adsorption as alternative process lucia r. raddi de … no3_p215-226_jul … · reactions in the...

Chemical Industry & Chemical Engineering Quarterly

Available on line at Association of the Chemical Engineers of Serbia AChE www.ache.org.rs/CICEQ

Chem. Ind. Chem. Eng. Q. 26 (3) 215−226 (2020) CI&CEQ

215

LUANA VENTURA BAIA1

LUCIA R. RADDI DE ARAUJO1

CARLOS GUERRA PEREIRA2

WALLACE CARVALHO DE SOUZA2

MARCO ANTONIO GAYA DE FIGUEIREDO1

1Instituto de Química, Programa de Pós-graduação em Engenharia

Química, Universidade do Estado do Rio de Janeiro, Maracanã, Rio

de Janeiro, Brasil 2Instituto de Química, Laboratório

de Engenharia e Tecnologia de Petróleo e Petroquímica,

Universidade do Estado do Rio de Janeiro, Maracanã, Rio de Janeiro,

Brasil

SCIENTIFIC PAPER

UDC 544.723.2:621:662.7

ADSORPTION AS ALTERNATIVE PROCESS IN THE PRELIMINARY PRODUCTION OF AUTOMOTIVE ADDITIVE

Article Highlights • In Brazil, urea solution for SCR is ARLA 32 with maximum content of 0.3 wt.% biuret • Adsorption was used as treatment process in removal of biuret from an aqueous urea

solution • After adsorption, commercial urea is a promising raw material for ARLA 32 production• Activated coal (high surface area) is a suitable adsorbent for removing biuret from the

urea solution Abstract

Nitrogenous contaminants in the diesel fraction are converted to NOx com-pounds in an automotive combustion chamber. Afterwards, they are reduced to nitrogen by catalytic reduction/oxidation reactions in presence of ammonia derived from a 32.5 wt.% urea solution. This process is named selective cat-alytic reduction (SCR). In Brazil, the urea solution for SCR is ARLA 32 and must comply with the limit content of 0.3 wt.% of biuret. However, the commer-cial Brazilian urea solution has an average biuret content of 0.5 wt.%. Thus, it is necessary to adjust the biuret content in urea solution to be used as ARLA 32, and adsorption is a low energy option. The objective of this study was to evaluate commercial adsorbents for removing biuret from solution of commer-cial urea to adjust it to the specification of ARLA 32. Two activated coals and one ion exchange resin were tested in adsorption assays, with best perform-ances of both coals.

Keywords: adsorption, biuret, characterization, coal.

In the last decades, the environment became a major concern of society due to people’s awareness of its importance to their quality of life. One of the major reasons of current air pollution is the emission of sulfur oxides (SOx) and nitrogen oxides (NOx) resulting from fossil fuels burning in automotive cycles [1,2]. This new priority has made the number of requirements regarding the specifications of petro-leum-derived fuels rise as well as the severity of the limit of SOx and NOx quantities emitted in current legislation [2,3].

Correspondence: L.R.R. de Araujo, Instituto de Química, Pro-grama de Pós-graduação em Engenharia Química, Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier, 524, Maracanã, Rio de Janeiro, RJ, CEP 20.550-900, Brasil. E-mail: [email protected] Paper received: 19 April, 2019 Paper revised: 26 September, 2019 Paper accepted: 2 December, 2019

https://doi.org/10.2298/CICEQ190419038B

Most of the sulfide and nitrogen compounds present in petroleum fractions is removed through the catalytic process known as hydrotreatment (HDT), in which, at high temperature and pressure, sulfur and nitrogenous hydrocarbons are converted into free hydrocarbons [4,5]. The remaining nitrogenous con-taminants not removed in the primary processes are still present in the fuel product and are converted to NOx compounds during burning in the automotive combustion chamber [6].

Before NOx compounds are released to the atmosphere, they are reduced to nitrogen gas (N2) and water (H2O) by catalytic reduction and oxidation reactions in the presence of catalysts such as pla-tinum/rhodium/alumina supported on cordierite or vanadium-tungsten-titanium oxides (V2O5/WO3/TiO2) [7]. In the case of diesel vehicles, there are numerous ways to reduce NOx compounds, however the most selective and currently used method is the process known as selective catalytic reduction (SCR) using

L.V. BAIA et al.: ADSORPTION AS ALTERNATIVE PROCESS… Chem. Ind. Chem. Eng. Q. 26 (3) 215−226 (2020)

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ammonia. In this process, a 32.5 wt.% aqueous sol-ution of urea is injected into the hot stream of the SCR catalyst and this urea undergoes hydrolysis, forming ammonia and carbon dioxide. The free ammonia reacts with NOx to form N2 and water [8].

In Brazil, the urea solution used in the SCR has been named ARLA 32 conferred by IBAMA (Por-tuguese acronym for Brazilian Institute of Environ-ment and Renewable Natural Resources) by means of a normative instruction of July 11, 2009. In this ins-truction, the urea solution specification was deter-mined so that it could be classified as ARLA 32. The specification of ARLA 32 solution sets the limiting content of 0.3 wt.% for the biuret compound and limits the presence of aldehydes and other substances [9]. Biuret is a dimer of urea, being a by-product of its pro-duction process [10]. Biuret (H2N-CO-NH-CO-NH2) is an organic compound with different commercial applications of potential value such as a precursor for drugs, herbicides and other compounds, analytical reagents and feed supplements for ruminants [11]. However, biuret is widely known for its toxicity to a wide variety of plants and is therefore regulated by government agencies and industries.

Nowadays, urea from Brazil has a maximum permitted biuret content of 1.5 wt.% [12], which results in a 32.5 wt.% aqueous urea solution with approximately 0.50 wt.% of biuret, in other words, this solution has a biuret content above the limit value stipulated by IBAMA’s specification. Thus, it is neces-sary to adjust the biuret content in urea produced in Brazilian industries and one possibility is through a rise in conditions of temperature and pressure, con-sequently, making the urea production cost higher. Another option is the performance of a post-treatment of the aqueous solution of urea currently produced in Brazilian industries, in terms of biuret content, in order to be used as ARLA 32 in diesel vehicles.

There are different methods for removing biuret from urea solution, however practically they are all found in the patent’s form. Among these methods, adsorption appears as an alternative [13] of low energy cost (performed at room temperature), simple operation, carried out in simple equipment, and, gen-erally, it presents high adsorption removal efficiency when the technique is applied correctly [14].

Adsorption is a spontaneous process, present in important industrial applications: current purification, separation processes, recovery processes, and sto-rage of gases, among others [15,16]. The selection of the adsorbent to be used in the adsorption process is important, since its surface characteristics generate adsorbate adherence [15,17]. In this research, two

activated coals and one ion exchange resin were tested as adsorbents.

The activated coal has differentiated adsorption properties as it can present a high specific area and its pore structure can be adjusted during the activat-ion process [18,19]. Activated carbon is usually apolar but may acquire a slight polarity from the oxidation of its surface, which tends to be hydrophilic and organo-philic, and is used in the adsorption of organic com-pounds and water purification, among other applic-ations [17].

Due to its chemical structure, the ion exchange resin can exchange the mobile ions along its chain with other ions present in the solution and is applied in water purification systems and separation pro-cesses [20].

In this context, this work aims to select the most promising adsorbent among those tested for the rem-oval of biuret in a 32.5 wt.% aqueous solution of commercial urea, by means of a treatment with the technique of adsorption in order to produce an urea aqueous solution within the specification range of ARLA 32 with respect to the biuret contaminant.

EXPERIMENTAL

Aqueous solutions containing 0.65 wt.% of biuret were used in the study. As adsorbents, two carbons (identified as A and B coals) and one basic ion exchange resin, all commercial materials, were tested.

Adsorbent characterization

The chemical composition of the materials was determined by X-ray dispersive energy fluorescence analysis by semi-quantitative scanning on Shimadzu EDX-720 equipment. The sample was sieved to 170 mesh size and subsequently subjected to vacuum.

For the structural characterization, the X-ray dif-fraction analysis was used, using the powder method and X'Pert Panalytical equipment with X-ray tubes with a copper anode, radiation of 0.154 nm, voltage of 40 kV, current of 35 mA, ranging from 6 to 70°, at a rate of 0.0765° s-1 and a step of 0.05°.

The textural properties were determined by nit-rogen adsorption/desorption isotherms at 77 K using Micromeritics ASAP 2020 equipment. Preliminarily, the sample was subjected to vacuum (5 mm Hg) at 350 °C overnight. The BET method was applied to determine the specific area, and the BJH method was used in the determination of pore diameter and volume. For the determination of the micropore area, the t-plot method was applied.


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Scanning electron microscopy (SEM) analysis was performed to evaluate the materials’ morphology. The sample was introduced into the Hitachi model TM3000 under vacuum and subjected to an electron beam. After selecting the image, the elements were mapped using SwiftED 3000 software.

Adsorption tests

Three types of adsorption tests were performed at room temperature (24-26 °C): kinetic curve, ads-orption isotherm and adsorption rupture. In all of these tests, biuret solution with 0.65 wt.% was used.

- Adsorption kinetic curves: the tests were performed in a Dubnoff bath (Quimis, model Q226M2), with shaking at 2.5 Hz. A constant ratio of 1:10 between the adsorbent mass and the volume of solution was used. The collection times were 10, 30, 60, 180, 300 and 360 min.

- Adsorption isotherm curves: the same con-ditions as for kinetic tests were used. The contact time between the solid and liquid phase was kept constant at 480 min for the two coals and at 300 min for the resin. The volume of solution was kept cons-tant at 10 mL for all points, and the adsorbent mass was varied: 0.10; 0.20; 0.35; 0.50; 0.75; 1.0; 1.5; 2.0; 3.0 and 4.0 g.

- Adsorption rupture test: the experiment was carried out in a 0.83 cm diameter glass column packed with adsorbent with a particle size of 30×60 mesh, in descending flow. A mass of 5.0 g of each adsorbent was used, the useful height of the bed varied between 13 and 16 cm and the flow rate was 1 mL min-1.

Mathematical treatment

Kinetics of adsorption For the treatment of kinetic adsorption data, a

model composed of two first-order differential equat-ions was used [21]. The following adsorption react-ions were assumed (Eqs. (1) and (2)):

+ →1

A S ASk

(1)

−

→ +1

AS A Sk

(2)

where A represents the adsorbate, S represents the solid, AS represents the concentration of adsorbate that has been adsorbed, k1 is the adsorption constant rate, and k-1 is the desorption constant rate.

Eqs. (3) and (4), below, represent equilibria (1) and (2) presented above:

−= − + a1 1

dd

mC k C k qt V

(3)

−= −1 1a

ddq Vk C k qt m

(4)

where C is the concentration of adsorbate in the fluid phase (g L-1), t is the time (min), q is the adsorption capacity (g kg-1), ma is the adsorbent mass (kg) and V is the volume of the solution (L).

To solve the system formed by Eqs. (3) and (4), numerical integration was performed, estimating the adsorption and desorption constants by the least squares method with the following objective function (O.F.) (Eq. (5)):

( ) ( )= =

= − + − 2 2' '

exp calc exp calc1 1

. . i i i i

n n

i i

O F C C q q (5)

where C'calc is the concentration calculated on the integration of Eq. (3) (g L-1), qcalc is the calculated capacity in the integration of Eq. (4) (g kg-1), C'exp is the experimental concentration (g L-1) and qexp is the experimental capacity (g kg-1).

Adsorption isotherm For the treatment of the equilibrium data, the

model predicted by the Langmuir-Freundlich isotherm (Eq. (6)) was used:

= +

1 11 1

s LF LF/ 1n nq q K C K C (6)

where qs is the capacity that corresponds to the total coverage, that is, for the formation of the monolayer (g kg-1), KLF is the adsorption equilibrium constant of the Langmuir-Freundlich isotherm, and n1 is the Lang-muir-Freundlich (dimensionless) isotherm constant.

For comparison purposes, the liquid-solid state version of BET isotherm was also used [22]. The isotherm is presented in Eq. (7):

( ) ( )=− − +

m BET

s s BET s BET m BET1 1q K Cq K C K C K C

(7)

where Km is the interaction constant of the first adsorption layer (adsorbate-adsorbent) in L g-1, Ks is the interaction constant of the following adsorption layers (adsorbate-adsorbate) in L g-1, qs corresponds to the maximum adsorbed amount of adsorbate (g kg-1), CBET is the concentration of adsorbate in the solution obtained from BET equation (g L-1), and q is the con-centration of adsorbate in the solid, also known as the adsorption capacity of the solid (g kg-1).

The parameters of the Langmuir-Freundlich (Eq. (6)) and BET (Eq. (7)) equations were adjusted by the least squares method whose minimized objective function (O.F.) is presented in Eq. (8):


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( )=

= −2

exp calc1

. . i i

n

i

O F q q (8)

Adsorption rupture test For the treatment of the rupture data, Eq. (9)

was used [23], as follows:

( ) γβα−−= e

b b0/ et

C C (9)

where α, β and γ are parameters to be adjusted, obeying the following conditions: α > 0; β > 1; γ > 0 (dimensionless), Cb is the concentration of adsorbate in the bed effluent at time t (g L-1), and Cbo is the initial concentration of adsorbate in the solution (g L-1).

The three parameters were adjusted by the least squares method and the minimized objective function is presented in Eq. (10):

=

= −

2

b b

b0 b01 exp calc

. .i i

n

i

C CO FC C

(10)

The Solver tool of the Microsoft Office ExcelTM program was used to minimize all previously pre-sented functions (Eqs. (5), (8) and (10)).

Assessment criteria for the adjustments The coefficient of variation of the mean square

error (CV(MSE)), whose formula is represented by Eq. (11), was used as the evaluation criterion for the adjustments of the experimental data generated in the three tests [24]:

( ) = y

mean

CV SE

MSEY

(11)

where Ymean is the mean of the measured values, and SEY is the standard error of the estimation calculated according to Eq. (12) as follows:

( )( )

−= − − est obs

2

Yr

1

i iY YSE n p (12)

where Yiest is the value estimated by the model, Yiobs is the observed value, nr is the sample size, and p is the number of variables in the regression equation.

Once the coefficient of variation of the mean square error for each adjustment was calculated, the following evaluation criterion was applied to classify it:

CV(MSE) ≤ 5%, “excellent” model (or fit); 5 < CV(MSE) ≤ 10%, “good” model; 10 < CV(MSE) ≤ 20%, “medium” model; CV(MSE) > 20%, “bad” model.

RESULTS AND DISCUSSION

Adsorbent characterization

In Table 1, the results of the chemical analysis of the resin and of the tested carbons are presented. The results of the resin analysis show that it has high carbon and hydrogen contents, which is consistent for this type of polymer material [25]. Table 1 shows that the two coals also have very high carbon and hyd-rogen contents, since coal is a graphite-based mat-erial resulting from the oxidation (carbonization) of carbon-rich material [17]. The other observed ele-ments (residual elements) are derived from the source raw material used during the production of these car-bons [26]. Among these elements, there are alkaline earth metals, which are present in higher content in carbon A than in carbon B.

Table 1. Chemical composition of the adsorbents

Element Concentration, ppm

Resin Coal A Coal B

Si - 5289 4951

S - 1223 3523

Al 401 3764 3521

Na 3928 - 3157

Fe - 2417 855

K - 310 473

Ti - 206 196

Ca - 2303 122

P - 151 111

Mg – 289 -

Ba – 213 -

C+H (wt.%) 99.5 98.4 98.3

The result of the X-ray diffraction analysis of the coals is presented in Figure 1. The ion exchange resin did not show crystalline phases, as was exp-ected for this type of material, evidencing the amor-phous state of the polymer matrices and it did not contain any additive or impurity that could confer crys-tallinity [27].

In Figure 1, in both diffractograms, it is possible to observe the presence of quartz (SiO2), identified by the peaks marked with Q, showing, in the diffraction pattern, reflections at 2θ 20.9, 26.6 and 50.0° [28]. This result agrees with the FRX analysis, which shows the presence of silicon and is another indi-cation of the presence of this residue.

Figure 2 shows the nitrogen adsorption/desorp-tion isotherms of the coals. Due to the thermodegrad-ation of the resin during the pretreatment (degasific-ation) carried out before nitrogen adsorption, it was


219

not possible to perform the textural analysis of the resin, since the result obtained diverged from the one provided in the manufacturer’s datasheet. The litera-ture shows that, due to the low thermal stability of the resins, there is a recognized difficulty in the determin-ation of textural data for this type of material [18].

Figure 1. Diffractograms of the coals: A (upper) and B (lower).

In Table 2, a resume of the data obtained from the textural analysis performed with the commercial coals is presented.

Table 2. Results of the textural analysis of the coals

Parameter Coal A Coal B

SBET (m2 g-1) 1129 961

Smicro (m2 g-1) 316 692

Dmeso (nm) 2.7 5.9

Dmicro (nm) 1.8 1.6

Vpore (cm3 g-1) 0.59 0.53

Table 2 shows that both carbons had a specific surface area (SBET) within the range indicated in the literature for this type of material, ranging from 500 to 1200 m2 g-1 [29]. These values are much higher than the specific area value provided by the resin manu-facturer, which is 30 m2 g-1. The pore volumes (Vpore) of the coals are at the top of the range commonly found in the literature for this type of material, which is 0.02 to 0.5 cm3 g-1 [17]. The coals have pore volumes that are higher than those reported in the resin data sheet (0.20 cm3 g-1).

Figure 2. Nitrogen adsorption and desorption isotherms at 77 K: a) coal A; b) coal B.


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Activated coals are materials known for their microporosity and the mean micropore diameters (Dmicro) were similar for both carbons [30]. These mat-erials also presented diameters in the mesoporous range, which confirms their multiporous character [17]. The coals presented mean pore diameter values that were much lower than the values reported in the resin datasheet, which is 29 nm indicating that this resin is a mesoporous material.

By analyzing the micropore area values (Smicro), we note that the microporous area of carbon B cor-responds to 72% of the total surface area. For coal A, the external area is much larger than the internal one.

The biuret structure was simulated to estimate the diameters of the molecule on each Cartesian axis (x, y, z). For this simulation, the ACDLabs ACD/3D program was used [31]. The following values were obtained as simulation results: X-axis: 0.37 nm; Y -axis: 0.356 nm; and Z-axis: 0.214 nm. So, the biuret molecule has dimensions that are much smaller than the pores of all the tested adsorbents, indicating that there was no steric hindrance during the adsorption studies performed with these adsorbents that could prevent the diffusion of the adsorbate into the pores.

In Figure 3, MEV images of coal A are pre-sented, and Figure 4 shows MEV images of coal B.

In Figure 3A, it is possible to visualize the pre-dominance of carbonaceous material on the surface

of the analyzed region, the expected result for coal [17]. Figure 3B shows a significant presence of silicon on the displayed surface. The presence of silicon is common in activated carbons derived from coals and it is usually found in the silica form, which is exposed to the surface during the activation process [32]. In Figure 3C, with a zoom of 2000×, it is possible to observe holes, black spots, which are identified as possible pores present on the charcoal surface ana-lyzed and are indicated by the red arrows [33].

In Figure 4A, the presence of the carbon ele-ment on the surface of the analyzed region predom-inates. Figure 4B, similar to activated carbon A, also shows the presence of the silicon element, probably because it is one of the mineral constituent of the coal used during the activation process [32]. In Figure 4C, with a zoom of 2000×, it is also possible to observe the porous structure of the solid indicated by the red arrows.

Adsorption Tests

Kinetic adsorption curves Adsorption kinetics tests were performed to

verify the time required for the adsorption system to reach equilibrium. The initial concentration of biuret in the test was approximately 0.65 wt.%.

In Figure 5, the adjusted adsorption kinetics curves (concentration versus time) are presented for

Figure 3. SEM images of coal A: a) 500× zoom; b) mapping of the Si element (500× zoom); c) 2000× zoom. The carbonaceous matter is

represented by the darker region.


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the three adsorbents. The resin reaches equilibrium between 60 and 120 min; while the carbons reached equilibrium between 120 min and 180 min, so their tests had a longer duration of 360 min.

Despite the lower kinetics, the two carbons had the lowest final concentrations of amide. Coal B reached a final concentration of 0.20 wt.% and coal A, 0.25 wt.%. The resin had a slightly higher final con-centration of biuret, 0.32 wt.%. Thus, the tested ads-orbents achieved the following biuret removal per-centages from the solution: 71 (carbon B), 68 (char-coal A) and 50% (resin).

These adsorption results are coherent with solid characterization, in which it is possible to observe that the two coals have higher specific areas, which indicates a higher number of available sites to per-form the adsorption of the amide. The resin has a specific area of 30 m2 g-1 (data sheet), a very low value compared to the other solids, indicating that its capacity is justified by other properties. As showed, the resin has sodium and aluminum in its compo-sition, and these ions may have contributed to the amide adsorption process.

Figure 4. SEM images of coal B: a) 500× zoom; b) mapping of the Si element (500× zoom); c) 2000× zoom. The carbonaceous matter is

represented by the darker region.

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0 50 100 150 200 250 300 350

Conc.(%m/m)

Time (min)Resin Carbon A Carbon B Figure 5. Adsorption kinetics curves of the three adsorbents with adjusted data.


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In Figure 6, the adsorption kinetics curves are presented as a function of the adsorption capacity of each adsorbent over time.

In Figure 6, the two coals exhibited the highest amide adsorption capacities, with the B-carbon hav-ing a maximum capacity of 49 g of amide/kg of ads-orbent and the A-charcoal 38 g of amide/kg of ads-orbent. The resin had a maximum capacity of 34 g of amide/kg of adsorbent. One hypothesis to justify the differences in the biuret adsorption capacity between the two coals is the higher alkaline earth metals con-tent in carbon A. The presence of these metals may contribute to turning carbon A’s surface more alkaline than the carbon B’s one. Considering the weak alka-line character of biuret in water, it would be expected that it had a slightly stronger interaction with the more acid surface of coal B.

Table 3 shows the adjusted parameters obtained from the mathematical treatment of the experimental data and the respective coefficients of variation of the mean square error.

Table 3. Parameters of adsorption kinetics and respective coefficients of variation of mean square error; CV (EMQ) (Conc.) - coefficient of variation of the mean square error for the concentration data; CV (EMQ) (Cap.) - coefficient of vari-ation of the mean square error for the capacity data

Solid k1

min-1 k-1

min-1 CV (EMQ) (Conc.)

% CV (EMQ) (Cap.)

%

Resin 0.531 0.544 5.22 7.19

Coal A 0.105 0.051 9.99 6.98

Coal B 0.157 0.067 7.11 4.96

Analyzing the values of the coefficients, accord-ing to the criteria mentioned before, we notice that both the adjustment of the concentration data and the capacity for the resin and the coals can be considered “good”, since they presented CV (EMQ) <10%. As a

suitable model for both concentration and capacity data is desired, the equations used can predict the kinetic behavior of all adsorbents under the tested conditions.

Adsorption isotherms In order to investigate the adsorption equilibrium

and to verify the adsorption capacity for different con-centrations of the studied amide, isothermal tests with the three adsorbents were performed. The average initial biuret concentration was 0.61 wt.%. The results of the isotherm test are shown in Figure 7, where the isotherm data adjustments for the three adsorbents were performed by the BET and Langmuir-Freundlich models.

In Figure 7, the decrease in the amide adsorp-tion capacity observed for the resin at higher equilib-rium concentrations may be due to solution/adsorbent ratios less suitable for the initial contents of the biuret, that is, the used mass compared to the used volume of solution is very low, which confers a very small variation between the final and initial amide concen-trations in the solution. The isotherm profiles of Figure 7 can be compared to isotherm models developed specifically for systems with solute in solution and solid adsorbent [34]. In this classification, both iso-therms of the coals belong to class L and subgroup 1. The class L refers to the isotherm in which the attractive forces between solute and solid molecules are relatively strong, but the attractive forces between solute molecules are very weak. Subgroup 1 indicates that the adsorbate monolayer was not fully formed.

Comparing the three isotherms presented in Fig-ure 7, the two carbons presented the highest biuret adsorption capacities within the studied equilibrium concentration range, with practically equivalent yields. However, the resin achieved much lower yields throughout the studied range.

0,0

10,0

20,0

30,0

40,0

50,0

0 50 100 150 200 250 300 350

Capacity(g/kg)

Time (min)Resin Carbon A Carbon B Figure 6. Adsorption kinetics curves of the adsorbents (capacity versus time) with adjusted data.


223

Table 4 shows the adjusted parameters of the tested models and the respective coefficients of variation of the mean square error. According to the criteria mentioned before, the resin presented values of coefficients of variation of the mean square error above 20% for both Langmuir-Freundlich and BET models, so, both adjustments can be considered bad and unable to predict the behavior of this solid under the tested equilibrium conditions. However, the two carbons presented values of CV(EMQ) of less than 10%, indicating that the adjustments can be classified as “good” and able to predict the behavior of these solids under the equilibrium conditions tested.

Adsorption rupture tests Fixed bed adsorption rupture tests were per-

formed with the three adsorbents, using a solution with approximately 0.65 wt.% of biuret. The results are shown in Figure 8.

From Figure 8, we notice that the resin started the saturation step before the time of 20 min and reached saturation in 90 min after the first drop of effluent was collected. On the other hand, the two coals started the saturation step at the time of 60 min, and visually, coal A reached saturation at 120 min and coal B close to 130 min, after the beginning of the collection of the effluent.

In this test, coal A had a retained mass of 120 g of amide/kg of adsorbent to produce a final effluent with a mean concentration of 0.3 wt.%. The carbon B had a retained mass of 127 g of amide/kg of ads-orbent at a cut of 0.3 wt.%. At a much lower yield, the resin had 53 g/kg for a cut of 0.3 wt.%.

These rupture curve results agree with the kin-etic curve and isotherm results in which the carbons presented the highest amide adsorption capacities, with practically equivalent results. It can be justified

Figure 7. Adsorption isotherms at room temperature for the three adsorbents, adjusted by the Langmuir, Freundlich and BET models:

a) resin; b) coal A; c) coal B.


224

by the fact that the coals have the highest specific areas and, in the regions observed by SEM, present a high silicon density that may be in silica form and may have contributed positively to the amide adsorption process, increasing the capacity compared to the resin.

The resin, despite having a lower adsorption capacity compared to the two coals, presented a significant result if its specific area was considered as reported by the manufacturer (30 m2 g-1), since the two coals have very high areas (> 900 m2 g-1) but did not present proportionally higher capacities. By nor-malizing the amount of amide adsorbed per area, instead of per mass, there would be a reversal of the results: the resin would have a retained mass of 1.77 g of amide/m2 of resin, coal A would have a mass retained equal to 0.11 g m-2 and coal B, a retained mass of 0.13 g m-2. This result indicates that the resin has a higher density of adsorption sites than the coals and that these, in principle, only presented better ads-orption performance because they have very high areas. However, as adsorbents are marketed per unit mass and not per unit area, the two coals remain the most attractive materials, industrially, compared to the resin, considering the results obtained in this study.

Table 5 shows the adjusted parameters of the tested model and the respective coefficients of vari-ation of the mean square error. We see that the data adjustments of the two coals can be classified as “excellent”, since both presented coefficients of vari-ation of the mean square error below 5%. Not too far, the adjustment of the resin data can be considered “good”, since it presented CV (EMQ) <10%, indicating that the employed equation can predict the behavior of adsorption disruption of the three solids under the conditions tested.

Table 5. Parameters adjusted from the adsorption rupture data and their respective quadratic mean error coefficients

Parameter Adsorbent

Resin Coal A Coal B

α 0.1 1.0 1.0

β 0.02 150.4 75.9

γ 460.0 0.07 0.05

CV (EMQ),% 7.0 1.2 1.8

CONCLUSION

The adsorbents’ characterization showed that the high specific area of the coals has contributed to

Table 4. Langmuir-Freundlich and BET adjusted parameters

Langmuir-Freundlich

Adsorbent qs / g kg-1 kLF / Pa-1 n1 CV (EMQ), %

Resin 52.0 0.00029 2.0 64.20

Coal A 170.0 0.00019 1.6 6.49

Coal B 3145.9 0.00935 1.3 7.82

BET

Km / L g-1 Ks / L g-1

Resin 90.00 0.00018 -0.107 60.66

Coal A 0.13 0.3030 0.015 5.59

Coal B 0.14 0.2500 0.031 8.31

0,00

0,20

0,40

0,60

0,80

1,00

0 30 60 90 120 150 180

C/C0

Time (min)

Resin Carbon A Carbon B Model Resin Model Carbon A Model Carbon B Figure 8. Biuret adsorption rupture curves of adsorbents with adjusted data.


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their higher adsorption capacities. From SEM ana-lysis, the presence of silicon may have contributed to coals’ adsorption capacities. In the kinetic tests, the coals presented slower adsorption kinetics but higher biuret adsorption capacity. In the isotherm tests, the coals had a higher biuret adsorption capacity than the resin, in accordance with adsorption rupture results (industrial conditions simulation), with higher biuret retention for the coals against the resin.

The adjustments of the kinetic data with the first-order differential equations were adequate to predict the adsorbents’ behavior. The adsorption isotherm data adjusted well to the Langmuir-Freundlich and BET models for the coals, but the models were not applicable to resin data. In the rupture data adjust-ments, the sigmoidal equation was able to predict the adsorbents’ rupture behavior.

The adsorption technique is suitable for rem-oving biuret from solution of Brazilian urea mainly by activated coal adsorbents. Therefore, the adsorption process appears as a promising and less expensive alternative to transform urea solution in ARLA 32. A study for reutilization of the exhausted adsorbent in order to turn the whole process feasible is strongly recommended.

Nomenclature

A – adsorbate S – solid adsorbent AS - concentration of adsorbate that has been adsorbed k1 - adsorption constant rate k-1 - desorption constant rate C - concentration of adsorbate in the fluid phase (g L-1) t - time (min) q - adsorption capacity (g kg-1) ma - adsorbent mass (kg) V - volume of the solution (L) C'calc - calculated concentration (g L-1) qcalc - calculated capacity (g kg-1) C'exp - experimental concentration (g L-1) qexp - experimental capacity (g kg-1) qs - capacity that corresponds to the formation of the monolayer (g kg-1) KLF - adsorption equilibrium constant of the Langmuir-Freundlich Km - interaction constant of the first adsorption layer (adsorbate-adsorbent) (L g-1) Ks - interaction constant of the following adsorption layers (adsorbate-adsorbate) (L g-1) qs - maximum adsorbed amount of adsorbate (g kg-1) CBET - concentration of adsorbate in the solution obtained from BET equation (g L-1)

α – parameter to be adjusted (dimensionless) β – parameter to be adjusted (dimensionless) γ - parameter to be adjusted (dimensionless) Cb - concentration of adsorbate in the bed effluent at time t (g L-1) Cbo - initial concentration of adsorbate in the solution (g L-1) Ymean - mean of the measured values SEY - standard error of the estimation calculated Yiest - value estimated by the model Yiobs - observed value nr - sample size p - number of variables in the regression equation (CV) (MSE) - coefficient of variation of the mean square error SBET – specific surface area calculated by BET method (m2 g-1) Vpore – pore volume (cm3 g-1) Dmicro – micropore diameter (nm) Dmeso – mesopore diameter (nm) Smicro – specific surface area of micropore (m2 g-1) CV (EMQ) (conc.) - coefficient of variation of the mean square error for the concentration data CV (EMQ) (cap.) - coefficient of variation of the mean square error for the capacity data

Aknowledgment

The authors thank all the crew of Laboratório de Engenharia e Tecnologia de Petróleo e Petroquímica (Laboratory of Petroleum and Petrochemical Engine-ering and Technology) and the Rio de Janeiro State University.

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LUANA VENTURA BAIA1

LUCIA R. RADDI DE ARAUJO1

CARLOS GUERRA PEREIRA2

WALLACE CARVALHO DE SOUZA2

MARCO ANTONIO GAYA DE FIGUEIREDO1

1Instituto de Química, Programa de Pós-graduação em Engenharia

Química, Universidade do Estado do Rio de Janeiro, Maracanã, Rio de

Janeiro, Brasil 2Instituto de Química, Laboratório de

Engenharia e Tecnologia de Petróleo e Petroquímica, Universidade do Estado

do Rio de Janeiro, Maracanã, Rio de Janeiro, Brasil

NAUČNI RAD

ADSORPCIJA KAO ALTERNATIVNI PROCES U PRETHODNOJ PROIZVODNJI DODATKA ZA MOTORE SA UNUTRAŠNJIM SAGOREVANJEM

Azotni kontaminanti u dizel frakciji konvertuju se u NOx jedinjenja u komori motora sa unutrašnjim sagorevanje, nakon čega se redukuju u azot katalitičkim reakcijama re-dukcije i oksidacije u prisustvu amonijaka dobijenog iz rastvora uree (32%). Ovaj postupak se naziva selektivna katalitička redukcija. Rastvor uree za selektivnu katali-tičku redukciju u Brazilu je proizvod ARLA 32, koji mora biti u skladu sa graničnim sadržajem biureta od 0,3%. Međutim, komercijalni rastvor brazilske uree ima prosečan sadržaj biureta od 0,5%. Stoga je potrebno prilagoditi sadržaj biureta u rastvoru uree koji se koristi kao ARLA 32, a adsorpcija je opcija sa malom potrošnjom energije. Cilj ovog istraživanja je bio da se proceni komercijalne adsorbense za uklanjanje biureta iz rastvora komercijalne uree kako bi se prilagodio specifikaciji ARLA 32. Dva aktivna uglja i jedna jonoizmenjivačka smola su testirana u adsorpcionim istraživanjima, pri čemu sa najbolje performance pokazala oba uglja.

Ključne reči: adsorpcija, biuret, karakterizacija, ugalj.

adsorption as alternative process lucia r. raddi de … no3_p215-226_jul … · reactions in the...

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