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Licensed Unlicensed Requires Authentication Published by De Gruyter March 23, 2017

Hydrogen Production via Low Temperature Water Gas Shift Reaction: Kinetic Study, Mathematical Modeling, Simulation and Optimization of Catalytic Fixed Bed Reactor using gPROMS

Davood Mohammady Maklavany, Ahmad Shariati, Mohammad Reza Khosravi-Nikou and Behrooz Roozbehani


The kinetics study, modeling, simulation and optimization of water gas shift reaction were performed in a catalytic fixed bed reactor. The renowned empirical power law rate model was used as rate equation and fitted to experimental data to estimate the kinetics parameters using gPROMS. A good fit between predicted and experimental CO conversion data was obtained. The validity of the kinetic model was then checked by simulation of plug flow reactor which shows a good agreement between experimental and predicted values of the reaction rate. Subsequently, considering axial dispersion, a homogeneous model was developed for simulation of the water-gas shift reactor. The simulation results were also validated by checking the pressure drop of the reactor as well as the mass concentration at equilibrium. Finally, a multi-objective optimization was conducted for water-gas shift reaction in order to maximize hydrogen formation and carbon monoxide conversion, whereas the reactor volume to be minimized. Implementation of optimal controls leads to increase in hydrogen formation at reactor outlet up to 25.55 %.


A.1 Heat of reaction

The heat of reaction, also named enthalpy change of reaction, is defined as follows [3]:


For the water-gas shift reaction


Heat capacity (Cp,i) data are shown in Table 5 [4]:

Table 5:

Gas phase heat capacity constants.


It should be noted that corresponding equation to calculate Cp,i is as follows:


there, T is in Kelvin and Cp,i is in cal/mol-Kelvin.

A.2 Correlations for transport parameters

A.2.1 Effective Diffusion Coefficient

The effective mass diffusion coefficient is expressed [43]:


Where Deff,i and Di,mix are effective and molecular diffusion coefficients of species i , εb is bed porosity, and τ is the tortuosity factor.

A.2.2 Axial diffusion coefficient

The effective axial diffusivity coefficient can be estimated by [44]:


where us is superficial velocity, and Dp is catalyst particle diameter.

A.2.1 Effective thermal conductivity

The effective thermal conductivity is calculated by [45]:


where Pr is the Prandtl number Pr=Cpμ/λg.

A.3 Physical properties and mixing rules

A.3.1 Gas diffusivity

Binary gas phase diffusivities for a wide range of temperatures and pressures applicable are estimated through published correlations detailed in Table 6 [46].

Table 6:

Binary gas diffusivity for component pairs.


It should be noted that:


Equations a, b, and c are as follow:


Here, T is in Kelvin, P is in atm, and Dij is in cm2/sec.

Gas diffusion in the mixture can be approximated by [4]:


where Di,mix is diffusivity of species i in the mixture, and yi is the mole fraction of species i .

A.3.2 Gas phase viscosity

Useful correlation data for gas phase viscosity are shown in Table 7 [46].

Table 7:

Gas phase viscosity constants.


It should be noted that corresponding equation to calculate μi is as follows:


Where, T is in Kelvin and μiis in N-sec/m2.

The viscosity of the gas phase mixtures is estimated by [13]:


where μ is the mixture viscosity, μi is the viscosity of species i , and Mj is the molecular weight of species j .

A.3.3. Gas phase thermal conductivity

The thermal conductivity of components as well as are calculated using the Euckan’s formula as [4]:


The thermal conductivity of the mixture is calculated using [4]:


A.3.4. Gas phase thermal conductivity

The heat capacity of the mixture (Cp) is taken as the molar average of the gas component heat capacity.



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Received: 2016-8-31
Revised: 2017-1-6
Accepted: 2017-1-23
Published Online: 2017-3-23

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