An algorithm for evaluating reaction rates of catalytic reaction networks with strong diffusion limitations

2001 ◽  
Vol 25 (9-10) ◽  
pp. 1185-1198 ◽  
Author(s):  
Sergio P. Bressa ◽  
Néstor J. Mariani ◽  
Néstor O. Ardiaca ◽  
Germán D. Mazza ◽  
Osvaldo M. Martı́nez ◽  
...  
2009 ◽  
Vol 113 (11) ◽  
pp. 3579-3583 ◽  
Author(s):  
Malika Kumarasiri ◽  
Gregory A. Baker ◽  
Alexander V. Soudackov ◽  
Sharon Hammes-Schiffer

2000 ◽  
Vol 163 (1-2) ◽  
pp. 189-204 ◽  
Author(s):  
Rostam J. Madon ◽  
Enrique Iglesia

Author(s):  
Xijia Lu ◽  
Ting Wang

In this paper, the coal-to-synthetic natural gas (SNG) technologies have been reviewed. Steam-oxygen gasification, hydrogasification, and catalytic steam gasification are the three major gasification processes used in coal-to-SNG production. So far, only the steam-oxygen gasification process is commercially proven by installing a catalytic methanation reactor downstream of the gasification process after syngas is produced, cleaned, and shifted to achieve an appropriate H2/CO ratio for methanation reaction. This process is expensive, less efficient, and time consuming. Ideally, it will be more effective and economic if methanation could be completed in an once-through entrained-flow gasifier. Technically, this idea is challenging because an effective gasification process is typically operated in a high-pressure and high-temperature condition, which is not favorable for methanation reaction, which is exothermic. To investigate this idea, a computational model is established and a sensitivity study of methanation reactions with and without catalysts are conducted in this study. In modeling the methanation process in a gasifier, correct information of the reaction rates is extremely important. Most of known methanation reaction rates are tightly linked to the catalysts used. Since the non-catalytic reaction rates for methanation are not known in a gasifer and the issues are compounded by the fact that inherent minerals in coal ashes can also affect the methanation kinetics, modeling of methanation in an entrained-flow gasifier becomes very challenging. Considering these issues, instead of trying to obtain the correct methnation reaction rate, this study attempts to use computational model as a convenient tool to investigate the sensitivity of methane production under a wide range methanation reaction rates with and without catalysts. From this sensitivity study, it can be learned that the concept of implementing direct methanation in a once-through entrained-flow gasifier may not be attractive due to competitions of other reactions in a high-temperature environment. The production of SNG is limited to about 18% (vol) with catalytic reaction with a pre-exponential factor A in the order of 107. A further increase of the value of A to 1011 doesn’t result in more production of SNG. This SNG production limit could be caused by the high-temperature and short residence time (3–4 seconds) in the entraind-flow gasifier.


2021 ◽  
Vol 4 (s1) ◽  
Author(s):  
Paolo Milazzo ◽  
Roberta Gori ◽  
Alessio Micheli ◽  
Lucia Nasti ◽  
Marco Podda

We present in silico modeling methods for the investigation of dynamical properties of biochemical pathways, that are chemical reaction networks underlying cell functioning. Since pathways are (complex) dynamical systems, in-silico models are often studied by applying numerical integration techniques for Ordinary Differential Equations (ODEs), or stochastic simulation algorithms. However, these techniques require a rather accurate knowledge of the kinetic parameters of the modeled chemical reactions. Moreover, in the case of very complex reaction networks, in silico analysis can become unfeasible from the computational viewpoint. Consequently, in the last few years several approaches have been proposed that focus on estimating or predicting dynamical properties from the analysis of the structure of the biochemical pathway. This means that the analysis focuses more on the interaction patterns than on the kinetic parameters, and this usually makes it possible to deduce the role of each molecule and how each molecule qualitatively influences each other, by abstracting away from quantitative details about concentrations and reaction rates.


Author(s):  
Alessandro Filisetti ◽  
◽  
Alex Graudenzi ◽  
Chiara Damiani ◽  
Marco Villani ◽  
...  

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