Evaluation of the Influence of Operating Parameters in the Modeling

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Evaluation of the influence of operational parameters in the modeling and simulation of sour gas stream desulphurization by adsorption João Paulo Lobo dos Santos, Ana Katerine de Carvalho Lima Lobato, Caetano Moraes, João Batista Severo júnior, José Jailton Marques, and Luiz Carlos Lobato dos Santos Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b03791 • Publication Date (Web): 11 Jan 2019 Downloaded from http://pubs.acs.org on January 17, 2019

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Evaluation of the influence of operating parameters in the modeling and simulation of sour

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gas stream desulfurization by adsorption

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João Paulo Lobo dos Santos1,*, Ana Katerine de Carvalho Lima Lobato2, Caetano Moraes3,

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João Baptista Severo Júnior4, José Jailton Marques5, Luiz Carlos Lobato dos Santos6.

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1,*Federal

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- Jardim Rosa Elze, Tel. +55 (79) 3194-6593, CEP: 49100-000, São Cristóvão/SE, Brazil.

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[email protected].

University of Sergipe - Petroleum Engineering Core - Av. Marechal Rondon, S/N

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2Salvador

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R. Dr. José Peroba, 251 – Stiep, CEP 41770-235, Salvador/BA, Brazil.

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3Federal

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Macedo, 2030, Ilha do Fundão, CEP 21941-909, Rio de Janeiro/RJ, Brazil.

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4Federal

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Rondon, S/N - Jardim Rosa Elze, CEP: 49100-000, São Cristóvão/SE, Brazil.

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5Federal

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Rondon, S/N - Jardim Rosa Elze, CEP: 49100-000, São Cristóvão/SE, Brazil.

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6Federal

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Aristides Novis, 2, 3º andar, Federação, CEP 40210-630, Salvador/BA, Brazil.

University - School of Architecture, Engineering and Information Technology –

University of Rio de Janeiro - Department of Chemical Engineering – Av. Horário

University of Sergipe - Department of Chemical Engineering - Av. Marechal

University of Sergipe - Department of Environmental Engineering - Av. Marechal

University of Bahia - Department of Materials Science and Technology - R. Prof.

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Abstract

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Natural gas is a fossil fuel whose participation in the world’s energy matrix has been

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growing, but, before becoming marketable, it has to undergo some treatments in order to

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eliminate corrosive compounds. Such treatments focus, mainly, on removing hydrogen

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sulphide (H2S) and carbon dioxide (CO2), being adsorption one of the recommended

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techniques for removal. The presence of H2S in the gas stream can lead to serious problems

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of corrosion and deposition of elemental sulfur. Its burning can also result in environmental

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and health problems. In this work, we evaluated the influence of operating parameters (bed

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length, feed flow of the adsorption column, adsorbate concentration in the gas stream,

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pressure and temperature) on the removal of H2S from a gas stream and, to this end, we

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employed a factorial design, which resulted in the simulation of different cases by

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modifying the above-cited parameters. To execute the simulations, we used Comsol

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Multiphisics 4.3a, a computational fluid dynamics software, and processed the results using

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Statistica 8.0. The results showed that, at the significance level of 95%, only pressure and

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temperature were statistically significant parameters. Moreover, we observed that an

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increase in pressure favors the adsorption while, for the temperature, the opposite process

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occurs.

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Keywords: Natural gas; H2S removal; Adsorption; operating parameters; desulfurization;

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Mathematical modeling.

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1. Introduction

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Among fossil fuels, natural gas has grown in importance in the world's energy

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matrix. This change in scenery is due to the technical, economic and environmental benefits 2 ACS Paragon Plus Environment

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provided by the use of this energy source. Compared with other fossil fuels, natural gas is a

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source of energy with numerous environmental benefits and features that favor a longer

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lifespan for gas-run equipment.1,2 In addition, it proves to be a versatile energy source that

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can be applied in industry, homes and in transport, as is the case of vehicular fuel.2

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The main contaminants found in natural gas are sulfur compounds, which include

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hydrogen sulfide (H2S), mercaptans (R-SH) and other organic sulfides such as RS, RS2 or

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R2S3. The hydrogen sulfide present in the gas stream can generate SO3, that produces acid

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rain, which is highly harmful to the environment and human health. It can also cause the

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deactivation of metal and metal-oxide catalysts in various processes and, finally, can lead to

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corrosion equipment and piping.2,3 The presence of H2S in the gas stream may form

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elemental sulfur (S8)4 or may even act as a solvent, increasing the capability of natural gas

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to carry and settle S85,6 in the pipelines, causing blockages.7 Therefore, the removal of H2S

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aims to increase the lifespan of equipment, improving their energy performance.8

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In the production of natural gas, desulfurization is necessary to meet the pipeline

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specifications or to allow its commercial use as fuel, and it involves the removal of H2S and

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CO2, among other components.9,10 In Brazil, in order to deliver natural gas to consumers,

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existing contaminants must be within an acceptable level as determined by the National

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Agency of Petroleum, Natural Gas and Biofuels (ANP), and the maximum level of sulfur

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compounds allowed is 70 mg/m3.11,12 In other countries, this maximum level is also

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stipulated in their respective legislation. For example, in the United States, the maximum

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acceptable levels are 10 ppm H2S and 250 ppm of SO2.13

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Gas desulfurization can be performed by: a) extraction with amine solution, b)

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catalytic hydrodesulfurization at high temperatures and pressures, c) catalytic

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oxidation/adsorption with metal oxides, d) adsorption in solid adsorbents under pressure 3 ACS Paragon Plus Environment

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and moderate temperature conditions, and e) membranes. 11,14-15 Among the adsorbents, the

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use of zeolites 16-18, activated carbon 19-21, metal oxides 15,22-23 and silica stand out.22,24

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Mathematical models are useful to understand the behavior of fixed bed columns as

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well as to design and to optimize the adsorption process without the need to perform a large

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number of experiments. With the advances in the development of numerical methods and

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computer power, the Computational Fluid Dynamics (CFD) technique has been used for

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predicting behaviors of fluid flows as well as heat and mass transfer in a fixed bed. The

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realization of experiments in full scale is often very expensive and complex and, therefore,

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the design and optimization of adsorption processes can be performed via CFD after

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understanding equilibrium data obtained in pilot scale. The influence of operating variables

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on the process can be investigated with basis on simulations, which reduces the need for

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experiments, as well as the time required to obtain results. The investigation of the

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interaction between the variables that affect the adsorption process is insufficiently

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discussed in the literature. Therefore, the main goal of this work is to check whether and

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how the five chosen operating parameters influence the modeling and to simulate the

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process of H2S removal from gas streams, using the 13X zeolite as adsorbent. The main

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advantages of 13X zeolite are that it is a commercial product readily available, as well as

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being easily regenerated and having a long lifespan, allowing its regeneration after several

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cycles of saturation.

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2. Methodology

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To carry out the simulations and to verify whether the results predicted in a

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simulation can be trusted and to more realistically represent the phenomenon under study,


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validation is necessary. We can do this validation by comparing the predicted data with

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experimental data and/or empirical correlations available in the literature. Thus, we

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obtained the experimental data used in the model validation from the literature.3,21 Table 1

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summarizes the adsorbent and adsorption column information used as input data for the

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simulations to predict breakthrough curves. Table 1

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According to Sigot et al. (2016)21, the gas stream in the adsorption column feed is a

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mixture of CH4 (42.7%), CO2 (41.5%), H2O (1.3%), O2 (2%) and H2S (4060 ppmv), while

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Mello et al. (2006)3 employs a synthetic mixture of CH4/H2S at 100 ppmv in hydrogen

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sulfide.

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In Santos et al. (2018)26, one can find the simulation methodology, the mathematical

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modeling and the considerations adopted, the choice of the Sips isotherm model as the one

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that best represents the equilibrium data between the adsorbent and the H2S, as well as

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further details. To validate the predicted results, we made a comparison between the

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experimental and simulated breakthrough curves by determining the mean relative error

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(MRE) between the experimental and predicted results. After the validation of the results,

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we varied the operating parameters as detailed below.

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2.1. Evaluation of the change in operating parameters

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We carried out a parametric analysis by systematically changing the following

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parameters: bed length (L), gas stream feed flow (Q), adsorbate concentration in the feed

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(C0), pressure in the adsorption column (P) and temperature of the column (T). First, we

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individually investigated the influence of each parameter, changing one of them while 5 ACS Paragon Plus Environment

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keeping the others constant. We considered the best fit between the simulated and the

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experimental curve as the Base Case (B. C.) to analyze the influence of the change in the

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operating variable on the dynamics of the breakthrough curves. Subsequently, we

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investigated the interaction between the parameters and a simulation planning using the

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software Statistica 8.0. We carried out the simulation planning using a fractional type

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design, with the use of a central composite rotational design (CCRD), resulting in at least

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27 simulations (2(5-1) plus 10 axial points plus 1 central point). In order to statistically

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evaluate the results, we used the amount of H2S adsorbed up until the exhaustion of the bed,

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also employing the software Statistica 8.0. The variables were assessed at a 95%

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significance level. Table 2 shows the variables used in the CCRD planning in terms of

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normalized and absolute values. Table 2

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Following the definition of the independent variables that had statistical significance

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at the 95% significance level, a new simulation was planned so that we could better

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evaluate the influence of these variables. The new simulation plan was also of fractional

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type, with the use of a central composite rotational design (CCRD), resulting in a minimum

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of 10 simulations (22 plus 4 axial points plus 2 repetitions at the central point), as will be

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detailed in the Results section. Table 3 shows the variables used in CCRD planning with

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normalized and absolute values.

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Table 3

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The amount of H2S retained by the adsorption column is a function of the

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concentration, pressure and temperature q=f (C0, P, T). Thus, for the situations that 6 ACS Paragon Plus Environment

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involved changes in any of these variables, we adjusted the amount of adsorbate adsorbed

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at equilibrium the new condition using Equation (1):

C

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Z.P. yi R.T

(1)

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where Z is the compressibility factor, P is the pressure (bar), yi is the adsorbate fraction in

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the mixture (ppmv/106), R is the universal gas constant 83.14x10-6 m³.bar/mol.K and T is

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the temperature (K).

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In the simulated process, the gas is almost dry and the temperature is close to room

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condition in the adsorption step. However, the gas pressure is high and therefore the ideal

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gas equation cannot be properly applied. In this case, we obtained gas properties such as

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density, viscosity and the compressibility factor, from the Peng-Robinson state equation,

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which is widely used in studies involving natural gas.18,25

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We inserted the value obtained from Eq. (1) into the Sips isotherm model to

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determine the adsorbed amount at equilibrium for the new desired condition of

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concentration, pressure or temperature, according to Equation (2).

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qe 



qm .bS .C n

1  bS .C

1

n



(2)

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where qm is the maximum amount adsorbed at equilibrium (mol/kg), and bs and n are

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constants of the Sips model. The values used in this equation were determined according to

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the our previous study 26, being 2.04 mol/kg, 2.41 and 0.087, respectively.


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We determined the dependence of the adsorption constant on the temperature by means of Equation (3):  H    RT 

b  b0 .e

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(3)

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where b0 is the adsorption constant at infinite temperature and ΔH is the adsorption heat

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assuming 35.5 kJ/mol according to Wynnyk et al. (2017).27

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We used the software Statistica 8.0 to process the data and used the amount of H2S

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adsorbed until exhaustion of the bed as the response variable. A typical value for bed

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exhaustion occurs when the concentration at the bed outlet reaches 95% of the feed

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concentration (C/C0=0.95).28 The amount adsorbed up until exhaustion of the bed was

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calculated using Equation (4): t QC0  C  q 1   dt m 0  C0 

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(4)

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where q is the amount adsorbed (mol/kg); Q is the gas flow rate in the feed (m³/min); C0 is

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the concentration of adsorbate in the gas stream (mol/m³); t is the time (min) and m is the

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adsorbent mass in the bed (kg).

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Thus, using the amount adsorbed as a dependent variable on the factors under

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evaluation, we could statistically evaluate those factors using a Pareto Chart, response

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surfaces and variance analysis (ANOVA). In addition, we could obtain a predictive model

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of the quantity of H2S adsorbed as a function of the change in the operating conditions.

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3. Results and discussion


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3.1. Validation

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According to Equation (2), the predicted amounts of hydrogen sulfide adsorbed at

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equilibrium were 4.17 mol/kg and 3.49 mol/kg using data from Sigot et al. (2016)21 and

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Mello et al. (2006)3, respectively.

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For experimental data by Sigot et al. (2016)21 the estimated Reynolds number

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(Re=3.69) is in the transition zone, which, according to Dantas et al. (2011)29 (Re10) considered a high Reynolds number.

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Santos et al. (2018)26 evaluated the influence of the axial dispersion coefficient on the Sips

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isotherm model, and we varied that parameter in the simulations, keeping the others

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constant. The values of the axial dispersion coefficient were used as: 6.57 x 10-5 m²/s, 1.49

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x 10-4 m²/s, 5.27 x 10-4 m²/s and 9.98 x 10-4 m²/s, respectively. The results for the Sips

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isotherm model are shown in Figure 1.

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Figure 1

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In Figure 1, it is possible to see that the reduction of the axial dispersion coefficient

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caused a decrease in the Mass Transfer Zone (MTZ), which in turn caused the simulated

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curves to shift to the right, and the best fit between the experimental curve and the

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simulated ones occurred for the axial dispersion coefficient of 6.57 x 10-4 m²/s. The

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simulated curve using the axial dispersion coefficient of 6.57 x10-4 m²/s was able to predict

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the breaking point and bed exhaustion with relative error of 4.87% and 3.88%, respectively.

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Therefore, based on the errors obtained, the predicted curve can be considered validated

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and, therefore, we can use it as the Base Case (B. C.) for evaluating the influence of

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changes in operating parameters.


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In order to better validating the proposed model, the simulated results were

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compared with experimental data obtained under different adsorption bed conditions. In

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this case, we simulated the breakthrough curve considering equilibrium data and applied

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the Sips model (Eq. 2) to the experimental conditions used by Mello et al. (2006)3, as

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shown in Table 1. Figure 2 shows the comparison between simulated and experimental

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breakthrough curves.

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Figure 2

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Figure 2 allows us to reinforce the fact that the proposed model was capable of

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satisfactorily reproducing the experimental curve and to predict the breakpoint and bed

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exhaustion with relative error of 8.07% and 3.34%, respectively. Moreover, it is possible to

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see that the constants of Sips model, even coming from a different gas stream according to

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the experimental data by Sigot et al. (2016)21, were able to predict the breakthrough curve.

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It shows that the proposed model can be used for different gas compositions.

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Furthermore, the validated breakthrough curve based on Sigot et al. (2016)21 data

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was used as the Base Case because it is referred to a field study and best represents real

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transport conditions for a gas stream. With basis on this B.C., we systemically evaluated

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the influence of each parameter on the behavior of the bed.

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3.2. Effects of changing in operating parameters

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In this section, we investigate the influence of the changes in the operating

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parameters on the performance of the adsorbent bed. In order to evaluate the influence of

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each parameter individually, we varied one of them while the others were kept constant in

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the simulations. Figure 3 shows the behavior of the bed as a result of a change in its length. 10 ACS Paragon Plus Environment

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Figure 3

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As expected, reducing the length of the bed by half causes the breaking point to

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occur earlier due to the shift of the breakthrough curve to the left. The exhaustion of the bed

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also occurs much sooner when compared to the base case. The behavior is consistent

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because, in this case, the adsorbent mass is also reduced by half and the saturation of the

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bed is reached sooner, which causes a smaller amount of H2S to be removed from the gas

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stream. Moreover, the reduction in porous bed length leads to a reduction in residence time,

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which also contributes to a decrease in the efficiency of the adsorbent bed. On the other

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hand, the increase in bed size (0.15 m) showed no significant change in the breakthrough

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point. However, the amount of adsorbed H2S increased, causing the bed to take longer to

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reach saturation. This behavior is consistent since, in this case, the adsorbent mass is 50%

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higher than in the case study base, which increases the number of active sites for

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adsorption. Also, the residence time increased, maintaining the gas stream in contact with

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the adsorbent for longer, which favors the removal. It is also evident that the increase in bed

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size decreases the slope of the breakthrough curve as a consequence of the increase in

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MTZ, which causes the flow pattern to deviate more from the ideal. This is due to the fact

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that very large bed lengths are not recommended for a single adsorption column, since this

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leads to an unstable flow rate in the column because of a higher resistance to the flow,

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according to the literature.30

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Figure 4 shows the influence of the feed stream flow rate and, consequently, the interstitial velocity in the behavior of the breakthrough curves. Figure 4


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In Figure 4, it is possible to see that, for higher flows and, consequently, larger

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interstitial velocities, the breakthrough curve presents a higher slope and a faster bed

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saturation. This behavior of the breakthrough curves is due to the fact that an increase of

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the interstitial velocity raises the coefficients of external and global mass transfer, thus

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reducing the resistance to mass transfer. A behavior similar to the one found in this work

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with the increase of interstitial velocity in the removal of H2S from a gas stream was

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obtained by Aguilera and Ortiz (2016).31 It is also worth mentioning that an increase in

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velocity results in a reduction in the residence time, which, as already discussed, leads to a

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reduction of the adsorbed amount of the contaminant, resulting in a lowered adsorbent bed

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efficiency, as well as the rupture time occurring earlier. Another important point that helps

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to explain the shift of the curve to the left is the variation in the value of the axial dispersion

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which increases with the interstitial velocity, ranging from 0.010 m/s to 0.016 m/s. The

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Peclet number varied from 30.8 to 33 for the interstitial velocity of 0.010 m/s and 0.016

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m/s, respectively.

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The amount of H2S adsorbed in the 13X zeolite is a function of its concentration in

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the feed stream. Using Equation (2), the amount of adsorbed H2S at feed concentrations of

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0.143 mol/m³ and 0.173 mol/m³ found were 4.14 mol/kg and 4.20 mol/kg, respectively.

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Figure 5 shows the influence of the alteration of the H2S concentration in the feed stream in

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the dynamics of the simulated breakthrough curves.

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Figure 5

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In Figure 5, one can notice that the increase the concentration of H2S in the feed

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stream leads to a faster saturation of the bed. According to the literature26, the overall mass

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transfer coefficient (KS) is directly proportional to the concentration of adsorbate in the feed


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stream and, therefore, the raise in KS results in the reduction of the mass transfer resistance,

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which causes to the breakthrough curves to shift left and, consequently, to a decrease in bed

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exhaustion time. So, it is possible to observe that the higher the H2S concentration in the

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feed stream, the faster the need for replacing and/or regenerating the adsorbent bed. In this

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case, the axial dispersion coefficient, the Reynolds number and the Peclet number remained

6

constant, thus not influencing the dynamics of the breakthrough curves.

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Figure 6 shows the effect of the change in the adsorption pressure on the behavior of

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the simulated fracture curves, the equilibrium adsorption capacity being determined using

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Eqs. (1) and (2).

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Figure 6

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Figure 6 shows that the raise in pressure in the adsorption step causes a shift of the

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breakthrough curves to the right, which leads to an increase in bed exhaustion time. The

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quantity adsorbed at equilibrium increased from 4.17 kg/mol to 4.95 kg/mol, when the

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pressure varied from 1 to 9 bar. In this case, the displacement of the breakthrough curves to

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the right is explained by the reduction of the external and global mass transfer coefficients,

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which leads to an increase in mass transfer resistance, causing the bed to take longer to

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reach saturation. This increase in mass transfer resistance can also be observed in Figure 6

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by the shape of the bending curves being slightly changed from steep concave, to flat

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concave (lower slope) when the adsorption pressure increased from 1 bar to 9 bar, leading

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to a wider MTZ. In this case, the axial dispersion and, consequently, the Peclet number

21

remained constant and did not exert influence on the dynamics of the simulated

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breakthrough curves.


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The results for the bed with increasing pressure match those found by Cavenati et al.

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(2004)32, who evaluated the adsorption of CO2 in zeolite 13X, and Wynnyk et a. (2017)27,

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who studied the adsorption of CH4, CO2 and H2S in zeolite 4A, and found that the adsorbed

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amount increased with the increase in pressure. However, it is worth noting that the

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adsorbed amount is not infinite with increasing pressure. Moreover, the behavior of the

6

curves shows that the Pressure Swing Adsorption (PSA) method could be used for bed

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regeneration, since the reduction in pressure lowers the adsorption capacity of the adsorbent

8

bed, allowing the removal of the adsorbed H2S and its reuse in another cycle.

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Based on the value of the adsorption constant for the temperature of 25 °C (298 K)

10

and the heat of adsorption, and using Eq. (3), we determined the adsorption constant for

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infinite temperature, and a value of 2.85 x 10-9 bar-1. In doing so, we could verify the effect

12

of the temperature on the simulated breakthrough curves, as shown in Figure 7.

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Figure 7

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In Figure 7, we can see that the raise in the temperature causes a shift of the

15

breakthrough curve to the left, which in turn causes a reduction of the rupture point, as well

16

as a shorter saturation time of the bed. This is due to the fact that the adsorption is,

17

generally, an exothermic process and the quantity adsorbed at equilibrium reduces with the

18

raise in temperature, being equal to 3.94 kg/mol for the temperature of 313 K and 3.74

19

kg/mol for the temperature of 328 K. In Eq. (3), we can observe that it matches the

20

Arrhenius model insomuch that the increase in temperature reduces the constant of the

21

adsorption isotherm model, implying a reduction in the amount of adsorbed H2S. In

22

addition, it is possible to observe that the effect of temperature is inverse to pressure, since,

23

in this scenario, there is a raise in the external and global mass transfer coefficients,


Page 15 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

1

resulting in the reduction of resistance to mass transfer, so the bed takes less time to reach

2

saturation. The raise in temperature leads to a raise in molecular diffusivity and effective

3

diffusivity, implying a raise in the external and global transfer coefficients, as revealed by

4

the increasing slope of the curves when the temperature rises from 298 K to 328 K. Once

5

more, in this case, the axial dispersion and the Peclet number remained constant and did not

6

influence the dynamics of the curves.

7

The behavior of the curves shows that operating the bed at higher temperatures

8

would not be interesting, since its efficiency is reduced. On the other hand, the use of high

9

temperatures may be an alternative for bed regeneration and the desorption process by TSA

10

could be employed, thus allowing the use of the bed in more than one adsorption cycle. The

11

outcome of changes in temperature change match those found in the reported studies27,33,

12

which evaluated the removal of CO2 and H2S in zeolite 13X.

13

3.3. Statistical evaluation of the influence of operating parameters

14

In order to analyze the dependence of the studied variables (bed length, operating

15

flow, feed concentration, pressure and temperature) on the amount of H2S adsorbed to the

16

saturation of the bed, it was necessary to perform simulations, according to the design

17

matrix of Table 4. The amount adsorbed up to the saturation of the bed used as response

18

variable was obtained by using Eq. (4).

19

Table 4

20

The data in Table 4 was subjected to a statistical analysis using the software

21

Statistica 8.0 to determine the effects of the 5 factors under study and their interactions on

22

the amount of adsorbed H2S. The calculations showed that, at the studied levels, only the 15 ACS Paragon Plus Environment

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Page 16 of 33

1

effects related to pressure variation and adsorption temperature variation are statistically

2

significant at the 95% significance level, as there are no interactions between the studied

3

variables, as seen in Pareto Chart, shown in Figure 8.

4

Figure 8

5

In the Pareto Chart, one can see that the main effects related to variations in

6

pressure and temperature have a greater linear dependence on the amount of adsorbed H2S.

7

In addition, the Pareto Chart corroborates the previously shown results for the behavior of

8

those variables, since the pressure raise is favorable for increasing the amount adsorbed,

9

while it becomes necessary to reduce the temperature to favor the process.

10

In the Pareto Chart, parameters that have p-values (probability of significance)

11

greater than 0.05 must be taken from the model, since they do not present statistical

12

significance34. Therefore, based on Figure 8, a new design was performed considering only

13

pressure and temperature. Table 5 shows the design matrix and responses considering only

14

pressure and temperature as evaluated factors.

15

Table 5

16

Table 5 contains a new statistical analysis of the factors and their interactions. The

17

new Pareto Chart ratified the previous behavior where only the main effects of pressure and

18

temperature are statistically significant for the levels under study, as seen in Figure 9.

19

Figure 9

20

The study of effects, considering pressure and temperature as variables, confirms

21

that, for the target levels, these parameters are statistically independent, that is, there are no

22

interactions between them. The response surface shown in Figure 10 confirms that the


Page 17 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1

increase in pressure favors the adsorption process, whereas for the temperature, the effect is

2

the inverse, agreeing with the behavior shown in the Pareto Charts.

3

Figure 10

4

In the response surface generated by the model it is possible to better visualize the

5

dependence of the adsorbed quantity with the variation of pressure and temperature. It is

6

evident that the raise in pressure favors an increase in the adsorbed amount. However, this

7

increase tends to be asymptotic in relation to the pressure and may not bring significant

8

gains in the quantity adsorbed at high pressures, since the value of the adsorbed quantity

9

tends to become constant. The effect of the temperature is the opposite, and it is necessary

10

to work with values closer to room temperature so that the adsorption process is favored.

11

Moreover, the effect of the pressure is more pronounced than that of the temperature when

12

its values increase in relation to room conditions. It is also possible to observe in the

13

response surface curve that the dependence between the quantity adsorbed and the pressure

14

is not totally linear, as shown in the Pareto chart.

15

Through the statistical analysis of the results, we found the regression coefficient

16

values for the significant variables that describe the empirical model, according to Equation

17

(5). The presence of quadratic terms in the equation obtained for the model, despite

18

presenting small values for the coefficients, ratifies the assertion that the dependence

19

between the quantity adsorbed and the pressure is not totally linear.

20

q  4.28  0.3113P  0.0968 P 2  0.1958T  0.0068T 2  0.0075 PT

(5)

21

The fit quality of the model can be assessed through the ANOVA (Analysis of

22

Variance) shown in Table 6. For the significance level α=0.05, the minimum value of Ftab is


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 33

1

5.19, while the value of Fcal is 133.34 and therefore, the value found is significant, and

2

therefore we can state that the errors of the model are consistent with the reality of the

3

simulations, i.e., the resulting model is able to represent the simulated situations.

4

Table 6

5

Another way to evaluate the model is by comparing the value predicted by the

6

model with the value of the adsorbed quantity at equilibrium in the experiment found in the

7

literature21 for the pressure and room temperature conditions, which was 4.17 mol/kg. The

8

value predicted by the model represented by Eq. (5) is 4.07 mol/kg, which results in a

9

relative error of 2.4%, which in turn shows that the resulting model can satisfactorily

10

predict the amount adsorbed.

11 12

4. Conclusions

13

In this work, we investigated the influence of the operating parameters on the

14

dynamics of the breakthrough curves for removal of H2S from gas streams. Prior to the

15

modification of the operating parameters, it was necessary to validate the results obtained in

16

the simulations, based on the calculation of the relative error between experimental21,3 and

17

predicted data, to estimate rupture and exhaustion points. The model herein used proved to

18

be robust, since it was able to reproduce experimental data with different gas compositions

19

and fixed bed operating conditions. After the validation of the results, it was possible to

20

perform a numerical analysis of the influence of operating parameters for different bed

21

configurations modifying the variables (length, gas flowrate, adsorbate concentration,

22

pressure and temperature). With basis on a factorial design, we could investigate whether

23

there is any interaction between the variables under study. The results showed that, at a


Page 19 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1

significance level of 95%, only pressure and temperature were statistically significant

2

parameters, as no interaction between factors was observed. Furthermore, it was observed

3

that the increase in pressure favored the adsorption process, whereas, for the temperature,

4

an inverse behavior occurred, which is coherent, given that the physical adsorption is an

5

exothermic process. This behavior indicated that the Pressure Temperature Swing

6

Adsorption technology is an attractive option for use in bed regeneration, thus allowing

7

13X zeolite to be reused in more than one cycle.

8

Acknowledgements

9

The authors thank the Coordination for Improvement of Higher Education

10

Personnel (CAPES), the Multidisciplinary Laboratory of Materials and Active Structures

11

(LaMMEA) and the High Voltage Laboratory (LAT) of the Federal University of Campina

12

Grande for support in this work.

13

References

14

(1) Santos, J. P. L.; Lobato, A. K. C. L.; Moraes, C.; Santos, L. C. L. Determination of

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elemental sulfur deposition rates for different natural gas compositions, J. Pet. Sci. Eng.

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2015, 135, 461-465.

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(2) Santana, T. S.; Ferraz, C. Estudo do processo de sequestro de sulfeto de hidrogênio

18

(H2S) de uma corrente de gás natural. XXI Congresso Brasileiro de Engenharia Química

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2016, Fortaleza-CE, 2016.

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(3) Melo, D. M. A.; Souza, J. R.; Melo, M. A. F.; Martinelli, A. E.; Cachima, G. H. B.;

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Cunha, J. D. Evaluation of the Zinox and Zeolite Materials as Adsorbents to Remove H2S

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from Natural Gas. Colloids Surf., A. 2006, 272, 32–36. 19 ACS Paragon Plus Environment

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(4) Kimtantas, C. L.; Taylor, M. A. An Effective Solution for Elemental Sulfur Deposition

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in Natural Gas Systems, Presented at the American Filter and Separation Society

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Conference 2014, Houston, Texas, USA, 2014.

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(5) Clark, P. D.; Davis, P.; Simion, J. E.; Lau, S. C. Fitzpatrick Recent Development in The

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Mitigation of Sulfur Deposition in Sour Gas Facilities, Alberta Sulphur Research Ltd.,

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Calgary, Alberta (1995).

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(6) Pack, D. J.; Parks, D. W.; Chesnoy, A. B. Gas pipeline preferential site selection

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occurrence for elemental sulphur & other particle matter formation & deposition. J. Pet.

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Sci. Eng. 2012, 94–95, 12–18.

10

(7) Zhou, F.; Hongchuan, L.; Jianyi, L.; Xu, O. Y.; Liuli, L. The elemental sulfur

11

deposition and its corrosion in high sulfur gas fields. Natur. Gas Ind. 2013, 33 (9), 102-109.

12

(8) Possa, R. D.; Formighieri, V. M.; Shanahan, J. A.; Rosas, T. L. S. Adsorção de H2S

13

utilizando carvão obtido da pirólise do Lodo de esgoto ativado com HCl e KOH. In II

14

Congresso Nacional de Engenharia de Petróleo, Gás Natural e Biocombustíveis 2016, Natal

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–RN (2016).

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(9) Koyun, T.; Kunduz, M.; Oztop, H. F.; Ucgul, I. Comparison of purification processes of

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natural gas obtained from different regions in the world. J. Nat. Gas Chem. 2012, 21, 61-

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68.

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(10) Kunz, O.; Wagner, W. The GERG-2008 Wide-Range Equation of State for Natural

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Gases and Other Mixtures: An Expansion of GERG-2004. J. Chem. Eng. Data, 2012, 57,

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C. L. Comparison of different processes for preventing deposition of elemental sulfur in

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natural gas pipelines: A review, J. Nat. Gas Sci. and Eng. 2016, 32, 364 -372.

4

(12) Santana, T. S.; Machado, L. C. Estudo de processos físico-químicos para o sequestro

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de sulfeto de hidrogênio (H2S) de uma corrente de gás natural. Cadernos de Graduação -

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Ciências Exatas e Tecnológicas UNIT, 2014, 2(1), 105-115.

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(13) Schmidt, R.;Cross, J. B.; Latimer, E. G. Tail-Gas Cleanup by Simultaneous SO2 and

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H2S Removal. Energy & Fuels, 2009, 23 (7), 3612-3616.

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(14) Kumar, P.; Sung, C.-Y.; Muraza, O.; Cococcioni, M.; Hashimi, S. A.; MCcormick, A.;

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Tsapatsis, M. H2S adsorption by Ag and Cu ion exchanged faujasites. Micropor. Mesopor.

11

Mat. 2011, 146, 127–133.

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(15) Ratnasamy, C.; Wagner, J. P.; Spivey, S.; Weston, E. Removal of sulfur compounds

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from natural gas for fuel cell applications using a sequential bed system. Catal. Today.

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2012, 198, 233– 238.

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(16) Ryzhikov, A.; Hulea, V.; Tichit, D.; Leroi, C.; Anglerot, D.; Coq, B.; Trens, P. Methyl

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mercaptan and carbonyl sulfide traces removal through adsorption and catalysis on zeolites

17

and layered double hydroxides. Appl. Catal., A. 2011, 397, 218–224.

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(17) Oliveira, L. H.; Meneguin, J. G.; Silva, E. A.; Barros, M. A. S. D.; Arroyo, P. A.;

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Grava, W. M.; Nascimento, J. F. Prediction of Three Component Gas Adsorption with

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IAST and Langmuir. COBEQ - XX Congresso Brasileiro de Engenharia Química 2014,

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Florianópolis - SC (2014). 21 ACS Paragon Plus Environment

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(18) Yousefi, H.; Mehrpooya, M.; Naeiji, E. Modeling and Optimization of Currently in

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Operation Natural Gas Desulfurization Process Using Adsorption Separation Method.

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Chem. Eng. Process. Process Intensif. 2017, 120, 220–233.

4

(19) Castrillon, M. C.; Moura, K. O.; Alves, C. A.; Bastos Neto, M.; Azevedo, D. C. S.;

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Hofmann, J.; Möllmer, J.; Einicke, W. D.; Glaser, R. CO2 and H2S removal from CH4-rich

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streams by Adsorption on Activated Carbons Modified with K2CO3, NaOH or Fe2O3.

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Energy & Fuels, 2016, 30 (11), 9596-9604.

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(20) Wang, J.; Ju, F.; Han, L.; Qin, H.;Hu, Y.; Chang, L.; Bao, W. Effect of Activated

9

Carbon Supports on Removing H2S from Coal-Based Gases using Mn-Based Sorbents.

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Energy & Fuels, 2015, 29 (2), 488-495.

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(21) Sigot, L.; Obis, M. F.; Benbelkacem, H.; Germain, P.; Ducom, G. Comparing the

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performance of a 13X zeolite and na impregnated activated carbon for H2S removal from

13

biogas to fuel an SOFC: Influence of water. Int. J. Hydrogen Energy. 2016, 1-9.

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(22) Ko, C. H.; Song, H. I.; Park, J. H.; Han, S. S.; Kim, J. N. Selective Removal of Sulfur

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Compounds in City-gas by Adsorbents. Korean J. Chem. Eng. 2007, 24(6), 1124-1127.

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(23) Sekhavatjou, M. S.; Moradi, R.; Hosseini Alhashemi, A.; Taghinia Hejabi, A. A new

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method for sulfur components removal from sour gas through application of zinc and iron

18

oxides nanoparticles. Int. J. Environ. Res. 2014, 8(2), 273-278.

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(24) Watabe, T.; Yogo, K. Isotherms and isosteric heats of adsorption for CO2 in amine-

20

functionalized mesoporous silicas. Sep. Purif. Technol. 2013, 120, 20-23.


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(25) Qazvini, O. T.; Fatemi, S. Modeling and simulation pressure–temperature swing

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adsorption process to remove mercaptan from humid natural gas; a commercial case study.

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Sep. Purif. Technol. 2015, 139, 88–103.

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(26) Santos, J. P. L. ; Cunha, A. L. ; Lobato, A. K. C. L. ; Moraes, C. ; Severo Junior, J.

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B.; Santos, L. C. L. . Modeling and simulation of the desulfurization of a sour gas streams

6

by adsorption: influence of the isotherm model. Chem. Ind. Chem. Eng. Q. 2018, 24, 20-50.

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(27) Wynnyk, K. G.; Hojjati, B.; Pirzadeh, P.; Marrioti, R. A. High-pressure sour gas

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adsorption on zeolite 4A. Adsorption. 2017, 23 (1), 149-162.

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(28) Possa, R. D.; Sousa, J. F.; Oliveira, J. A.; Nascimento, P. F. P.; Lima, M. T. A.;

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Brandão, I. B.; Bezerra, M. B. D. Dynamic adsorption of H2S in a fixed bed of sewage

11

sludge pyrolysis char. Braz. J. Pet. Gas. 2018, 12 (2), 77-90.

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(29) Dantas, T. L. P.; Luna, F. M. T.; Silva Júnior, I. J.; Torres, A. E. B.; Azevedo, D. C.

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S.; Rodrigues, A. E.; Moreira, F. P. M. Modeling of the fixed-bed adsorption of carbon

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dioxide and a carbon dioxide-nitrogen mixture on zeolite 13X. Braz. J. Chem. Eng. 2011,

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behaviors for adsorption of levulinic acid from aqueous solution using SY-01 resin. Sep.

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Purif. Technol. 2017, 174, 222-231.


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(31) Aguilera, P. G. Ortiz, F. J. G. Prediction of fixed-bed breakthrough curves for H2S

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adsorption from biogas: importance of axial dispersion for design. Chem. Eng. J. 2016,

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(32) Cavenati, S.; Grande, C. A.; Rodrigues, A. E. Adsorption Equilibrium of Methane,

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Carbon Dioxide, and Nitrogen on Zeolite 13X at High Pressures. J. Chem. Eng. Data.

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2004, 49 (4), 1095–1101.

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(33) Hauchhum, L.; Mahanta, P. Carbon dioxide adsorption on zeolites and activated

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(34) Bard, Y. Nonlinear parameter estimation, Academic Press. 1974, New York.

11 12 13 14 15 16 17 18


Page 25 of 33

1

Figure captions

1.10 1.00 0.90 0.80 C/C0

0.70

Experimental data [21]

0.60

Sips and DL=6.57x10^(-5)

0.50 0.40


0.30


0.20


0.10 0.00 0

20

40 t (h)

60

80

2 3

Figure 1 - Comparison between the experimental data21and simulated breakthrough curves

4

using Sips isotherm model.

1.10 1.00 0.90 0.80 0.70 C/C0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

0.60 Experimental data [3]

0.50 0.40

Sips with DL=3.43x10^(-4)

0.30 0.20 0.10 0.00 0

10

20 t (h)

30

40

5


Energy & Fuels

Figure 2 - Comparison between the experimental data3 and simulated breakthrough curves

2

using Sips isotherm model.

C/C0

1

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

L=0.05 m L=0.10 m (B. C.) L=0.15 m 0

50

4

100

150

t (h)

3

Figure 3 - Effect of bed length change on simulated breakthrough curves.

1.00 0.90 0.80 0.70 0.60 C/C0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 33

0.50

Q=0.8 L/min

0.40

Q=1 L/min (B.C.)

0.30

Q=1.2 L/min

0.20 0.10 0.00 0

20

40 t (h)

60

80

5 6

Figure 4 - Effect of the flowrate change on the simulated breakthrough curves.


Page 27 of 33

1.00 0.90 0.80 0.70 C/C0

0.60 0.50

C0=0.143 mol/m³

0.40 0.30

C0=0.158 mol/m³ (B. C.)

0.20 0.10

C0=0.173 mol/m³

0.00 0

20

40 t (h)

60

80

1 2

Figure 5 - Influence of H2S concentration on the simulated breakthrough curves.

1.00 0.90 0.80 0.70 0.60 C/C0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

0.50

P=1 bar (B. C.)

0.40

P=5 bar

0.30

P=9 bar

0.20 0.10 0.00 0

20

40 t (h)

60

80

3 4

Figure 6 - Effect of the pressure change in the adsorption step on the breakthrough curves.


Energy & Fuels

1.00 0.90 0.80 0.70 0.60 C/C0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 33

0.50

T=298 K (B. C.)

0.40

T=313 K

0.30

T=328 K

0.20 0.10 0.00 0

20

40 t (h)

60

80

1 2

Figure 7 - Effect of the temperature change in the adsorption step on the breakthrough

3

curves.

4 5

Figure 8 - Pareto Chart for the five effects evaluated. 28 ACS Paragon Plus Environment

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1 2

Figure 9 - Pareto Chart for the two effects evaluated.

3 4

Figure 10 - Response surface as a function of pressure and temperature variables.

5 6 7 8 29 ACS Paragon Plus Environment

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1

Tables Table 1 - Feed specifications and bed characteristics.21

2

Experimental data21

Experimental data3

Concentration of H2S in the feed (mol/m³)

0.158

0.02

Bed length (mm)

100

40

Diameter of bed (mm)

40

3.175

Particle Diameter

2

2

Particle Density (kg/m³)

1130*

1130

Particle Porosity [-]

0.24*

0.24

Particle Tortuosity

1.38*

1.38

Average pore diameter (m)

7.4x10-10

7.4x10-10

Density of bed (kg/m³)

700

660

Porosity of bed [-]

0.36

0.41

Feed rate (L / min)

1

0.03

Temperature [K]

298

298

Pressure (bar)

1

4.9

Parameters

3

Page 30 of 33

*Taken

from.25


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Energy & Fuels

Table 2 - CCRD planning matrix.

1

Levels Factors

(-1)

(0)

(+1)

Bed length (m)

0.05

0.1

0.15

Gas flow in the feed (L/min)

0.8

1

1.2

0.143

0.158

0.173

1

5

9

298

313

328

Concentration in the feed (mol/m³) Pressure in the adsorption column (bar) Temperature of the adsorption column (K) 2 3

Table 3 - Matrix of the new CCRD type planning.

4

Levels Factors Pressure in the adsorption column (bar) Temperature of the adsorption column (K)

(-1)

(0)

(+1)

1

5

9

298

313

328

5 6 7 31 ACS Paragon Plus Environment

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Page 32 of 33

Table 4 - Design matrix and responses for five variables.

1 Simulations

L (m)

Q (L/min)

C0 (mol/m³)

P (bar)

T (K)

q (mol/kg)

1

-1

-1

-1

-1

+1

3.54

2

-1

-1

-1

+1

-1

4.76

3

-1

-1

+1

-1

-1

3.99

4

-1

-1

+1

+1

+1

4.30

5

-1

+1

-1

-1

-1

3.34

6

-1

+1

-1

+1

+1

4.02

7

-1

+1

+1

-1

+1

3.38

8

-1

+1

+1

+1

-1

4.38

9

+1

-1

-1

-1

-1

4.13

10

+1

-1

-1

+1

+1

4.42

11

+1

-1

+1

-1

+1

3.76

12

+1

-1

+1

+1

-1

4.86

13

+1

+1

-1

-1

+1

3.56

14

+1

+1

-1

+1

-1

4.71

15

+1

+1

+1

-1

-1

3.97

16

+1

+1

+1

+1

+1

4.20

17

-1

0

0

0

0

4.03

18

+1

0

0

0

0

4.30

19

0

-1

0

0

0

3.42

20

0

+1

0

0

0

4.21

21

0

0

-1

0

0

4.25

22

0

0

+1

0

0

4.31

23

0

0

0

-1

0

3.74

24

0

0

0

+1

0

4.49

25

0

0

0

0

-1

4.52


Page 33 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Simulations

L (m)

Q (L/min)

C0 (mol/m³)

P (bar)

T (K)

q (mol/kg)

26

0

0

0

0

+1

4.07

27 (C)

0

0

0

0

0

4.28

1 2

Table 5 - Design matrix and responses for two variables.

3

Simulations

P (bar)

T (K)

q (mol/kg)

1

-1

-1

4.03

2

-1

+1

3.55

3

+1

-1

4.73

4

+1

+1

4.28

5

-1

0

3.74

6

+1

0

4.49

7

0

-1

4.52

8

0

+1

4.07

9 (C)

0

0

4.28

10 (C)

0

0

4.28

4 5

Table 6 - ANOVA for the obtained model. FV

SQ

nGL

MQ

Fcalc

Ftab

P

Regression

1.13

5

1.13

133.34

5.19

1.85

Waste

0.033785

4

0.00845

Pure error

0.033785

4

0.00845

Total

1.166410

9

6


Evaluation of the Influence of Operating Parameters in the Modeling

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