dynamic column adsorption study of methylene blue (MB) onto pine cone. Desalin Water Treat 2015;55:1026-39.
24
ACCEPTED MANUSCRIPT
[35] Thomas HC. Heterogeneous ion exchange in a flowing system. J Am Chem Soc 1944;66:1664-6. [36] Yoon YH, Nelson JH. Application of gas adsorption kinetics .1. A theoretical model for
RI PT
respirator cartridge service life. Am Ind Hyg Assoc J 1984;45:509-16.
[37] El-Kamash AM. Evaluation of zeolite A for the sorptive removal of Cs+ and Sr2+ ions from aqueous solutions using batch and fixed bed column operations. J Hazard Mater 2008;151:432-
SC
45.
[38] Sadaf S, Bhatti HN. Evaluation of peanut husk as a novel, low cost biosorbent for the
Technol Envir 2014;16:527-44.
M AN U
removal of Indosol Orange RSN dye from aqueous solutions: batch and fixed bed studies. Clean
[39] Cruz-Olivares J, Perez-Alonso C, Barrera-Diaz A, Urena-Nunez F, Chaparro-Mercado MC, Bilyeu B. Modeling of lead (II) biosorption by residue of allspice in a fixed-bed column. Chem
TE D
Eng J 2013;228:21-7.
[40] Kahraman S, Yalcin P, Kahraman H. The evaluation of low-cost biosorbents for removal of an azo dye from aqueous solution. Water Environ J 2012;26:399-404.
EP
[41] Sen TK, Mahajan SP, Khilar KC. Colloid-associated contaminant transport in porous media: 1. Experimental studies. AIChE J 2002;48:2366-74.
AC C
[42] Hamdaoui O. Dynamic sorption of methylene blue by cedar sawdust and crushed brick in fixed bed columns. J Hazard Mater 2006;138:293-303. [43] Ahmad AA, Hameed BH. Fixed-bed adsorption of reactive azo dye onto granular activated carbon prepared from waste. J Hazard Mater 2010;175:298-303.
25
ACCEPTED MANUSCRIPT
[44] Han RP, Wang Y, Zhao X, Wang YF, Xie FL, Cheng JM, et al. Adsorption of methylene blue by phoenix tree leaf powder in a fixed-bed column: experiments and prediction of breakthrough curves. Desalination 2009;245:284-97.
RI PT
[45] Tan IAW, Ahmad AL, Hameed BH. Adsorption of basic dye using activated carbon prepared from oil palm shell: batch and fixed bed studies. Desalination 2008;225:13-28.
[46] Han RP, Wang YF, Yu WH, Zou WH, Shi H, Liu HM. Biosorption of methylene blue from
SC
aqueous solution by rice husk in a fixed-bed column. J Hazard Mater 2007;141:713-8.
[47] Aksu Z, Gonen F. Biosorption of phenol by immobilized activated sludge in a continuous
AC C
EP
TE D
M AN U
packed bed: prediction of breakthrough curves. Process Biochem 2004;39:599-613.
26
ACCEPTED MANUSCRIPT
2
Some of selected physic-chemical properties of CFAC.
7 3 4 5 6 7 8
6.95 0.45 1058 417 0.69 0.21 26.5 360
Applied Method ASTMD 3838-80 ASTM E-873-06
M AN U
pH Bulk Density, g mL-1 Surface Area (BET), m2 g-1 t-plot micropore area, m2 g-1 Total pore volume, cm3 g-1 t-plot micropore volume, cm3 g-1 Average pore diameter, Å Methylene Blue Value, mg g-1 Proximate Analysis: Moisture (%) Volatile matter (%) Ash (%) Fixed Carbon (%) (By Difference) Ultimate Analysis: C (%) H (%) N (%) S (%) O (%) Burn off (%)
5.1 12.9 6.9 75.1
TE D
6
Value
82.78 5.88 0.26 0.03 12.05 83.8
EP
4 5
Properties
AC C
Sr. No. 1 2 3
RI PT
Table 1
SC
1
9 10 11
27
JIS K 1470-1991
ASTM D3173 ASTM D3175 ASTM D3174 ASTM E1131-08 ASTM Standard
ACCEPTED MANUSCRIPT
Table 2 Calculated parameters derived from BTCs of column for adsorption of RO84 on to CFAC at different influent flowrate. Q (mL min-1) 10 15 18
t
V
b
(min) 240 90 65
eff
(mL) 12400 12300 10350
t
total
(min) 1240 820 585
m
total
(mg) 1860 1845 1575
q
total
(mg) 563 531 492
HB (cm)
4.03 4.45 4.48
0.97 0.55 0.52
Table 3 Calculated parameters derived from BTCs of column for adsorption of RO84 on to CFAC at different initial concentration. Concentration (mg L-1) 100 150 200
tb
Veff
ttotal
mtotal
qtotal
% Removal
(min) 240 90 30
(mL) 16500 12300 10300
(min) 1100 820 690
(mg) 1650 1845 2070
(mg) 449 531 720
MTZ (cm)
HB (cm)
27 29 35
3.91 4.45 4.78
1.09 0.55 0.22
EP
TE D
23
Table 4 Calculated parameters derived from BTCs of column for adsorption of RO84 on to CFAC at different bed height.
AC C
24 25 26 27 28 29 30 31 32 33
30 29 25
M AN U
17 18 19 20 21 22
MTZ (cm)
SC
16
% Removal
RI PT
12 13 14 15
Bed Height (cm) 3 5 7
t
b
(min) 55 90 200
V
eff
(mL) 7500 12300 16800
t
m
total
(min) 500 820 1120
total
(mg) 1125 1845 3360
34 35 28
q
total
(mg) 247 531 1142
% Removal
MTZ (cm)
HB (cm)
22 29 34
2.67 4.45 5.75
0.33 0.55 1.25
ACCEPTED MANUSCRIPT
Table 5 Calculated constants of Thomas kinetic model at different experimental condition. Flowrate (mL min-1) 10 15 18
Initial Dye Conc. (mg L-1) 100 150 200
Bed Height (cm) 3 5 7
0.032
0.040
0.078
0.045
0.040
0.035
0.076
0.040
0.028
212 208 0.90 0.95 0.14
161 162 0.92 0.84 0.093
121 120 0.93 0.42 0.084
142 148 0.91 0.39 0.25
161 162 0.92 0.84 0.093
186 189 0.97 0.41 0.094
128 130 0.90 1.05 0.11
161 162 0.92 0.84 0.093
182 181 0.91 1.71 0.09
TE D
M AN U
39
Table 6 Calculated constants of Yoon-Nelson kinetic model at different experimental condition. Flowrate (mL min-1) 10 15 18
Initial Dye Conc. (mg L-1) Bed Height (cm) 100 150 200 3 5
7
kYN (min )
0.046
0.063
0.107
0.047
0.063
0.071
0.094
0.063
0.042
τ (min) (exp) τ (min) (cal) R2 SSE ARS
525 650 0.90 7.91 0.65
309 351 0.91 4.57 0.35
205 239 0.93 4.44 0.14
530 613 0.91 6.92 0.63
309 351 0.91 4.57 0.35
268 287 0.97 2.94 0.28
194 2465 0.91 5.32 0.58
309 351 0.91 4.57 0.35
336 453 0.91 7.86 0.69
Yoon-Nelson Parameters
AC C
-1
EP
40 41 42 43 44 45 46 47 48 49 50 51 52 53
RI PT
Thomas Parameters kTh (mL min-1 mg-1) qs (mg g-1) (exp) qs (mg g-1) (cal) R2 SSE ARS
SC
36 37 38
54 55 56
29
SC
RI PT
ACCEPTED MANUSCRIPT
58
Fig. 1. Chemical Structure of RO84.
59 60 61
65 66 67 68 69 70
EP
64
AC C
63
TE D
62
M AN U
57
71 72
30
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
73 74
Fig. 2. Schematic diagram of preparation of CFAC.
78 79 80 81 82 83
EP
77
AC C
76
TE D
75
84 85 86
31
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
87
EP
TE D
Fig. 3. Schematic Diagram of the adsorption column.
AC C
88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
32
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
109
Fig. 4. (a) Adsorption-desorption isotherm of N2 at 77 K on the CFAC; (b) Pore size
111
distributions of the CFAC by BJH method.
EP
113 114 115 116 117 118 119 120 121
AC C
112
TE D
110
33
126 127 128 129
EP
125
Fig. 5. (a) FTIR spectra of empty cotton flower and CFAC; (b) SEM image of empty cotton flower and (c) SEM image of CFAC.
AC C
122 123 124
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
130 131 132 34
137 138 139
Fig. 6. Effect of different parameters on shape of BTCs of RO84 adsorption on CFAC (a) Flowrate; (b) Inlet RO84 dye concentration; (c) bed height.
EP
134 135 136
AC C
133
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
35
AC C
140 141 142 143 144 145 146 147 148 149 150
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 7. Linear plots of Thomas kinetic model for the adsorption of RO84 dye on CFAC with (a) different flowrate; (b) different initial concentration; (c) different bed height.
36
151 152 153 154 155 156 157
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 8. Linear plots of Yoon-Nelson kinetic model for the adsorption of RO84 dye on CFAC with (a) different flowrate, (b) different initial concentration and(c) different bed height.
37
162 163 164 165
170 171 172
EP
169
AC C
168
TE D
166 167
SC
Fig. 9. Bed depth versus service time plot
M AN U
158 159 160 161
RI PT
ACCEPTED MANUSCRIPT
38