Reaction conditions reactor modeling

Note, however, that, in the case of fundamental models, there is not always a need to discriminate among rival models since, often, only a single model has been built up. Furthermore, the best criterion of the quality of a model is the consistency of fundamental parameter estimates with other values obtained by means of several methods under a large range of experimental conditions. Let us not be misled about the principle enemy the systematic errors both in experiments and in reaction and reactor models. 

The glycolysis of PETP was studied in a batch reactor at 265C. The reaction extent in the initial period was determined as a function of reaction time using a thermogravimetric technique. The rate data were shown to fit a second order kinetic model at small reaction times. An initial glycolysis rate was calculated from the model and was found to be over four times greater than the initial rate of hydrolysis under the same reaction conditions. 4 refs. 

Empirical grey models based on non-isothermal experiments and tendency modelling will be discussed in more detail below. Identification of gross kinetics from non-isothermal data started in the 1940-ties and was mainly applied to fast gas-phase catalytic reactions with large heat effects. Reactor models for such reactions are mathematically isomorphical with those for batch reactors commonly used in fine chemicals manufacture. Hopefully, this technique can be successfully applied for fine chemistry processes. Tendency modelling is a modern technique developed at the end of 1980-ties. It has been designed for processing the data from (semi)batch reactors, also those run under non-isothermal conditions. 

Before collecting data, at least two lean/rich cycles of 15-min lean and 5-min rich were completed for the given reaction condition. These cycle times were chosen so as the effluent from all reactors reached steady state. After the initial lean/rich cycles were completed, IR spectra were collected continuously during the switch from fuel rich to fuel lean and then back again to fuel rich. The collection time in the fuel lean and fuel rich phases was maintained at 15 and 5 min, respectively. The catalyst was tested for SNS at all the different reaction conditions and the qualitative discussion of the results can be found in [75], Quantitative analysis of the data required the application of statistical methods to separate the effects of the six factors and their interactions from the inherent noise in the data. Table 11.5 presents the coefficient for all the normalized parameters which were statistically significant. It includes the estimated coefficients for the linear model, similar to Eqn (2), of how SNS is affected by the reaction conditions. 

In this paper we will first describe a fast-response infrared reactor system which is capable of operating at high temperatures and pressures. We will discuss the reactor cell, the feed system which allows concentration step changes or cycling, and the modifications necessary for converting a commercial infrared spectrophotometer to a high-speed instrument. This modified infrared spectroscopic reactor system was then used to study the dynamics of CO adsorption and desorption over a Pt-alumina catalyst at 723 K (450°C). The measured step responses were analyzed using a transient model which accounts for the kinetics of CO adsorption and desorption, extra- and intrapellet diffusion resistances, surface accumulation of CO, and the dynamics of the infrared cell. Finally, we will briefly discuss some of the transient response (i.e., step and cycled) characteristics of the catalyst under reaction conditions (i.e.,... 

In Table 17.2, fA (for the reaction A products) is compared for each of the three flow reactor models PFR, LFR, and CSTR. The reaction is assumed to take place at constant density and temperature. Four values of reaction order are given in the first column n = 0,1/2,1, and 2 ( normal kinetics). For each value of n, there are six values of the dimensionless reaction number MAn = 0, 0.5, 1, 2, 4, and °°, where MAn = equation 4.3-4. The fractional conversion fA is a function only of MAn, and values are given for three models in the last three columns. The values for a PFR are also valid for a BR for the conditions stated, with reaction time t = t and no down-time (a = 0), as described in Section 17.1.2. 

In this chapter, we develop some guidelines regarding choice of reactor and operating conditions for reaction networks of the types introduced in Chapter 5. These involve features of reversible, parallel, and series reactions. We first consider these features separately in turn, and then in some combinations. The necessary aspects of reaction kinetics for these systems are developed in Chapter 5, together with stoichiometric analysis and variables, such as yield and fractional yield or selectivity, describing product distribution. We continue to consider only ideal reactor models and homogeneous or pseudohomogeneous systems. 

The first-order, liquid-phase reaction of lidocaine (A) to monoethylglycinexylidide (MEGX) is conducted in a reactor (the liver) with arbitrary flow conditions that is to be modeled by the TIS reactor model with N tanks. Derive an expression for c, the steady-state outlet concentration from the Nth tank, in terms of system parameters. 

As Figure 11.26 undoubtedly demonstrates, the deviation between the same catalytic material under practically identical reaction conditions is in the range of 2% conversion (if appropriate measures are taken this error can be reduced to 0.5%). These experimental data points lead to the important verification of the above-discussed CFD modeling results and confirm the assumption of realizing identical reaction conditions over the whole reactor system independent from the position of a catalyst to be tested. By testing inert carrier material in reactor column number 8, the inertness and catalytic inactivity of the reactor steel can be proven. 

Two approaches are common in modelling the SSP process. For the first approach, an overall reaction rate is used which describes the polycondensation rate in terms of the increase of intrinsic viscosity with time. Depending on the size and shape of the granules, the reaction temperature, the pressure, and the amount and type of co-monomers, the overall polycondensation rate lies between 0.01 and 0.03 dL/g/h. The reaction rate has to be determined experimentally and can be used for reactor scale-up, but cannot be extrapolated to differing particle geometry and reaction conditions. 

The Rh catalysed carbonylation of MeOH to AcOH was studied at Monsanto by HP IR under working reaction conditions using a short path length transmission cell coupled to a stirred reactor [12]. The presence of [Rh(CO)2l2] as the principal Rh species was generally noted. Consistent with the model studies and the kinetics of the carbonylation reaction, which tended to first order in total Rh and Mel, the rate controlling step was of course the reaction of [Rh(CO)2l2r with Mel. 

The use of a monolithic stirred reactor for carrying out enzyme-catalyzed reactions is presented. Enzyme-loaded monoliths were employed as stirrer blades. The ceramic monoliths were functionalized with conventional carrier materials carbon, chitosan, and polyethylenimine (PEI). The different nature of the carriers with respect to porosity and surface chemistry allows tuning of the support for different enzymes and for use under specific conditions. The model reactions performed in this study demonstrate the benefits of tuning the carrier material to both enzyme and reaction conditions. This is a must to successfully intensify biocatalytic processes. The results show that the monolithic stirrer reactor can be effectively employed in both mass transfer limited and kinetically limited regimes. 

Industrially relevant consecutive-competitive reaction schemes on metal catalysts were considered hydrogenation of citral, xylose and lactose. The first case study is relevant for perfumery industry, while the latter ones are used for the production of sweeteners. The catalysts deactivate during the process. The yields of the desired products are steered by mass transfer conditions and the concentration fronts move inside the particles due to catalyst deactivation. The reaction-deactivation-diffusion model was solved and the model was used to predict the behaviours of semi-batch reactors. Depending on the hydrogen concentration level on the catalyst surface, the product distribution can be steered towards isomerization or hydrogenation products. The tool developed in this work can be used for simulation and optimization of stirred tanks in laboratory and industrial scale. 

In chapters 2-5 two models of oscillatory reaction in closed vessels were considered one based on chemical feedback (autocatalysis), the other on thermal coupling under non-isothermal reaction conditions. To begin this chapter, we again return to non-isothermal systems, now in a well-stirred flow reactor (CSTR) such as that considered in chapter 6. 

The discussion above explains why basic information on sorption and diffusion under the reaction conditions, especially at elevated pressures, is required for kinetic and mass- and heat- transfer modelling of catalytic polymerization reactors. If such information is sufficiently available, one should be able, for example, to compare the kinetics of gas-phase and slurry-processes directly by taking into account both gas solubilities in swollen polymers and the hydrocarbons used in slurry processes. 

One of the major drawbacks to defining the influence of the feedstock on the process is that the research with respect to feedstocks has been fragmented. In every case, a conventional catalyst has been used, and the results obtained are only valid for the operating conditions, reactor system, and catalyst used. More rigorous correlation is required and there is a need to determine the optimum temperature for each type of sulfur compound. In order to obtain a useful model, the intrinsic kinetics of the reaction for a given catalyst should also be known. In addition, other factors that influence the desulfurization process such as (1) catalyst inhibition or deactivation by hydrogen sulfide, (2) effect of nitrogen... 

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