Bilinear approximations of a nonlinear distributed fixed-bed reactor model

1994 ◽  
Vol 4 (3) ◽  
pp. 149-161 ◽  
Author(s):  
Xiang-Ming Hua
Keyword(s):  
Fixed Bed ◽  
Fixed Bed Reactor ◽  
Reactor Model

Methane Dry Reforming over Ni-Co/Al2O3: Kinetic Modelling in a Catalytic Fixed-bed Reactor

2017 ◽  
Vol 15 (6) ◽  
Author(s):  
Yacine Benguerba ◽  
Mirella Virginie ◽  
Christine Dumas ◽  
Barbara Ernst
Keyword(s):  
Catalyst Deactivation ◽  
Kinetic Modelling ◽  
Fixed Bed ◽  
Dry Reforming ◽  
Fixed Bed Reactor ◽  
Coke Deposition ◽  
Dry Reforming Of Methane ◽  
Reactor Model ◽  
Reverse Water Gas Shift ◽  
Different Temperatures

Abstract The dry reforming of CH4 was investigated in a catalytic fixed-bed reactor to produce hydrogen at different temperatures over supported bimetallic Ni-Co catalyst. The reactor model for the dry reforming of methane used a set of kinetic models: The Zhang et al model for the dry reforming of methane (DRM); the Richardson-Paripatyadar model for the reverse water gas shift (RWGS); and the Snoeck et al kinetics for the coke-deposition and gasification reactions. The effect of temperatures on the performance of the reactor was studied. The amount of each species consumed or/and produced were calculated and compared with the experimental determined ones. It was showed that the set of kinetic model used in this work gave a good fit and accurately predict the experimental observed profiles from the fixed bed reactor. It was found that reaction-4 and reaction-5 could be neglected which could explain the fact that this catalyst coked rapidly comparatively with other catalyst. The use of large amount of Ni-Co will lead to carbon deposition and so to the catalyst deactivation.

A Trickle Fixed-Bed Recycle Reactor Model for the Fischer-Tropsch Synthesis

2012 ◽  
Vol 10 (1) ◽  
Author(s):  
Kyle M. Brunner ◽  
Joshua C. Duncan ◽  
Luke D. Harrison ◽  
Kyle E. Pratt ◽  
Robson P. S. Peguin ◽  
...  
Keyword(s):  
Pressure Drop ◽  
Prandtl Number ◽  
Fixed Bed ◽  
Effective Diffusivity ◽  
Tropsch Synthesis ◽  
Fixed Bed Reactor ◽  
Maximum Temperature ◽  
Reactor Model ◽  
Fischer Tropsch ◽  
Fischer Tropsch Synthesis

A trickle fixed-bed reactor model for the Fischer-Tropsch synthesis applicable to both cobalt and iron catalysts which accounts for gas and liquid recycle is described. A selection of kinetic models for both iron and cobalt catalysts (4 each) is included in the reactor model and their effect on model predictions is compared. While the model is 1-D and reaction rates are determined for quasi-average radial bed temperatures, a correlation is used to account for radial thermal conductivity and radial convective heat transfer. Traditional pressure drop calculations for a packed column were modified with a correlation to account for trickle-flow conditions. In addition to describing the model in detail and showing validation results, this paper presents results of varying fundamental, theoretically-based parameters (i.e. effective diffusivity, Prandtl number, friction factor, etc.). For example, the model predicts that decreasing effective diffusivity from 7.1E-09 to 2.8E-09 m2/s results in a lower maximum temperature (518 K vs. 523 K) and a longer required bed length to achieve 60% conversion of CO (8.5 m vs. 5.7 m). Using molar averages of properties to calculate the Prandtl number for the gas phase (recommended by the authors) results in average bed temperatures up to 10 K higher and reactor lengths 17-45% shorter than assuming a Prandtl number of 0.7. Using the Tallmadge equation to estimate friction losses, as recommended by the authors, results in a pressure drop 40% smaller than using the Ergun equation. Validation of the model was accomplished by matching published full-scale plant data from the SASOL Arge reactors.

Simulation of coke and metal deposition in catalyst pellets using a non-steady state fixed bed reactor model

2006 ◽  
Vol 61 (22) ◽  
pp. 7463-7478 ◽  
Author(s):  
Bas M. Vogelaar ◽  
Rob J. Berger ◽  
Bas Bezemer ◽  
Jean-Paul Janssens ◽  
A. Dick van Langeveld ◽  
...  
Keyword(s):  
Steady State ◽  
Fixed Bed ◽  
Metal Deposition ◽  
Fixed Bed Reactor ◽  
Reactor Model ◽  
Catalyst Pellets

scholarly journals Validation of a Fixed Bed Reactor Model for Dimethyl Ether Synthesis Using Pilot-Scale Plant Data

Catalysts ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1522
Author(s):  
Daesung Song ◽  
Sung Yong Cho ◽  
Thang Toan Vu ◽  
Yen Hoang Phi Duong ◽  
Eunkyu Kim
Keyword(s):  
Dimethyl Ether ◽  
Fixed Bed ◽  
Pilot Scale ◽  
Fixed Bed Reactor ◽  
Average Error ◽  
Reactor Model ◽  
Feed Pressure ◽  
Co Conversion ◽  
Dimethyl Ether Synthesis ◽  
The One

The one-dimensional (1D) mathematical model of fixed bed reactor was developed for dimethyl ether (DME) synthesis at pilot-scale (capacity: 25–28 Nm3/h of syngas). The reaction rate, heat, and mass transfer equations were correlated with the effectiveness factor. The simulation results, including the temperature profile, CO conversion, DME selectivity, and DME yield of the outlet, were validated with experimental data. The average error ratios were below 9.3%, 8.1%, 7.8%, and 3.5% for the temperature of the reactor, CO conversion, DME selectivity, and DME yield, respectively. The sensitivity analysis of flow rate, feed pressure, H2:CO ratio, and CO2 mole fraction was investigated to demonstrate the applicability of this model.

scholarly journals Dehydration of 2,3-Butanediol to 1,3-Butadiene and Methyl Ethyl Ketone: Modeling, Numerical Analysis and Validation Using Pilot-Scale Reactor Data

Catalysts ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 999
Author(s):  
Daesung Song ◽  
Sung-Yong Cho ◽  
Toan-Thang Vu ◽  
Hoang-Phi-Yen Duong ◽  
Eunkyu Kim
Keyword(s):  
Numerical Analysis ◽  
Fixed Bed ◽  
Pilot Scale ◽  
Fixed Bed Reactor ◽  
Sensitivity Analyses ◽  
Inlet Temperature ◽  
Reactor Model ◽  
Feed Composition ◽  
Efficient Operation ◽  
Operation Conditions

This work presents the numerical analysis and validation of a fixed bed reactor model for 2,3-butanediol (2,3-BDO) dehydration. The 1D heterogeneous reactor model considering interfacial and intra-particle gradients, was simulated and numerical analysis of the model was conducted to understand the characteristics of the reactions in a catalyst along the reactor length. The model was also validated by comparing predicted performance data with pilot-scale plant data operated at 0.2 bar, 299–343 °C and 0.48–2.02 h−1 of weight hourly space velocity (WHSV). The model showed good agreement with the temperature profile, 2,3-BDO conversion and selectivity of target products. In addition, sensitivity analyses of the model were investigated by changing feed flow rate, feed composition, and inlet temperature. It was found that stable and efficient operation conditions are lower than 0.65 h−1 of WHSV and 330–340 °C of inlet temperature. Additionally, the reactor performance was not affected by 2,3-BDO feed concentration above 70%.

Development of a Robust Fixed-Bed Reactor Model for Supercritical Citral Hydrogenation

2017 ◽  
Vol 40 (11) ◽  
pp. 2075-2083
Author(s):  
Hans Häring ◽  
Steffen Wilhelm ◽  
Daniel Horn ◽  
Stefan Haase
Keyword(s):  
Fixed Bed ◽  
Fixed Bed Reactor ◽  
Reactor Model ◽  
Citral Hydrogenation

scholarly journals Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO2 Methanation Using Physics-Informed Neural Networks

Catalysts ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1304
Author(s):  
Son Ich Ngo ◽  
Young-Il Lim
Keyword(s):  
Neural Networks ◽  
Reaction Kinetics ◽  
Flow Reactor ◽  
Plug Flow ◽  
Fixed Bed ◽  
Effectiveness Factor ◽  
Fixed Bed Reactor ◽  
Reactor Model ◽  
Co2 Methanation ◽  
Chemical Reaction Kinetics

In this study, we develop physics-informed neural networks (PINNs) to solve an isothermal fixed-bed (IFB) model for catalytic CO2 methanation. The PINN includes a feed-forward artificial neural network (FF-ANN) and physics-informed constraints, such as governing equations, boundary conditions, and reaction kinetics. The most effective PINN structure consists of 5–7 hidden layers, 256 neurons per layer, and a hyperbolic tangent (tanh) activation function. The forward PINN model solves the plug-flow reactor model of the IFB, whereas the inverse PINN model reveals an unknown effectiveness factor involved in the reaction kinetics. The forward PINN shows excellent extrapolation performance with an accuracy of 88.1% when concentrations outside the training domain are predicted using only one-sixth of the entire domain. The inverse PINN model identifies an unknown effectiveness factor with an error of 0.3%, even for a small number of observation datasets (e.g., 20 sets). These results suggest that forward and inverse PINNs can be used in the solution and system identification of fixed-bed models with chemical reaction kinetics.

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