Complex reaction system modeling

A complex reaction system cannot be considered completely in most cases. The number of simplifications required for the model increases with the growing complexity of the system. 

Braun et al. (1988) developed a computer program named ACUCHEM for modeling complex reaction systems. 

Braun, W., Herron, J.T., and Kahanar, D.K., ACUCHEM a computer program for modeling complex reaction systems, Int.. Chem. Kinetics, 20, 51-62, 1988. 

The examples presented in Section 8.3 demonstrate this synergy in an approach using calorimetry and IR-ATR spectroscopy. For the hydrolysis of acetic anhydride, the combination of the two analytical techniques enabled a differentiation between the heat effect due to the chemical reaction and that due to a physical phenomenon - in this case, mixing. Due to this separation of the physical heat effect, a more reliable value for the chemical heat effect was obtained. For the sequential epoxidation of 2,5-di-fert-butyl-l,4-benzoquinone, the importance of selection of an appropriate kinetic model has been demonstrated. For complex reaction systems, several models can be postulated. The appropriateness of these models can then be tested on the basis of experimental data. Combined analytical techniques provide an enriched data set for this purpose as has been demonstrated for this example. After the selection of the most appropriate model, the corresponding parameters can be used... 

It is clear that the model and ADM can be used for the analysis of the PBE reactor in the more complex reaction system. 

Equation (21) represents one of the best approaches to the modeling of complex reaction systems providing a satisfactory representation for many rate processes (see, e.g., Refs. [19, 76-78]). It has gained widespread acceptance in various chemical and reactor engineering areas [79], and is also recommended for use in the modeling of reactive separation operations [14, 78]. For the design of large industrial reactive separation units, the analytical solution (Eqs. (19), (20)) can be considered as a useful simplification as compared to numerical methods. 

Neurock, M., C. Libanati, A. Nigam, and M.T. Klein, Monte Carlo Simulation of Complex Reaction Systems Molecular Structure and Reactivity in Modelling of Heavy Oils., Chem. Eng. Sci., 45, (8), 2083-2088,1990. 

Modeling Complex Reaction Systems in Fluidized-Bed Reactors... 

In the present paper Werther s two-phase model for fluidized bed reactors is applied to the synthesis of maleic anhydride as an example of a complex reaction system. Based on experimental data found in the literature two process routes differing in the feedstocks used were investigated. In both cases the model is able to describe the behaviour of the fluid bed reactors including the scale-up effects. 

Among the many mathematical models of fluidized bed reactors found in the literature the model of Werther (J ) has the advantage that the scale-dependent influence of the bed hydrodynamics on the reaction behaviour is taken into account. This model has been tested with industrial type gas distributors by means of RTD-measurements (3)and conversion measurements (4), respectively. In the latter investigation (4) a simple heterogeneous catalytic reaction i.e. the catalytic decomposition of ozone has been used. In the present paper the same modelling approach is applied to complex reaction systems. The reaction system chosen as an example of a complex fluid bed reaction is the synthesis of maleic anhydride (Figure 1). 

Heterogeneously catalyzed reactions are usually studied under steady-state conditions. There are some disadvantages to this method. Kinetic equations found in steady-state experiments may be inappropriate for a quantitative description of the dynamic reactor behavior with a characteristic time of the order of or lower than the chemical response time (l/kA for a first-order reaction). For rapid transient processes the relationship between the concentrations in the fluid and solid phases is different from those in the steady-state, due to the finite rate of the adsorption-desorption processes. A second disadvantage is that these experiments do not provide information on adsorption-desorption processes and on the formation of intermediates on the surface, which is needed for the validation of kinetic models. For complex reaction systems, where a large number of rival reaction models and potential model candidates exist, this give rise to difficulties in model discrimination. 

The information needed about the chemical kinetics of a reaction system is best determined in terms of the structure of general classes of such systems. By structure we mean quahtative and quantitative features that are common to large well-defined classes of systems. For the classes of complex reaction systems to be discussed in detail in this article, the structural approach leads to two related but independent results. First, descriptive models and analyses are developed that create a sound basis for understanding the macroscopic behavior of complex as well as simple dynamic systems. Second, these descriptive models and the procedures obtained from them lead to a new and powerful method for determining the rate parameters from experimental data. The structural analysis is best approached by a geometrical interpretation of the behavior of the reaction system. Such a description can be readily visualized. 

Just as for monomolecular systems, the equilibrium points are structural features that play a central role in the discussion of general complex reaction systems. It is not, however, necessary to introduce them into the system as explicit basic assumptions or to introduce them by means of thermodynamics or statistical mechanics they arise as a consequence of some much more primitive concepts, which are always included in the basic models for closed reaction systems and for many open systems as well. The reader may ask why raise the question as long as the existence of the equilibrium points are assured by some known principles such as those provided by thermodynamics the reason is that a new point of view and an appreciation of the consequences of implicitly and explicitly known basic characteristics often reveal to us the path to a better understanding of nature and to the solution of a particular problem. 

Neurock, M., Libatani, C., Nigam, A., and Klein, M. T., Monte Carlo simulation of complex reaction systems Molecular structure and reactivity in modelling heavy oils, Chem. Eng. Sci. 45(8), 2083-2088 (1990). 

Source R. J. Quann and S. B. Jaffe, Building Useful Models of Complex Reaction Systems in Petroleum Refining, Chemical Engineering Science 51 1615-1635 (1996). With permission. 

Successful kinetic modeling of this complex reaction system can be made using Langmuir-Hinshelwood kinetics and lumping the appropriate species. 

The three-phase model is readily extended to more complex reaction systems if one is willing to endure the algebraic complications. This was worked out [O. Levenspiel, N. Baden and B.D. Kulkarni, Ind. Eng. Chem. Process Design Devel., 17, 478 (1978)] for the classical Denbigh reaction sequence. 

Frenklach, M., Packard, A., Seiler, P, Feeley, R. Collaborative data processing in developing predictive models of complex reaction systems, Int. J. Chem. Kinet. 2004, 36, 57-66. 

Rate constant results from QRRK/Master Equation analysis are shown to accurately reproduce (model) experimental data on several complex systems. They also provide a reasonable method to estimate rate constants for numerical integration codes by which the effects of temperature and pressure can be evaluated in complex reaction systems. 

Our objective has been to develop a procedure through which fundamental properties of reversible complexation reaction systems can be used to predict and to optimize FT of gases. This procedure includes the selection of carriers, the measurement of the relevant physical properties (RPP) and fluxes, the use of an optimization model to identify factors that limit the transport, and the modification of the carrier to improve the facilitation. 

Braun, W. Herron, J.T. Kahaner, D. ACUCHEM Computer Program for Modelling Complex Reaction Systems National Bureau of Standards Gaithersburg, Maryland, 1986. 

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