Deactivation models

Thus it is important to obtain reliable models for catalyst deactivation and to investigate, whether it is possible to decouple the deactivation model from the kinetic model or if it is necessary to treat the catalyst deactivation as one of the surface reactions on the catalyst [45]. 

Figure 4.6 Deactivation model for idealized cavity. (From Chen, 1968. Copyright 1968 by American Society of Mechanical Engineers, New York. Reprinted with permission.)...

The deactivation process of enzymes can be classified generally into first-order and non-first-order processes. The first-order model is often sufficient for describing the deactivation, especially in case of highly stable enzymes in microemulsions, like lipases. For a lot of enzymes the activity decay in microemulsions has to be fitted to the deactivation model which involves an active intermediate in order to describe the deactivation processes qualitatively [34,100-102] ... 

In commercial aging simulations, KINPTR s deactivation model is used to predict cycle lengths (time between catalyst regenerations) and reactor inlet temperature requirements with time on stream to maintain target reformate... 

The effect of combining the deactivation model with the simple catalytic sequence of the Michaelis-Menten relation is shown below ... 

Despite the increase in zeolite content, catalyst B deactivated faster than A requiring only four and a half hours to reach 66 FAI activity. Using our catalyst deactivation model which allows us to accurately translate laboratory catalyst deactivation to commercial make-up predictions (10), we estimate a much higher makeup rate is required for catalyst B to achieve the same activity as catalyst A (see Table I). 

All the previously cited models and works also consider, and some explicitly cite, this assumption—that the catalyst activity varies with time-on-stream (or with coke concentration [12]) in the same manner or with the same deactivation function (VO for all reactions in the network. That is, a nonselective deactivation model is always used. Corella et al. (16) have recently demonstrated that in the FCC process this assumption is not true and that it would be better to use a selective deactivation model. Another work (17) also shows how this consideration, when applied to catalytic cracking, influences the yield-conversion curves. Nevertheless, to avoid an additional complication, we will use in this chapter a nonselective deactivation model with the same a—t kinetic equation and deactivation function (VO for all the cracking reactions of the network. 

FIG. 19-19 Relative changes in conversion versus temperature behavior for various deactivation models. (Ftg. 5.4 in Heck, Farrauto, and Gulati, Catalytic Air Pollution Control Commercial Technology, Wiley-Interscience, 2002.)... 

The changing catalyst porous texture is modelled using a Bethe network originating from percolation concepts. Preliminary results indicate that reliable metal deposition profiles and catalyst life-time predictions can be made by the proposed catalyst deactivation model. 

As observed from simulations, the formulated HDM catalyst deactivation model based on the percolation approach can predict metal deposition profiles and catalyst life time. In the industrial application of hydrotreating catalysts metal deposition maxima are observed in spent catalysts, which is in qualitative agreement with the developed model... 

The kinetic and deactivation models were fitted by non-linear regression analysis against the experimental data using the Modest software, especially designed for the various tasks -simulations, parameter estimation, sensitivity analysis, optimal design of experiments, performance optimization - encountered in mathematical modelling [6], The main interest was to describe the epoxide conversion. The kinetic model could explain the data as can be seen in Fig. 1 and 2, which represent the data sets obtained at 70 °C and 75°C, respectively. The model could also explain the data for hydrogenated alkyltetrahydroanthraquinone. 

Gomez JL, Bodalo A, Gomez E et al (2008) A covered particle deactivation model and an expanded Dunford mechanism for the kinetic analysis of the immobilized SBP/phenol/ hydrogen peroxide system. Chem Eng J 138 460-473... 

Deactivation parameters obtained by plotting ln[(l — a) a)] versus time are listed in Table XIX for a number of nickel and nickel bimetallic catalysts. The fact that these plots were generally linear confirms that these data are fitted well by this deactivation model. These data, which include initial site densities for sulfur adsorption, deactivation rate constants, and breakthrough times for poisoning by 1-ppm H2S at a space velocity of 3000 hr-1 provide meaningful comparisons of sulfur resistance and catalyst life for both unsupported and supported catalysts. Table XIX shows that the... 

The work of Rostrup-Nielsen is very informative, but it also raises a number of important questions. How can more realistic temperature and concentration profiles through the reactor be incorporated into a reactor deactivation model Could experimental measurements be performed to determine how sulfur is actually distributed in the catalyst pellets and in the bed and how this distribution changes as a function of time at various H2S concentrations Would it be worthwhile to consider a modification of the model by Wise and co-workers (195,233) for steam reforming in which pore... 

Those deactivation models accounting for both coke and metal sulfides are rather simple. Coke and metals foul residue hydrodesulfurization catalysts simultaneously via different processes, and decrease both intrinsic reaction rate and effective diffusivity. They never uniformly distribute in the commercial reactors. We have examined the activity and diffusivity of the aged and regenerated catalysts which were used at the different conditions as well as during the different periods. This paper describes the effects of vacuum residue conversion, reactor position, and time on-stream on the catalyst deactivation. Two mechanisms of the catalyst deactivation, depending on residue conversion level and reactor position, are also proposed. 

Key-issue in hydrodemetallisation (HDM) process design and operation is the development of catalyst deactivation models which give reliable predictions of catalyst life-time and activity, thus providing a tool for designing optimized catalysts. 

The general approach for modelling catalyst deactivation is schematically organised in Figure 2. The central part are the mass balances of reactants, intermediates, and metal deposits. In these mass balances, coefficients are present to describe reaction kinetics (reaction rate constant), mass transfer (diffusion coefficient), and catalyst porous texture (accessible porosity and effective transport properties). The mass balances together with the initial and boundary conditions define the catalyst deactivation model. The boundary conditions are determined by the axial position in the reactor. Simulations result in metal deposition profiles in catalyst pellets and catalyst life-time predictions. 

A Catalyst Deactivation Model for Residual Oil Hydrodesulfiirization and Application to Deep Hydrodesulfurization of Diesel Fuel... 

The catalyst deactivation model developed in this paper accounts for the nonsteady-state activity of commercial catalysts measured using accelerated sulfur aging experi-... 

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