- Email: [email protected]
Experimental and CFD study of H2S oxidation by activated carbon prepared from cotton pulp black liquor
Journal Pre-proof Experimental and CFD study of H2 S oxidation by activated carbon prepared from cotton pulp black liquor Yong Sun, Jun He, Yunshan Wang, Gang Yang, Guangzhi Sun, ´ Valerie Sage
PII:
S0957-5820(19)31016-X
DOI:
https://doi.org/10.1016/j.psep.2019.11.035
Reference:
PSEP 2009
To appear in:
Process Safety and Environmental Protection
Received Date:
3 June 2019
Revised Date:
15 October 2019
Accepted Date:
26 November 2019
Please cite this article as: Sun Y, He J, Wang Y, Yang G, Sun G, Sage V, Experimental and CFD study of H2 S oxidation by activated carbon prepared from cotton pulp black liquor, Process Safety and Environmental Protection (2019), doi: https://doi.org/10.1016/j.psep.2019.11.035
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Experimental and CFD study of H2S oxidation by activated carbon prepared from cotton pulp black liquor Yong Sun1,2*, Jun He2, Yunshan Wang3, Gang Yang3*, Guangzhi Sun1, Valérie Sage4 1.
Corresponding author address: Dr Yong Sun; School of Engineering, 270 Joondalup Drive Joondalup
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*
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School of Engineering, Edith Cowan University, 270 Joondalup Drive Joondalup WA 6027 Australia. 2. Department of Chemical and Environmental Engineering, University of Nottingham Ningbo, 315100, China 3. State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China; 4. The Commonwealth Scientific and Industrial Research Organization (CSIRO), Energy Business Unit, WA, 6151, Australia.
WA 6027 Edith Cowan University. Australia. Department of Chemical and Environmental Engineering, of
Nottingham
Ningbo,
China
315100,
Email:
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University
[email protected];
[email protected]; Dr/A Professor Gang Yang, State Key Laboratory of Biochemical
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Email: [email protected]
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Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China,
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GAS IN
SEM
Mesh
QU ARTZ WOOL AC BED
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SCREEN
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Sulfur deposition
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GAS OUT
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A porous carbon was synthesized from cotton black liquor. The carbon produced at the optimal condition was used for H2S oxidation at 423K at different experimental conditions. The H2S oxidation was modeled by a novel CFD approach by coupling the stoichiometric reaction with variation of porosity as the userdefined scalars (UDS). The established model can be a useful tool for studying the fluid dynamics of H2S oxidation by AC catalyst at experimental conditions in this work.
Highlights
A porous carbon (AC) was synthesized from cotton black liquor The AC prepared under the optimal condition was applied to the direct H2S catalytic oxidation Complete parametrical computational fluid-dynamic (CFD) model was constructed
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Abstract A porous activated carbon (AC) was synthesized from cotton black liquor, and the synthesis process was optimized using response surface method (RSM). The AC prepared under the optimal condition was applied to the direct H2S catalytic oxidation. Complete parametrical computational fluid-dynamic (CFD) model, coupling stoichiometric reaction with variation of porosity as the user-defined scalars (UDS), was developed to simulate the removal process. Various experiments, including breakthrough, pressure drops,
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and H2S conversion at different experimental conditions, were carried out. The CFD model was validated by results from the experiments, as the experiment results were found to adequately match model
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predictions. Hence, the model developed in this study can be a useful tool for studying the fluid dynamics of H2S oxidation by AC catalyst at investigated experimental conditions.
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Keywords: Activated carbon; black liquor lignin, CFD; H2S; 1. Introduction
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Hydrogen sulfide (H2S) is a major odorous compound in the gas streams of many chemical industry processes [1-3]. It is hazardous to human health and environment [4], and detrimental to the activity of
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catalyst (poisoning of catalyst) in downstream catalytic gas processing such as Fischer-Tropsch (FT) synthesis [5, 6]. Numerous porous materials (e.g. bauxite, activated carbon (AC), molecular seize, zeolite)
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have been studied for the removal of sulfuric compounds [7-11]. AC-catalyzed hydrogen sulfide oxidation is among the most competitive methods of industrial sulfur removal. As the catalyst for H2S oxidation, ACs often have advantages over other materials (e.g. well-developed specific surface areas and porosities) [12, 13]; the oxidation efficiency being highly dependent on the characteristics of the AC employed [14]. The kinetics of H2S catalytic oxidation by commercial ACs have been studied and reported extensively
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[15-17].
The cascade and highly effective utilization of renewable resources is one of the important impetus for
socio-economic appropriation of offsetting net primary production, potentially leading to much diminished disturbances in the natural material and energy flow of ecosystems [18]. Researches are now exploring renewable precursors for the AC preparations [19-22]. Among different types of agricultural crops, cotton is a common crop that is planted worldwide [23]. Cotton pulp black liquor is a byproduct generated in
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large quantities in paper pulp processes [24, 25]. The main composition of black liquor include dissolved lignin, hemicellulose, celluloses, ash contents from biomass and cooking chemicals [26, 27]. Due to its complex composition, the utilization of black liquor has been technical challenge in the realm of integrated biomass utilization [28]. Recently, the Nordic countries such as Sweden, Finland have successfully demonstrated feasibility of integrating utilization of black liquor waste (wood based biomass) into biomass energy generation and utilization. The appealing advantages of black liquor over other type of biomass are
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due to its pumpable liquid form, relative high heat capacity, and rich in alkali [29]. This makes black liquor readily available to be utilized in boiler to generate steam and recover alkali [30, 31]. Apart from this
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mature technique, other approaches such as acid precipitation , gasification [32], supercritical gasification [33], bio-carbon production by hydrothermal technique [34] have also been explored extensively. Among
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them, spray drying technique was also found to be a feasible and alternative approach for converting black liquor [35]. In this study, we aims to: (a) investigate direct application of the ACs prepared from the black
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liquor via spray drying followed by activation, and (b) develop computational fluid-dynamic (CFD) model with particular focus on the kinetics of catalytic H2S oxidation by the prepared ACs. To the best knowledge
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of the authors, direct employment of AC prepared from cotton black liquid via spray drying followed by activation in H2S removal, combined with CFD modeling by finite element methods, has not been reported
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in the literature until now. 2. Experimental
The catalyst used in this system was made from cotton black liquid via spray drying process followed by subsequent alkaline activation. Detailed preparation procedures have been reported in previous publications [35, 36]. The produced carbon was pelletized and seize to have an average particle diameter
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about 1mm (0.84-1.81 mm). The specific surface area and porosity of the catalyst sample was determined by nitrogen gas adsorption-desorption at 77 K using an ASAP 2020 Automated Gas Sorption System. Nitrogen adsorption-desorption isotherms of the samples were obtained by calculating their BrunauerEmmett-Teller (BET) surface areas, assuming the area of N2 molecule being 0.162 nm2. The density functional theory (DFT) method was used for analyzing the pore size distribution of the result carbons in the microporic and mesoporic ranges. The response surface methodology (RSM) was employed to
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optimize the preparation process, in order to discover the maximum BET specific surface area of resultant carbon by adjusting one of the most critical operational parameters (activation temperature and activation duration) during AC preparation process. The chemical reaction taking place at the experimental condition is simplified as the following: 1
1
2
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H2 S + O2 = S8 + H2 O
(1)
The simulated H2S/O2 gas was passed through the reactor for H2S removal. The cylinder gas of H2S/O2
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(H2S-maximum 8000 ppm supplied by Dalian Special Gas Co., Ltd) were used for simulated gas feed. The H2S concentrations were varied from 300-3000 ppm in the reaction system. The H2S stream
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constitutes were analyzed by the gas chromatograph (GC-HP 6890) equipped with a pulsed flame photometric detector (PFPD) detector. The internal reference and quality controls (QC) were conducted to
𝑐𝑖𝐻2𝑆 −𝑐𝑜𝐻2 𝑆 𝑐𝑖𝐻2𝑆
× 100
(2)
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H2 S 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛, (%) =
-p
ensure experimental data performance. The H2S conversion is defined as the following:
where 𝑐𝑖𝐻2 𝑆 is the inlet concentration (mol/m3) and 𝑐𝑜𝐻2 𝑆 is the outlet concentration (mol/m3) of the reactor.
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3. Mathematical model:
To derive an appropriate approximation of the kinetics, some assumptions are further taken: i) the SO2 production is assumed to be negligible at the experimental condition, ii) the surface reactions of the
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adsorbed initiated O2 and H2S are the rate determine step (RDS), iii) the sorption of water vapor is negligible to oxidative rate, iv) both external and intra-particle resistances at experimental conditions are ignored, v) the produced sulfur will deposit resulting in reduction of the void fraction of the catalytic bed. To further validate the assumption, the Mears criteria was employed as the following: 𝑟𝑖 𝜌𝑠 𝑟𝑛
(3)
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𝑀=
𝑘𝑚 𝑐𝑖
where 𝑟𝑖 is the reaction rate (mol.kgcat-1s-1), 𝜌 𝑠 density of catalyst (kg.m-3), 𝑟 denotes the average catalyst radius (m), 𝑛 is reaction order (herein, for easiness of calculation, we assuming it is a first order reaction, -), 𝑘𝑚 represents overall mass transfer coefficient (m.s-1), and 𝑐𝑖 refers to the concentration of reagent species (mol.m-3). In order to correlate 𝑅𝑖 (mol.kgcat-1s-1) for estimation of Mears value, the generalized kinetic model for sulfur deposition without deactivation is employed as the following [37]:
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𝑅𝐻2 𝑆 =
0.5 𝐾𝐾𝐻2 𝑆 𝐾𝑂 𝑝 𝑝0.5 2 𝐻2 𝑆 𝑂2
(4)
0.5 𝑝0.5 )2 (1+𝐾𝐻2 𝑆 𝑝𝐻2 𝑆 +𝐾𝑂 2 𝑂2
where 𝑅𝐻2 𝑆 is the reaction rate (mol.kgcat-1s-1), 𝑝𝐻2𝑆 and 𝑝𝑂2 represent partial pressure of the reagents (Pa), and temperature dependence kinetic constants are correlated with the following: 23600
(5)
𝑅𝑇
ln 𝐾𝐻2 𝑆 = −3.71 +
16000
ln 𝐾𝑂2 = −18.02 +
73800
(6)
𝑅𝑇
(7)
𝑅𝑇
of
ln 𝐾 = 5.46 −
The overall mass transfer coefficient of 𝑘𝑚 correlation is given by Frössling as the following [38]: 𝐷𝑚
𝑑𝑃 𝒖
𝑅𝑒 =
-p
𝑣 𝜇 𝜌𝑠 𝐷𝑚
𝐷𝑚 =
1/2 1 1 + ] 𝑀𝐴 𝑀𝐵 1/3 1/3 2 𝑃[𝑣𝐴 +𝑣𝐵 ]
10−3 𝑇 1.75 [
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𝑆𝑐 =
𝑘𝑚 𝑑𝑃
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𝑆ℎ = 2 + 0.6𝑅𝑒1/2 𝑆𝑐1/3 =
(8)
(9) (10)
(11)
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where Sh denotes as Sherwood dimensionless number (-), Re is Reynolds dimensionless number (-), Sc represents Schmidt dimensionless number (-), 𝒖 is the superficial gas velocity (m.s-1), 𝐷𝑚 refers to binary
1.mol-1),
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gas diffusivity (m2.s-1), 𝐷𝑘 represents Knudsen diffusivity (m2.s-1), 𝑣𝐴 molecular volume of gas A (m3.g𝑀𝐴 represents the molecular weight (g.mol-1), and 𝑣 is kinematic viscosity (m2.s-1). The gas-solid
mass transfer coefficient is estimated to be 0.0373 (m.s-1) at baseline condition in reactor. The corresponding Mears criteria value is estimated to be around 0.115, which is smaller than 0.15, indicating
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the ignorable external diffusional limitation at experimental conditions in this reactor. To estimate the intra-particle diffusional limitation, the dimensionless Thiele modulus ( Φ ) was employed to as the following:
𝜌𝑠 (−𝑅𝐻2 𝑆 )
Φ = 𝐿𝑔 √ 𝐵=
(12)
𝐷𝑒 𝑐𝐻2 𝑆
0.357 𝐷𝑚 2𝜀𝑝0 𝐷𝑒
𝑅𝑒 0.641 𝑆𝑐1/3
(13)
where 𝐿𝑔 is geometry characteristic length (m), 𝐷𝑒 represents effective diffusivity (m2.s-1), 𝐵 is the Biot
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number (-), 𝜀𝑝0 porosity of catalyst particle prior to reaction (-). At the baseline condition, since Re is much less than 2000, the 𝐵 is estimated around 5 in this work [39]. The calculated Φ at base line condition is 0.93, which is less than 1, indicating the impact of intra-particle film diffusional limitation could be ignored at baseline condition in this work. The reactor model is proposed as an isothermal decomposition of H2S in a perfect mixed reactor with constant volume as the following: 𝜕𝑐𝑖
(14)
𝑅𝑖 = 𝑣𝑖 𝑟𝑖 α
(15)
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= 𝑅𝑖
𝜕𝑡
Where 𝑣𝑖 represents reaction stoichiometric matrix, α denotes the catalyst activities (-), and the catalyst
𝑑α
= −𝑘𝛼 r𝑖2 𝑐𝑆 α
(16)
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𝑑𝑡
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time dependent activity is expressed as the following:
where 𝑐𝑆 denotes produced sulfur concentration (mol.m-3), and 𝑘𝛼 represents temperature dependent
ln 𝑘𝛼 = 1.012 +
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kinetic constant ((mol.m-3)3.s) is correlated with the following: 9100 𝑅𝑇
(17)
reaction are given: 𝜕𝑡 𝑑𝑐𝑂2 𝜕𝑡
= −𝑣𝑖 𝑟𝑖 𝛼
𝑑𝑐𝐻2 𝑂 𝜕𝑡 𝑑𝑐𝑆
(18) (19)
= 𝑣𝑖 𝑟𝑖 𝛼
(20)
= 𝑣𝑖 𝑟𝑖 𝛼
(21)
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𝜕𝑡
= −𝑣𝑖 𝑟𝑖 𝛼
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𝑑𝑐𝐻2 𝑆
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The mass balances of different species stoichiometric (𝑆𝑖 stoichiometry of the reaction) involving with the
where 𝑐𝐻2 𝑆 , 𝑐𝑂2 , 𝑐𝐻2 𝑂 , 𝑐𝑆 are concentrations of each different species, the kinetic profile of each different species can be integrated over the course of time on stream (TOS) shown from Figure 2d on top of experimental setup and will be passed into convection and diffusion subdomain calculations. Then the total pressure is estimated by the following: 𝑝 = 𝑅𝑔 𝑇(𝑐𝐻2 𝑆 + 𝑐𝑂2 + 𝑐𝐻2 𝑂 )
(22)
The void fraction balance together with consequent changed permeability of reagents can be then coupled
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with the reaction rate of sulfur deposition as the following [40, 41]: 𝑑𝜀𝑝 𝜕𝑡
𝜀 𝑟
= 𝑀𝑝 𝜌𝑖
(23)
𝑆 𝑆
𝜀
3.55
𝜅 = 𝜅0 (𝜀 𝑝𝑡 )
(24)
𝑝0
𝐷𝑝2 𝜀3
𝜅0 = 150(1−𝜀𝑏
(25)
2 𝑏)
where is 𝜅 the permeability during the reaction (m2), 𝜅0 the initial permeability prior to the reaction (m2),
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𝜀𝑝 denotes the porosity (-), 𝑀𝑆 represents sulfur molecular weight (kg.mol-1), 𝜌𝑆 represents density of the produced sulfur (kg.m-3), 𝜀𝑝0 represents catalyst porosity prior to reaction, 𝜀𝑝𝑡 refers to catalyst porosity
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at time of t (-), 𝐷𝑝 (m) represents the average diameter of the particle, and 𝜀𝑏 is void of the bed (-).
The incompressible Navier-Stokes equations were employed to model the flow before and after porous
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adsorbents bed as the following: 𝜕𝒖
(26)
re
𝜌 𝜕𝑡 + ∇ ∙ [−𝜂(∇𝒖 + (∇𝒖)𝑇 ) + ∇𝑝] = −𝜌(𝒖 ∙ ∇)𝒖
where 𝜌 denotes the density of the fluid (kg.m-3), 𝒖 represents velocity (m.s-1), 𝜂 is the viscosity (Pa.s-1),
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and 𝑝 is the pressure (Pa), with ∇ ∙ 𝒖 = 0. The flow in porous material is simplified by using the Brinkman model equations as the following: + ∇ ∙ [−
𝜂 𝜀𝑝
𝜂
(∇𝒖 + (∇𝒖)𝑇 ) + ∇𝑝] = − 𝒖 𝜅
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𝜌 𝜕𝒖 𝜀𝑝 𝜕𝑡
(27)
where 𝜀𝑝 denotes the porosity (kg.m-3), 𝜅 is the permeability (m2), and characteristic dimension of the porous medium. The pressure lost in porous media is correlated by Ergun equation as the following [7]: ∇𝑝 𝐿
=
150𝜇(1−𝜀𝑏 )2 𝑢 𝐷𝑃2 𝜀𝑏3
+ 1.75
1−𝜀𝑏 𝜌𝑔 𝜇2 𝜀𝑏3
(28)
𝐷𝑝
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where 𝐿 is the length along the macroscopic pressure gradient in a porous medium with boundary conditions setting 𝒖 ∙ 𝐧 = 𝑢0 at inlet, 𝒖 = 0 on wall, and 𝑝 = 0 at the outlet. The mass transport is described by the following:
𝜕𝑐𝑖 𝜕𝑡
+ ∇ ∙ (−𝐷𝑖 ∇𝑐𝑖 + 𝑐𝑖 𝒖) + 𝑅𝑖 = 0
(29)
where 𝑐𝑖 is the species concentration (mol/m3), 𝐷𝑖 denotes the diffusion coefficient of i species (m2.s-1), 𝑅𝑖 represents the net reaction source term (mol.m-3.s-1), with 𝐧 ∙ (−𝐷∇𝑐) = 0 as the convection flux and
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𝐧 ∙ (−𝐷∇𝑐 + 𝑐𝒖) = 0. The proposed model treats isothermal decomposition of H2S in a perfect mixed plug flow reactor at experimental conditions. In general, two computational fluid-dynamic (CFD) approaches are widely employed to simulate the flow in fixed-bed reactors. The first one handles the catalyst in the fixed-bed reactor as a porous medium or applies a quasi-homogeneous reactor model [42]. The other directly solves the governing equations of fluid flow and transportation in complex pipelines, which does not simplify the flow patterns and the
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governing equations [43, 44]. As the second approach needs complex meshes and intensive computational resources. For simplicity of model and cost-effectiveness of computation perspective together with
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reasonable accuracy in model prediction, the relative single approach of modeling the flow through the porous media with selection of porous media models in CFD codes together with considerations of sulfur
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depositions effect upon the fluid flow in porous media were employed in this work. 3.1 Boundary conditions
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In this work, the generalized Neumann boundary conditions are employed for inlet as the following for
ℎ𝑢 = 𝑟, n ⃗ ∙ (𝑐∇𝒖) + 𝑞𝑢 = 𝑔
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the interior boundaries:
(30)
where n ⃗ is dependent variable of outward unit normal, 𝑞 (n ⃗ ×n ⃗ matrix) are 𝑔 (n ⃗ × 1 vector) functions
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defined in the domain, 𝑐 is defined as the coefficients from second order differential operators, ℎ is n ⃗ ×n ⃗ matrix, and 𝑟 is n ⃗ × 1 vector.
The Dirichlet boundary conditions are employed for walls as the following [45]: ℎ𝑢 = 𝑟, n ⃗ ∙ (𝑐∇𝒖) + 𝑞𝑢 = 𝑔 + ℎ𝑇 𝜇
(31)
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3.2 Simulation methods
The reactor is considered as a 2-D axisymmetric domain, the continuity, and mass balance equations
were solved by COMSOL Multiphysics 3.5a. Prior to solving the transport equation and reaction in fixed bed reactor using COMSOL, extra scalars were implemented in the subdomains. The user-defined scalars (UDS) were implemented into the mass transfer source term by considering reactions (convections and diffusion) and sulfur depositions caused by oxidation reactions. Its corresponding effects upon the porosity of the bed were implemented into calculating the absolute deposition amount. The unsymmetric sparse
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linear systems in stationary solver employed the Unsymmetrical MultiFrontal Pack (UMFPACK). The generalized alpha method with consistent initialization differential-algebraic systems (DAE) system using backward Euler approaches was employed for time stepping. All terms of the governing equations were discretized by the first order upwind scheme in the simulation. 4. Results and Discussion 4.1 Material characterization and geometry meshing together with reaction kinetics
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The results from Figure 1a indicate that operational parameters keeping at 850 °C for 45 min will produce AC with BET specific surface area reaching around 700 m2/g, which is also the validated by Figure 1b showing ±10% experimental uncertainties. The N2
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validation experiments in
adsorption/desorption isotherm from Figure 2a and 2b show that the prepared AC is mainly microporous
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material with existences of mesopores. The X-ray deflection (XRD) analysis in Figure 2c indicates i) the peaks are scattering with two broad humps centered at 22° and 44°, which correspond to crystallite phase
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of graphite at d(002) and d(10) planes [46], indicating the existence of the small graphite crystals in the matrix of prepared carbon, ii) the sharp peaks centered at 45° and 73° attribute to the existence of ash
ur na Color points by value of Conversion: 700
a
Predicted vs. Actual 700
700.00
b
450
600
550
650
650.00
a1
Predict
650
P re d ic te d
Actual Factors C: C = 8.00 D: D = 5.00
Design-Expert?Software Conversion
700
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X1 = A: A X2 = B: B
These results also agree well with results of ash content analysis.
BET specific surface area/m²/g
450
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contents (possibly the silica, MgO) when carbonaceous materials were activated at high temperature [47]. Design-Expert?Software Factor Coding: Actual Conversion Design points above predicted value Design points below predicted value 700
500
600
600.00
550
550.00
500
500.00
450
450.00
50
950
40
450.00 450
500
500.00
550.00 550
600.00 600
900 30
Actual
850 20
Actual
800 10 750
10
650
650.00
700.00 700
Figure 1. Preparation optimization and characterization of prepared pellet AC. a) response surface methodology optimization, b) model prediction vs actual experimental values.
160 140 120 100 80 60 40 20 0.0
0.2
0.4
0.6
0.8
c
b
1200 1000 800 600 400 200 0 0.1
1.0
1
0
10
20
30
e
d
40
50
60
70
80
90
2 theta degree/-
16 12
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Mass fraction/%
20
10
Pore diameter/nm
Relative pressure/(P/P )
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0
1400
8 4 0 0.8
1.0
1.2
1.4
1.6
1.8
10 um
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Particle Size/mm
-p
Pore volume/(mol/g)
180
Intensity/C.P.
a
Derivative cumulative pore volume/-
200
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Figure 2. Characterization of prepared AC. a) adsorption isotherm of AC, b) DFT (density functional theory) calculation of pore size distribution, c) XRD spectra, d) particle size of the AC e) SEM morphology of AC The particle size distribution of the pelleted AC is shown in Figure 2d. The average particle size of the
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pelleted catalyst is around 1 mm which will be used for kinetic model calculations. The scanning electron microscopy morphology (SEM) of the prepared AC prior to pelleting in Figure 2e shows the irregular shapes of the porous material. This is closely associated with the carbonization and activation step. Overall, the prepared carbonaceous catalyst presents good pore formation with carbon element reaching 55% of the entire material. The detailed porosity and elementary analysis of the catalyst prepared at the optimal
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condition are summarized in Table 1.The carbon and oxygen element predominate in the prepared activated carbon, with trace amount of sulfur content being found in the prepared carbon due to the features of biomass precursor. In this work, the AC prepared at the optimal condition (the detailed porosity parameters are listed in Table 1) will be used as the catalyst for subsequent H2S oxidation. Table 1. Porosity parameters of AC prepared from optimal condition
Parameters
Unit
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Values
BET specific surface area Pore volumes Micropore volume Average pore width Ash content Average pellet size N C H S O (by difference)
m2g-1 cm3g-1 cm3g-1 nm % mm % % % % %
695 0.4 0.3 2.4 5 1 0.6 55 4.5 0.6 39.5
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The experimental rig setup, the packing scheme, meshing of reaction zone, the kinetics of catalytic
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oxidation are shown in Figure 3. In this study, the reaction was carried out in a down flow fixed-bed stainless steel reactor, with 12.7 mm ID and 400 mm in length (Figure 3a and 3b). The simplified geometry
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of the system was constructed in Figure 3c, the final mesh, on the basis of an independent physical study, was constituted by 32150 free mesh elements and 3080 boundary elements. The baseline condition was
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set as the following: flow rate (1000 ml.min-1), H2S/O2 are fed in reactor at stoichiometric ratio at 423 K with the feeding H2S concentration (0.126 mol.m-3). The corresponding concentration variations of
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reagents and products at the baseline condition are summarized Figure 3d.
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Concentration/mol.m
-3
0.14 d)
c_H2S
GAS IN
c_O2
0.12
b)
c_H2O c_S8
0.10
Mesh
0.08
c)
0.06 0.04
QU ARTZ WOOL
0.02
AC BED
0.00
0
200 P
400 600 TOS/min
800
SCREEN
1000
H2S
TT1 AC TT2 BED TT3 P
Thermal static
GC
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BPC
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O2
GAS OUT
-p
MFC
TT0
of
a)
4.2 Model validation
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Figure 3. a) Diagram of the experimental setup used for sulfur removal, b) reactor packing scheme. c) meshing of the geometry of reactor bed, d) reaction kinetics, where MFC is mass flow controller, BPC is back pressure controller, GC is gas chromatography, P is pressure transducer, TT is thermocouples.
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The constructed model was further validated by the experimental results. At first, the setup model was used to predict breakthrough curve. The experiments were conducted at the baseline condition, the exit H2S concentration of the bed over the TOS is shown in Figure 4a. Except for the relative exceptional high errors at around (45% uncertainties) 5 minute, the overall error of the entire model prediction falls within
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the range of ±5.5% suggesting a good agreement with the experimental results. At 5 minutes from Figure 4a, it indicates the model overshooting the amount of sulfur deposition in the porous catalyst, which makes outlet concentration of H2S being less than experimental data. At the initial stage of deposition, the evolutions of porous medium caused by the formation of deposition is not stable. This also suggests that quasi-homogeneous porous media hypothesis might face the challenges when morphological development is not steady. The non-uniform local depositions along axial of the catalytic bed will be likely to contribute
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to the discrepancy of the model.
a
Model Fitting Experimental Data
2500 2000 1500 1000
2
4 6 Time/min
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b
1000 ml/min 2000 ml/min 3000 ml/min
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100 75 50 25 0
0
250
500 TOS/min
Model Fitting
750
1000
c
Pressure drop/Pa
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3000
10
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0
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H2S conversion percentage/%
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500
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Concentration outlet/ppmv
3000
2500 2000 1500 1000
500 0
Experimental data Simulation
0
200
400
600
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TOS/min Figure 4. Validation of the model, a) breakthrough modeling at the baseline conditions, b) H2S conversion modeling, c) pressure drop vs TOS on baseline condition. As the corresponding initial morphological changes is reflected from the calculation of porosity and
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permeability of the bed (Eq.23, Eq.24 and Eq.25), it will affect the sulfur balances accordingly. This could possibly explain the exceptional higher errors occurred at beginning of the deposition reaction (around 5 min). As reaction continues, the error dropped appreciably when the deposition continued (over 10 minutes) suggesting that the morphological change becomes more stable. This is a good indication of the fact that the spatial organization of the porous material predominantly determines the function of permeability, porosity and breakthrough curve predictions [48]. Apart from baseline flowrate, different flowrates (from
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1000 to 3000 l.min-1) while the rest of conditions being kept at the baseline condition were conducted. The H2S removal profile at different flowrate together with model prediction was shown in Figure 4b. With an
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increase of flowrate, the corresponding increased H2S removal percentage was observed, and the model also responded well to the increase of inlet flowrate of H2S gas with less than ±10% percentage of
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uncertainties. In addition to the mass balances, the pressure drop over the course of reaction were continuously monitored, the pressure drops together with the model prediction were shown in Figure 4c.
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All of the sulfur produced during the reaction at the unsteady state deposits in the AC pores leading to the deactivation and variation of pressure drop. It has been found that the sulfur deposition is
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thermodynamically facilitated by the significant decreased sulfur vapor pressure (a decrease of a factor of one thousand inside the pores) in the pores [49]. The simulation results agree well with the experimental
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results indicates that the simplified assumption of pore filling by sulfur depositions, which consequently affect the porosity of the fixed bed reactor and ultimately changed the fluid dynamic features in the porous media is reasonably appropriate over the course of TOS at experimental condition. In addition, during model derivation, Eq. (1) was employed to describe reaction at experimental conditions. The reaction mechanism of H2S oxidization catalyzed by AC is still controversial. Different mechanisms such as
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chemisorption of H2S and O2 followed by reaction between the formed intermediate [50], chemisorption followed by oxidation reaction being only dependent on the unity of H2S not O2 [51], oxidation-reduction [52], oxidation facilitated by Quinone and carbonyl functional groups on surface of catalyst [53] etc have been proposed by different scholars. In this work, to simplify the reaction process, we assume that the RDS is only governed by the surface reactions between the adsorbed intermediates and the partial pressure of the produced water vapor is negligible to the oxidation rate of H2S. However, in order to calculate the
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overall pressure, the partial pressure of produced water vapor must be considered for total pressure estimation. 4.3 Model simulation The simulation result of sulfur distributions at different TOS and across reactor is shown in Figure 5. As reaction proceeds over the course of TOS, the sulfur was captured and deposited in the porous catalytic bed continuously. Over the entire length of the bed, sulfur tends to deposit more at the inlet due to the
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transport governed by convection, diffusion and reaction. Ultimately, the entire bed will be saturated with
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the sulfur leading to the breakthrough of the bed, which agrees well with experimental observations.
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Figure 5. Simulation results of sulfur deposition in the bed at the baseline condition on different TOS. Because of the high practical and fundamental interests that widely existed in the chemical, civil, oil
& gas processing, the deposition in the porous media is one of the most pivotal phenomena that needed to be gauged during the operations of the process. When fluids are introduced into porous media, the deposition reactions, which can be referring to either the precipitation on the gas-solid interface, or the trapping of fine particles at pore throats etc., cause the morphological changes of the pore space and lead
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to the decreased porosity and permeability [54]. The simulated variations of porosity, pressure drop together with permeability of reagents at experimental condition (baseline) over the course of TOS are shown in Figure 6. As reaction proceeds, the deposition of sulfur lead to the variation of porosity of the porous catalytic bed. Using the constructed CFD models, the detailed variations of important physical features could be well monitored. Prior to the reaction, the porosity of the clean bed starts with a constant value of porosity across the bed. As the reaction proceeds, the porosity of porous catalytic bed is altered,
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the detailed variations are shown in Figure 6a. As depicted from Eq. (23) to (25), the permeability of the bed correlates with porosities of the bed with a power that ranges from 3.5 to 4 [55]. As the reaction
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proceeds from beginning to 1000 min, the variations of permeability and porosity of the catalytic bed (Figure 6a and 6c) become more appreciable across the entire catalytic bed, resulting porosity difference
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(0.2) and pressure drop (>3000 Pa) between the entrance and exit of the catalytic bed when reaction continues for 1000 min. The initial permeability of reagent gas is a constant value in the clean bed (1.33E-
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9 m2). As the reaction continues, the permeability of reagent gas at the front end of the porous catalytic bed changes over three order of magnitude during the course of the reaction (Figure 6b). From Figure 6,
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it indicates i) the setup system is effective in sulfur depositions, ii) the depositions continuously change the morphological shape of the porous structure of the pores in the matrix of prepared pellet catalyst.
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According to the fundamental studies [56], the spatial organization of the porous material predominantly determines the function of permeability and porosity. As deposition reaction continues, the homogeneous assumptions of porosity along the axial of the catalytic bed (quasi-homogeneous medium) on the macroscopic scale might experience the theoretical challenge [57], which will lead to the significant prediction errors especially at the beginning of the reaction. The non-uniform local depositions along axial
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of the catalytic bed will be likely to contribute to the discrepancy of the model (quasi-homogeneous porous medium model) that is used to estimate the permeability, porosity and corresponding pressure lost in the CFD model. As reflected from Eq. (28), the Ergun equation used in the CFD model is dynamically correlated with morphological change of the porous media. Because of the different expressions of Eq. (23) to (25) and Eq. (28), the profiles of pressure lost (Figure 6b) are different to that of permeability and porosity of the catalytic bed at experimental conditions.
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a Clean bed
100 200 400 600 800 1000
0.005
0.020
0.015 0.010 Length of bed/m
b
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10 0.000
0.005
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100
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Pressure drop/Pa
1000
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Porosity of bed/(-)
0.40 0.36 0.32 0.28 0.24 0.20 0.16 0.12 0.08 0.04 0.00 0.000
0.010
0.015
0.020
1E-10
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Permeability/m
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1E-9
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Length of bed/m
Clean bed 100 200 400 600 800 1000
1E-11
1E-12 0.000
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c
0.005
0.010
0.015
0.020
Length of bed/m
Figure 6. Simulations, a) porosity distribution of the bed at the baseline conditions, b) pressure drop across the bed at the baseline conditions, c) permeability (H2S) across the bed over different TOS at the baseline conditions. 5. Conclusion The preparation conditions for porous AC were optimized by using the well-established RSM considering the activation temperature and duration as the independent variables. The application of H2S
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oxidation using the obtained AC prepared from cotton black liquor under the optimal preparation condition was tested. A CFD model was successfully developed to model the H2S oxidation using AC as catalyst for sulfur removal. The morphological changes of pore structure in the porous medium as deposition reaction proceeds affect appreciably on the hydrodynamic parameters such as permeability, pressure drop ect. Overall, the constructed quasi-homogeneous porous medium model presents good predictions in kinetic experiments (breakthrough, H2S conversion and pressure drops) during H2S oxidation at the investigated
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experimental conditions. The established model is a very useful and complementary tool for monitoring
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catalytic removal of H2S over the porous AC catalyst.
conflicts of Interest
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We herein declare:
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Authors: Yong Sun, Jun He, Yunshan Wang, Gang Yang, Guangzhi Sun, Valérie Sage.
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Title: Experimental and CFD study of H2S oxidation by activated carbon prepared from cotton pulp black liquor
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Have not been submitted elsewhere except Process Safety and Environmental Protection for consideration of publication. And we have no conflicts of interests in this publication. Acknowledgements The authors would like to appreciate the critical and insightful comments that raised from anonymous
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reviewers in significantly improving the quality of this work. The following funding support are appreciated: National Key R&D Program of China (2018YFC1903500), Faculty Inspiration Grant of University of Nottingham (FIG2019) and Qianjiang Talent Scheme (QJD1803014). The Acid-Based coupled production group at Institute of Process Engineering, Chinese Academy of Science is also highly appreciated. References
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PII:
S0957-5820(19)31016-X
DOI:
https://doi.org/10.1016/j.psep.2019.11.035
Reference:
PSEP 2009
To appear in:
Process Safety and Environmental Protection
Received Date:
3 June 2019
Revised Date:
15 October 2019
Accepted Date:
26 November 2019
Please cite this article as: Sun Y, He J, Wang Y, Yang G, Sun G, Sage V, Experimental and CFD study of H2 S oxidation by activated carbon prepared from cotton pulp black liquor, Process Safety and Environmental Protection (2019), doi: https://doi.org/10.1016/j.psep.2019.11.035
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Experimental and CFD study of H2S oxidation by activated carbon prepared from cotton pulp black liquor Yong Sun1,2*, Jun He2, Yunshan Wang3, Gang Yang3*, Guangzhi Sun1, Valérie Sage4 1.
Corresponding author address: Dr Yong Sun; School of Engineering, 270 Joondalup Drive Joondalup
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*
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School of Engineering, Edith Cowan University, 270 Joondalup Drive Joondalup WA 6027 Australia. 2. Department of Chemical and Environmental Engineering, University of Nottingham Ningbo, 315100, China 3. State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China; 4. The Commonwealth Scientific and Industrial Research Organization (CSIRO), Energy Business Unit, WA, 6151, Australia.
WA 6027 Edith Cowan University. Australia. Department of Chemical and Environmental Engineering, of
Nottingham
Ningbo,
China
315100,
Email:
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University
[email protected];
[email protected]; Dr/A Professor Gang Yang, State Key Laboratory of Biochemical
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Email: [email protected]
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Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China,
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GAS IN
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Mesh
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SCREEN
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Sulfur deposition
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GAS OUT
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A porous carbon was synthesized from cotton black liquor. The carbon produced at the optimal condition was used for H2S oxidation at 423K at different experimental conditions. The H2S oxidation was modeled by a novel CFD approach by coupling the stoichiometric reaction with variation of porosity as the userdefined scalars (UDS). The established model can be a useful tool for studying the fluid dynamics of H2S oxidation by AC catalyst at experimental conditions in this work.
Highlights
A porous carbon (AC) was synthesized from cotton black liquor The AC prepared under the optimal condition was applied to the direct H2S catalytic oxidation Complete parametrical computational fluid-dynamic (CFD) model was constructed
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Abstract A porous activated carbon (AC) was synthesized from cotton black liquor, and the synthesis process was optimized using response surface method (RSM). The AC prepared under the optimal condition was applied to the direct H2S catalytic oxidation. Complete parametrical computational fluid-dynamic (CFD) model, coupling stoichiometric reaction with variation of porosity as the user-defined scalars (UDS), was developed to simulate the removal process. Various experiments, including breakthrough, pressure drops,
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and H2S conversion at different experimental conditions, were carried out. The CFD model was validated by results from the experiments, as the experiment results were found to adequately match model
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predictions. Hence, the model developed in this study can be a useful tool for studying the fluid dynamics of H2S oxidation by AC catalyst at investigated experimental conditions.
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Keywords: Activated carbon; black liquor lignin, CFD; H2S; 1. Introduction
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Hydrogen sulfide (H2S) is a major odorous compound in the gas streams of many chemical industry processes [1-3]. It is hazardous to human health and environment [4], and detrimental to the activity of
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catalyst (poisoning of catalyst) in downstream catalytic gas processing such as Fischer-Tropsch (FT) synthesis [5, 6]. Numerous porous materials (e.g. bauxite, activated carbon (AC), molecular seize, zeolite)
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have been studied for the removal of sulfuric compounds [7-11]. AC-catalyzed hydrogen sulfide oxidation is among the most competitive methods of industrial sulfur removal. As the catalyst for H2S oxidation, ACs often have advantages over other materials (e.g. well-developed specific surface areas and porosities) [12, 13]; the oxidation efficiency being highly dependent on the characteristics of the AC employed [14]. The kinetics of H2S catalytic oxidation by commercial ACs have been studied and reported extensively
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[15-17].
The cascade and highly effective utilization of renewable resources is one of the important impetus for
socio-economic appropriation of offsetting net primary production, potentially leading to much diminished disturbances in the natural material and energy flow of ecosystems [18]. Researches are now exploring renewable precursors for the AC preparations [19-22]. Among different types of agricultural crops, cotton is a common crop that is planted worldwide [23]. Cotton pulp black liquor is a byproduct generated in
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large quantities in paper pulp processes [24, 25]. The main composition of black liquor include dissolved lignin, hemicellulose, celluloses, ash contents from biomass and cooking chemicals [26, 27]. Due to its complex composition, the utilization of black liquor has been technical challenge in the realm of integrated biomass utilization [28]. Recently, the Nordic countries such as Sweden, Finland have successfully demonstrated feasibility of integrating utilization of black liquor waste (wood based biomass) into biomass energy generation and utilization. The appealing advantages of black liquor over other type of biomass are
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due to its pumpable liquid form, relative high heat capacity, and rich in alkali [29]. This makes black liquor readily available to be utilized in boiler to generate steam and recover alkali [30, 31]. Apart from this
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mature technique, other approaches such as acid precipitation , gasification [32], supercritical gasification [33], bio-carbon production by hydrothermal technique [34] have also been explored extensively. Among
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them, spray drying technique was also found to be a feasible and alternative approach for converting black liquor [35]. In this study, we aims to: (a) investigate direct application of the ACs prepared from the black
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liquor via spray drying followed by activation, and (b) develop computational fluid-dynamic (CFD) model with particular focus on the kinetics of catalytic H2S oxidation by the prepared ACs. To the best knowledge
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of the authors, direct employment of AC prepared from cotton black liquid via spray drying followed by activation in H2S removal, combined with CFD modeling by finite element methods, has not been reported
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in the literature until now. 2. Experimental
The catalyst used in this system was made from cotton black liquid via spray drying process followed by subsequent alkaline activation. Detailed preparation procedures have been reported in previous publications [35, 36]. The produced carbon was pelletized and seize to have an average particle diameter
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about 1mm (0.84-1.81 mm). The specific surface area and porosity of the catalyst sample was determined by nitrogen gas adsorption-desorption at 77 K using an ASAP 2020 Automated Gas Sorption System. Nitrogen adsorption-desorption isotherms of the samples were obtained by calculating their BrunauerEmmett-Teller (BET) surface areas, assuming the area of N2 molecule being 0.162 nm2. The density functional theory (DFT) method was used for analyzing the pore size distribution of the result carbons in the microporic and mesoporic ranges. The response surface methodology (RSM) was employed to
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optimize the preparation process, in order to discover the maximum BET specific surface area of resultant carbon by adjusting one of the most critical operational parameters (activation temperature and activation duration) during AC preparation process. The chemical reaction taking place at the experimental condition is simplified as the following: 1
1
2
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H2 S + O2 = S8 + H2 O
(1)
The simulated H2S/O2 gas was passed through the reactor for H2S removal. The cylinder gas of H2S/O2
of
(H2S-maximum 8000 ppm supplied by Dalian Special Gas Co., Ltd) were used for simulated gas feed. The H2S concentrations were varied from 300-3000 ppm in the reaction system. The H2S stream
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constitutes were analyzed by the gas chromatograph (GC-HP 6890) equipped with a pulsed flame photometric detector (PFPD) detector. The internal reference and quality controls (QC) were conducted to
𝑐𝑖𝐻2𝑆 −𝑐𝑜𝐻2 𝑆 𝑐𝑖𝐻2𝑆
× 100
(2)
re
H2 S 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛, (%) =
-p
ensure experimental data performance. The H2S conversion is defined as the following:
where 𝑐𝑖𝐻2 𝑆 is the inlet concentration (mol/m3) and 𝑐𝑜𝐻2 𝑆 is the outlet concentration (mol/m3) of the reactor.
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3. Mathematical model:
To derive an appropriate approximation of the kinetics, some assumptions are further taken: i) the SO2 production is assumed to be negligible at the experimental condition, ii) the surface reactions of the
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adsorbed initiated O2 and H2S are the rate determine step (RDS), iii) the sorption of water vapor is negligible to oxidative rate, iv) both external and intra-particle resistances at experimental conditions are ignored, v) the produced sulfur will deposit resulting in reduction of the void fraction of the catalytic bed. To further validate the assumption, the Mears criteria was employed as the following: 𝑟𝑖 𝜌𝑠 𝑟𝑛
(3)
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𝑀=
𝑘𝑚 𝑐𝑖
where 𝑟𝑖 is the reaction rate (mol.kgcat-1s-1), 𝜌 𝑠 density of catalyst (kg.m-3), 𝑟 denotes the average catalyst radius (m), 𝑛 is reaction order (herein, for easiness of calculation, we assuming it is a first order reaction, -), 𝑘𝑚 represents overall mass transfer coefficient (m.s-1), and 𝑐𝑖 refers to the concentration of reagent species (mol.m-3). In order to correlate 𝑅𝑖 (mol.kgcat-1s-1) for estimation of Mears value, the generalized kinetic model for sulfur deposition without deactivation is employed as the following [37]:
5
𝑅𝐻2 𝑆 =
0.5 𝐾𝐾𝐻2 𝑆 𝐾𝑂 𝑝 𝑝0.5 2 𝐻2 𝑆 𝑂2
(4)
0.5 𝑝0.5 )2 (1+𝐾𝐻2 𝑆 𝑝𝐻2 𝑆 +𝐾𝑂 2 𝑂2
where 𝑅𝐻2 𝑆 is the reaction rate (mol.kgcat-1s-1), 𝑝𝐻2𝑆 and 𝑝𝑂2 represent partial pressure of the reagents (Pa), and temperature dependence kinetic constants are correlated with the following: 23600
(5)
𝑅𝑇
ln 𝐾𝐻2 𝑆 = −3.71 +
16000
ln 𝐾𝑂2 = −18.02 +
73800
(6)
𝑅𝑇
(7)
𝑅𝑇
of
ln 𝐾 = 5.46 −
The overall mass transfer coefficient of 𝑘𝑚 correlation is given by Frössling as the following [38]: 𝐷𝑚
𝑑𝑃 𝒖
𝑅𝑒 =
-p
𝑣 𝜇 𝜌𝑠 𝐷𝑚
𝐷𝑚 =
1/2 1 1 + ] 𝑀𝐴 𝑀𝐵 1/3 1/3 2 𝑃[𝑣𝐴 +𝑣𝐵 ]
10−3 𝑇 1.75 [
re
𝑆𝑐 =
𝑘𝑚 𝑑𝑃
ro
𝑆ℎ = 2 + 0.6𝑅𝑒1/2 𝑆𝑐1/3 =
(8)
(9) (10)
(11)
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where Sh denotes as Sherwood dimensionless number (-), Re is Reynolds dimensionless number (-), Sc represents Schmidt dimensionless number (-), 𝒖 is the superficial gas velocity (m.s-1), 𝐷𝑚 refers to binary
1.mol-1),
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gas diffusivity (m2.s-1), 𝐷𝑘 represents Knudsen diffusivity (m2.s-1), 𝑣𝐴 molecular volume of gas A (m3.g𝑀𝐴 represents the molecular weight (g.mol-1), and 𝑣 is kinematic viscosity (m2.s-1). The gas-solid
mass transfer coefficient is estimated to be 0.0373 (m.s-1) at baseline condition in reactor. The corresponding Mears criteria value is estimated to be around 0.115, which is smaller than 0.15, indicating
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the ignorable external diffusional limitation at experimental conditions in this reactor. To estimate the intra-particle diffusional limitation, the dimensionless Thiele modulus ( Φ ) was employed to as the following:
𝜌𝑠 (−𝑅𝐻2 𝑆 )
Φ = 𝐿𝑔 √ 𝐵=
(12)
𝐷𝑒 𝑐𝐻2 𝑆
0.357 𝐷𝑚 2𝜀𝑝0 𝐷𝑒
𝑅𝑒 0.641 𝑆𝑐1/3
(13)
where 𝐿𝑔 is geometry characteristic length (m), 𝐷𝑒 represents effective diffusivity (m2.s-1), 𝐵 is the Biot
6
number (-), 𝜀𝑝0 porosity of catalyst particle prior to reaction (-). At the baseline condition, since Re is much less than 2000, the 𝐵 is estimated around 5 in this work [39]. The calculated Φ at base line condition is 0.93, which is less than 1, indicating the impact of intra-particle film diffusional limitation could be ignored at baseline condition in this work. The reactor model is proposed as an isothermal decomposition of H2S in a perfect mixed reactor with constant volume as the following: 𝜕𝑐𝑖
(14)
𝑅𝑖 = 𝑣𝑖 𝑟𝑖 α
(15)
of
= 𝑅𝑖
𝜕𝑡
Where 𝑣𝑖 represents reaction stoichiometric matrix, α denotes the catalyst activities (-), and the catalyst
𝑑α
= −𝑘𝛼 r𝑖2 𝑐𝑆 α
(16)
-p
𝑑𝑡
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time dependent activity is expressed as the following:
where 𝑐𝑆 denotes produced sulfur concentration (mol.m-3), and 𝑘𝛼 represents temperature dependent
ln 𝑘𝛼 = 1.012 +
re
kinetic constant ((mol.m-3)3.s) is correlated with the following: 9100 𝑅𝑇
(17)
reaction are given: 𝜕𝑡 𝑑𝑐𝑂2 𝜕𝑡
= −𝑣𝑖 𝑟𝑖 𝛼
𝑑𝑐𝐻2 𝑂 𝜕𝑡 𝑑𝑐𝑆
(18) (19)
= 𝑣𝑖 𝑟𝑖 𝛼
(20)
= 𝑣𝑖 𝑟𝑖 𝛼
(21)
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𝜕𝑡
= −𝑣𝑖 𝑟𝑖 𝛼
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𝑑𝑐𝐻2 𝑆
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The mass balances of different species stoichiometric (𝑆𝑖 stoichiometry of the reaction) involving with the
where 𝑐𝐻2 𝑆 , 𝑐𝑂2 , 𝑐𝐻2 𝑂 , 𝑐𝑆 are concentrations of each different species, the kinetic profile of each different species can be integrated over the course of time on stream (TOS) shown from Figure 2d on top of experimental setup and will be passed into convection and diffusion subdomain calculations. Then the total pressure is estimated by the following: 𝑝 = 𝑅𝑔 𝑇(𝑐𝐻2 𝑆 + 𝑐𝑂2 + 𝑐𝐻2 𝑂 )
(22)
The void fraction balance together with consequent changed permeability of reagents can be then coupled
7
with the reaction rate of sulfur deposition as the following [40, 41]: 𝑑𝜀𝑝 𝜕𝑡
𝜀 𝑟
= 𝑀𝑝 𝜌𝑖
(23)
𝑆 𝑆
𝜀
3.55
𝜅 = 𝜅0 (𝜀 𝑝𝑡 )
(24)
𝑝0
𝐷𝑝2 𝜀3
𝜅0 = 150(1−𝜀𝑏
(25)
2 𝑏)
where is 𝜅 the permeability during the reaction (m2), 𝜅0 the initial permeability prior to the reaction (m2),
of
𝜀𝑝 denotes the porosity (-), 𝑀𝑆 represents sulfur molecular weight (kg.mol-1), 𝜌𝑆 represents density of the produced sulfur (kg.m-3), 𝜀𝑝0 represents catalyst porosity prior to reaction, 𝜀𝑝𝑡 refers to catalyst porosity
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at time of t (-), 𝐷𝑝 (m) represents the average diameter of the particle, and 𝜀𝑏 is void of the bed (-).
The incompressible Navier-Stokes equations were employed to model the flow before and after porous
-p
adsorbents bed as the following: 𝜕𝒖
(26)
re
𝜌 𝜕𝑡 + ∇ ∙ [−𝜂(∇𝒖 + (∇𝒖)𝑇 ) + ∇𝑝] = −𝜌(𝒖 ∙ ∇)𝒖
where 𝜌 denotes the density of the fluid (kg.m-3), 𝒖 represents velocity (m.s-1), 𝜂 is the viscosity (Pa.s-1),
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and 𝑝 is the pressure (Pa), with ∇ ∙ 𝒖 = 0. The flow in porous material is simplified by using the Brinkman model equations as the following: + ∇ ∙ [−
𝜂 𝜀𝑝
𝜂
(∇𝒖 + (∇𝒖)𝑇 ) + ∇𝑝] = − 𝒖 𝜅
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𝜌 𝜕𝒖 𝜀𝑝 𝜕𝑡
(27)
where 𝜀𝑝 denotes the porosity (kg.m-3), 𝜅 is the permeability (m2), and characteristic dimension of the porous medium. The pressure lost in porous media is correlated by Ergun equation as the following [7]: ∇𝑝 𝐿
=
150𝜇(1−𝜀𝑏 )2 𝑢 𝐷𝑃2 𝜀𝑏3
+ 1.75
1−𝜀𝑏 𝜌𝑔 𝜇2 𝜀𝑏3
(28)
𝐷𝑝
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where 𝐿 is the length along the macroscopic pressure gradient in a porous medium with boundary conditions setting 𝒖 ∙ 𝐧 = 𝑢0 at inlet, 𝒖 = 0 on wall, and 𝑝 = 0 at the outlet. The mass transport is described by the following:
𝜕𝑐𝑖 𝜕𝑡
+ ∇ ∙ (−𝐷𝑖 ∇𝑐𝑖 + 𝑐𝑖 𝒖) + 𝑅𝑖 = 0
(29)
where 𝑐𝑖 is the species concentration (mol/m3), 𝐷𝑖 denotes the diffusion coefficient of i species (m2.s-1), 𝑅𝑖 represents the net reaction source term (mol.m-3.s-1), with 𝐧 ∙ (−𝐷∇𝑐) = 0 as the convection flux and
8
𝐧 ∙ (−𝐷∇𝑐 + 𝑐𝒖) = 0. The proposed model treats isothermal decomposition of H2S in a perfect mixed plug flow reactor at experimental conditions. In general, two computational fluid-dynamic (CFD) approaches are widely employed to simulate the flow in fixed-bed reactors. The first one handles the catalyst in the fixed-bed reactor as a porous medium or applies a quasi-homogeneous reactor model [42]. The other directly solves the governing equations of fluid flow and transportation in complex pipelines, which does not simplify the flow patterns and the
of
governing equations [43, 44]. As the second approach needs complex meshes and intensive computational resources. For simplicity of model and cost-effectiveness of computation perspective together with
ro
reasonable accuracy in model prediction, the relative single approach of modeling the flow through the porous media with selection of porous media models in CFD codes together with considerations of sulfur
-p
depositions effect upon the fluid flow in porous media were employed in this work. 3.1 Boundary conditions
re
In this work, the generalized Neumann boundary conditions are employed for inlet as the following for
ℎ𝑢 = 𝑟, n ⃗ ∙ (𝑐∇𝒖) + 𝑞𝑢 = 𝑔
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the interior boundaries:
(30)
where n ⃗ is dependent variable of outward unit normal, 𝑞 (n ⃗ ×n ⃗ matrix) are 𝑔 (n ⃗ × 1 vector) functions
ur na
defined in the domain, 𝑐 is defined as the coefficients from second order differential operators, ℎ is n ⃗ ×n ⃗ matrix, and 𝑟 is n ⃗ × 1 vector.
The Dirichlet boundary conditions are employed for walls as the following [45]: ℎ𝑢 = 𝑟, n ⃗ ∙ (𝑐∇𝒖) + 𝑞𝑢 = 𝑔 + ℎ𝑇 𝜇
(31)
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3.2 Simulation methods
The reactor is considered as a 2-D axisymmetric domain, the continuity, and mass balance equations
were solved by COMSOL Multiphysics 3.5a. Prior to solving the transport equation and reaction in fixed bed reactor using COMSOL, extra scalars were implemented in the subdomains. The user-defined scalars (UDS) were implemented into the mass transfer source term by considering reactions (convections and diffusion) and sulfur depositions caused by oxidation reactions. Its corresponding effects upon the porosity of the bed were implemented into calculating the absolute deposition amount. The unsymmetric sparse
9
linear systems in stationary solver employed the Unsymmetrical MultiFrontal Pack (UMFPACK). The generalized alpha method with consistent initialization differential-algebraic systems (DAE) system using backward Euler approaches was employed for time stepping. All terms of the governing equations were discretized by the first order upwind scheme in the simulation. 4. Results and Discussion 4.1 Material characterization and geometry meshing together with reaction kinetics
of
The results from Figure 1a indicate that operational parameters keeping at 850 °C for 45 min will produce AC with BET specific surface area reaching around 700 m2/g, which is also the validated by Figure 1b showing ±10% experimental uncertainties. The N2
ro
validation experiments in
adsorption/desorption isotherm from Figure 2a and 2b show that the prepared AC is mainly microporous
-p
material with existences of mesopores. The X-ray deflection (XRD) analysis in Figure 2c indicates i) the peaks are scattering with two broad humps centered at 22° and 44°, which correspond to crystallite phase
re
of graphite at d(002) and d(10) planes [46], indicating the existence of the small graphite crystals in the matrix of prepared carbon, ii) the sharp peaks centered at 45° and 73° attribute to the existence of ash
ur na Color points by value of Conversion: 700
a
Predicted vs. Actual 700
700.00
b
450
600
550
650
650.00
a1
Predict
650
P re d ic te d
Actual Factors C: C = 8.00 D: D = 5.00
Design-Expert?Software Conversion
700
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X1 = A: A X2 = B: B
These results also agree well with results of ash content analysis.
BET specific surface area/m²/g
450
lP
contents (possibly the silica, MgO) when carbonaceous materials were activated at high temperature [47]. Design-Expert?Software Factor Coding: Actual Conversion Design points above predicted value Design points below predicted value 700
500
600
600.00
550
550.00
500
500.00
450
450.00
50
950
40
450.00 450
500
500.00
550.00 550
600.00 600
900 30
Actual
850 20
Actual
800 10 750
10
650
650.00
700.00 700
Figure 1. Preparation optimization and characterization of prepared pellet AC. a) response surface methodology optimization, b) model prediction vs actual experimental values.
160 140 120 100 80 60 40 20 0.0
0.2
0.4
0.6
0.8
c
b
1200 1000 800 600 400 200 0 0.1
1.0
1
0
10
20
30
e
d
40
50
60
70
80
90
2 theta degree/-
16 12
ro
Mass fraction/%
20
10
Pore diameter/nm
Relative pressure/(P/P )
of
0
1400
8 4 0 0.8
1.0
1.2
1.4
1.6
1.8
10 um
re
Particle Size/mm
-p
Pore volume/(mol/g)
180
Intensity/C.P.
a
Derivative cumulative pore volume/-
200
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Figure 2. Characterization of prepared AC. a) adsorption isotherm of AC, b) DFT (density functional theory) calculation of pore size distribution, c) XRD spectra, d) particle size of the AC e) SEM morphology of AC The particle size distribution of the pelleted AC is shown in Figure 2d. The average particle size of the
ur na
pelleted catalyst is around 1 mm which will be used for kinetic model calculations. The scanning electron microscopy morphology (SEM) of the prepared AC prior to pelleting in Figure 2e shows the irregular shapes of the porous material. This is closely associated with the carbonization and activation step. Overall, the prepared carbonaceous catalyst presents good pore formation with carbon element reaching 55% of the entire material. The detailed porosity and elementary analysis of the catalyst prepared at the optimal
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condition are summarized in Table 1.The carbon and oxygen element predominate in the prepared activated carbon, with trace amount of sulfur content being found in the prepared carbon due to the features of biomass precursor. In this work, the AC prepared at the optimal condition (the detailed porosity parameters are listed in Table 1) will be used as the catalyst for subsequent H2S oxidation. Table 1. Porosity parameters of AC prepared from optimal condition
Parameters
Unit
11
Values
BET specific surface area Pore volumes Micropore volume Average pore width Ash content Average pellet size N C H S O (by difference)
m2g-1 cm3g-1 cm3g-1 nm % mm % % % % %
695 0.4 0.3 2.4 5 1 0.6 55 4.5 0.6 39.5
of
The experimental rig setup, the packing scheme, meshing of reaction zone, the kinetics of catalytic
ro
oxidation are shown in Figure 3. In this study, the reaction was carried out in a down flow fixed-bed stainless steel reactor, with 12.7 mm ID and 400 mm in length (Figure 3a and 3b). The simplified geometry
-p
of the system was constructed in Figure 3c, the final mesh, on the basis of an independent physical study, was constituted by 32150 free mesh elements and 3080 boundary elements. The baseline condition was
re
set as the following: flow rate (1000 ml.min-1), H2S/O2 are fed in reactor at stoichiometric ratio at 423 K with the feeding H2S concentration (0.126 mol.m-3). The corresponding concentration variations of
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ur na
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reagents and products at the baseline condition are summarized Figure 3d.
12
Concentration/mol.m
-3
0.14 d)
c_H2S
GAS IN
c_O2
0.12
b)
c_H2O c_S8
0.10
Mesh
0.08
c)
0.06 0.04
QU ARTZ WOOL
0.02
AC BED
0.00
0
200 P
400 600 TOS/min
800
SCREEN
1000
H2S
TT1 AC TT2 BED TT3 P
Thermal static
GC
re
BPC
ro
O2
GAS OUT
-p
MFC
TT0
of
a)
4.2 Model validation
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Figure 3. a) Diagram of the experimental setup used for sulfur removal, b) reactor packing scheme. c) meshing of the geometry of reactor bed, d) reaction kinetics, where MFC is mass flow controller, BPC is back pressure controller, GC is gas chromatography, P is pressure transducer, TT is thermocouples.
ur na
The constructed model was further validated by the experimental results. At first, the setup model was used to predict breakthrough curve. The experiments were conducted at the baseline condition, the exit H2S concentration of the bed over the TOS is shown in Figure 4a. Except for the relative exceptional high errors at around (45% uncertainties) 5 minute, the overall error of the entire model prediction falls within
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the range of ±5.5% suggesting a good agreement with the experimental results. At 5 minutes from Figure 4a, it indicates the model overshooting the amount of sulfur deposition in the porous catalyst, which makes outlet concentration of H2S being less than experimental data. At the initial stage of deposition, the evolutions of porous medium caused by the formation of deposition is not stable. This also suggests that quasi-homogeneous porous media hypothesis might face the challenges when morphological development is not steady. The non-uniform local depositions along axial of the catalytic bed will be likely to contribute
13
to the discrepancy of the model.
a
Model Fitting Experimental Data
2500 2000 1500 1000
2
4 6 Time/min
8
b
1000 ml/min 2000 ml/min 3000 ml/min
-p
100 75 50 25 0
0
250
500 TOS/min
Model Fitting
750
1000
c
Pressure drop/Pa
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ur na
3000
10
ro
0
re
H2S conversion percentage/%
0
of
500
lP
Concentration outlet/ppmv
3000
2500 2000 1500 1000
500 0
Experimental data Simulation
0
200
400
600
800
TOS/min Figure 4. Validation of the model, a) breakthrough modeling at the baseline conditions, b) H2S conversion modeling, c) pressure drop vs TOS on baseline condition. As the corresponding initial morphological changes is reflected from the calculation of porosity and
14
permeability of the bed (Eq.23, Eq.24 and Eq.25), it will affect the sulfur balances accordingly. This could possibly explain the exceptional higher errors occurred at beginning of the deposition reaction (around 5 min). As reaction continues, the error dropped appreciably when the deposition continued (over 10 minutes) suggesting that the morphological change becomes more stable. This is a good indication of the fact that the spatial organization of the porous material predominantly determines the function of permeability, porosity and breakthrough curve predictions [48]. Apart from baseline flowrate, different flowrates (from
of
1000 to 3000 l.min-1) while the rest of conditions being kept at the baseline condition were conducted. The H2S removal profile at different flowrate together with model prediction was shown in Figure 4b. With an
ro
increase of flowrate, the corresponding increased H2S removal percentage was observed, and the model also responded well to the increase of inlet flowrate of H2S gas with less than ±10% percentage of
-p
uncertainties. In addition to the mass balances, the pressure drop over the course of reaction were continuously monitored, the pressure drops together with the model prediction were shown in Figure 4c.
re
All of the sulfur produced during the reaction at the unsteady state deposits in the AC pores leading to the deactivation and variation of pressure drop. It has been found that the sulfur deposition is
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thermodynamically facilitated by the significant decreased sulfur vapor pressure (a decrease of a factor of one thousand inside the pores) in the pores [49]. The simulation results agree well with the experimental
ur na
results indicates that the simplified assumption of pore filling by sulfur depositions, which consequently affect the porosity of the fixed bed reactor and ultimately changed the fluid dynamic features in the porous media is reasonably appropriate over the course of TOS at experimental condition. In addition, during model derivation, Eq. (1) was employed to describe reaction at experimental conditions. The reaction mechanism of H2S oxidization catalyzed by AC is still controversial. Different mechanisms such as
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chemisorption of H2S and O2 followed by reaction between the formed intermediate [50], chemisorption followed by oxidation reaction being only dependent on the unity of H2S not O2 [51], oxidation-reduction [52], oxidation facilitated by Quinone and carbonyl functional groups on surface of catalyst [53] etc have been proposed by different scholars. In this work, to simplify the reaction process, we assume that the RDS is only governed by the surface reactions between the adsorbed intermediates and the partial pressure of the produced water vapor is negligible to the oxidation rate of H2S. However, in order to calculate the
15
overall pressure, the partial pressure of produced water vapor must be considered for total pressure estimation. 4.3 Model simulation The simulation result of sulfur distributions at different TOS and across reactor is shown in Figure 5. As reaction proceeds over the course of TOS, the sulfur was captured and deposited in the porous catalytic bed continuously. Over the entire length of the bed, sulfur tends to deposit more at the inlet due to the
of
transport governed by convection, diffusion and reaction. Ultimately, the entire bed will be saturated with
ur na
lP
re
-p
ro
the sulfur leading to the breakthrough of the bed, which agrees well with experimental observations.
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Figure 5. Simulation results of sulfur deposition in the bed at the baseline condition on different TOS. Because of the high practical and fundamental interests that widely existed in the chemical, civil, oil
& gas processing, the deposition in the porous media is one of the most pivotal phenomena that needed to be gauged during the operations of the process. When fluids are introduced into porous media, the deposition reactions, which can be referring to either the precipitation on the gas-solid interface, or the trapping of fine particles at pore throats etc., cause the morphological changes of the pore space and lead
16
to the decreased porosity and permeability [54]. The simulated variations of porosity, pressure drop together with permeability of reagents at experimental condition (baseline) over the course of TOS are shown in Figure 6. As reaction proceeds, the deposition of sulfur lead to the variation of porosity of the porous catalytic bed. Using the constructed CFD models, the detailed variations of important physical features could be well monitored. Prior to the reaction, the porosity of the clean bed starts with a constant value of porosity across the bed. As the reaction proceeds, the porosity of porous catalytic bed is altered,
of
the detailed variations are shown in Figure 6a. As depicted from Eq. (23) to (25), the permeability of the bed correlates with porosities of the bed with a power that ranges from 3.5 to 4 [55]. As the reaction
ro
proceeds from beginning to 1000 min, the variations of permeability and porosity of the catalytic bed (Figure 6a and 6c) become more appreciable across the entire catalytic bed, resulting porosity difference
-p
(0.2) and pressure drop (>3000 Pa) between the entrance and exit of the catalytic bed when reaction continues for 1000 min. The initial permeability of reagent gas is a constant value in the clean bed (1.33E-
re
9 m2). As the reaction continues, the permeability of reagent gas at the front end of the porous catalytic bed changes over three order of magnitude during the course of the reaction (Figure 6b). From Figure 6,
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it indicates i) the setup system is effective in sulfur depositions, ii) the depositions continuously change the morphological shape of the porous structure of the pores in the matrix of prepared pellet catalyst.
ur na
According to the fundamental studies [56], the spatial organization of the porous material predominantly determines the function of permeability and porosity. As deposition reaction continues, the homogeneous assumptions of porosity along the axial of the catalytic bed (quasi-homogeneous medium) on the macroscopic scale might experience the theoretical challenge [57], which will lead to the significant prediction errors especially at the beginning of the reaction. The non-uniform local depositions along axial
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of the catalytic bed will be likely to contribute to the discrepancy of the model (quasi-homogeneous porous medium model) that is used to estimate the permeability, porosity and corresponding pressure lost in the CFD model. As reflected from Eq. (28), the Ergun equation used in the CFD model is dynamically correlated with morphological change of the porous media. Because of the different expressions of Eq. (23) to (25) and Eq. (28), the profiles of pressure lost (Figure 6b) are different to that of permeability and porosity of the catalytic bed at experimental conditions.
17
a Clean bed
100 200 400 600 800 1000
0.005
0.020
0.015 0.010 Length of bed/m
b
ro Clean bed 100 200 400 600 800 1000
10 0.000
0.005
-p
100
re
Pressure drop/Pa
1000
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Porosity of bed/(-)
0.40 0.36 0.32 0.28 0.24 0.20 0.16 0.12 0.08 0.04 0.00 0.000
0.010
0.015
0.020
1E-10
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Permeability/m
2
1E-9
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Length of bed/m
Clean bed 100 200 400 600 800 1000
1E-11
1E-12 0.000
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c
0.005
0.010
0.015
0.020
Length of bed/m
Figure 6. Simulations, a) porosity distribution of the bed at the baseline conditions, b) pressure drop across the bed at the baseline conditions, c) permeability (H2S) across the bed over different TOS at the baseline conditions. 5. Conclusion The preparation conditions for porous AC were optimized by using the well-established RSM considering the activation temperature and duration as the independent variables. The application of H2S
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oxidation using the obtained AC prepared from cotton black liquor under the optimal preparation condition was tested. A CFD model was successfully developed to model the H2S oxidation using AC as catalyst for sulfur removal. The morphological changes of pore structure in the porous medium as deposition reaction proceeds affect appreciably on the hydrodynamic parameters such as permeability, pressure drop ect. Overall, the constructed quasi-homogeneous porous medium model presents good predictions in kinetic experiments (breakthrough, H2S conversion and pressure drops) during H2S oxidation at the investigated
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experimental conditions. The established model is a very useful and complementary tool for monitoring
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catalytic removal of H2S over the porous AC catalyst.
conflicts of Interest
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We herein declare:
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Authors: Yong Sun, Jun He, Yunshan Wang, Gang Yang, Guangzhi Sun, Valérie Sage.
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Title: Experimental and CFD study of H2S oxidation by activated carbon prepared from cotton pulp black liquor
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Have not been submitted elsewhere except Process Safety and Environmental Protection for consideration of publication. And we have no conflicts of interests in this publication. Acknowledgements The authors would like to appreciate the critical and insightful comments that raised from anonymous
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reviewers in significantly improving the quality of this work. The following funding support are appreciated: National Key R&D Program of China (2018YFC1903500), Faculty Inspiration Grant of University of Nottingham (FIG2019) and Qianjiang Talent Scheme (QJD1803014). The Acid-Based coupled production group at Institute of Process Engineering, Chinese Academy of Science is also highly appreciated. References
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