Reforming kinetics kinetic lumps

Start-of-cycle kinetic lumps in KINPTR are summarized in Table V. A C5-light gas lump is required for mass balance. Thirteen hydrocarbon lumps are defined. The reforming kinetic behavior can be modeled without splitting the lumps into their individual isomers (e.g., isohexane and n-hexane). Also, the component distribution within the C5- lump can be described by simple correlations, as discussed later. The start-of-cycle reaction network that defines the interconversions between the 13 kinetic lumps is shown in Fig. 9. This reaction network results from kinetic studies on pure components and narrow boiling fractions of naphthas. It includes the basic reforming reactions... 

Reaction rates for the start-of-cycle reforming system are described by pseudo-monomolecular rates of change of the 13 kinetic lumps. That is, the rates of change of the lumps are represented by first-order mass action kinetics with the same adsorption isotherm applicable to each reaction step. Following the same format as Eq. (4), steady-state material balances for the hydrocarbon lumps are derived for a plug-flow, fixed bed catalytic reformer. A nondissociation, Langmuir-Hinshelwood adsorption model is employed. Steady-state material balances written over a differential fractional catalyst volume dv are the following ... 

While the 13 hydrocarbon lumps accurately represent the hydrocarbon conversion kinetics, they must be delumped for the deactivation kinetics. In addition, delumping is necessary to estimate many of the product properties and process conditions important to an effective reformer process model. These include H2 consumption, recycle gas H2 purity, and key reformate properties such as octane number and vapor pressure. The following three lump types had to be delumped the C5- kinetic lump into Cl to C5 light gas components, the paraffin kinetic lumps into isoparaffin and n-paraffin components, and the Cg+ kinetic lumps into Cg, C9, C10, and Cn components by molecular type. 

To model the performance of the autothermal reformer, kinetics from the literature that had been determined for the catalytic combustion of methane over a platinum-based catalyst and for steam reforming over nickel-based catalyst were combined and fitted to the experimental data of Flytzani-Stephanopoulos et al. [153]. The water-gas shift reaction was assumed to reach thermodynamic equilibrium under all conditions in the reformer reactor, which is usually the case in reformers. Methane formation was not considered. Because catalyst pellets had been used for the determination of the kinetics, diffusion limitations were to be expected. They had been lumped into the kinetic models. The hot spot formation usually observed at... 

Research on mathematical lumping has focused on constructing kinetic lumps and determining the conditions under which the lumped system can at least approximate the underlying unlumped system. In so doing, one often needs to impose some constraints. Take catalytic reforming as an example. Kinetically and analytically, it makes sense to lump iso and normal paraffins together, but these hydrocarbons have so different octane numbers that they should be separated. 

Figure 5.54 shows the Feed Data tab from the Reformer sub-model. The Feed Type is a basic set of relationships and initial values for the all kinetic lumps in the reactor model. Aspen HYSYS uses bulk property information such as density, distillation curves and total PNA content in conjunction with the feed type to predict the composition of feed lumps to the model. The Default type is sufficient for hght-to-heavy naphtha. However, there is no guarantee that a particular feed type represents the actual feed accurately. Aspen HYSYS will attempt to manipulate the feed composition to satisfy bulk property measures given. In general, we advise users to develop a few sets of compositional analysis to verify the kinetics lumps calculated by Aspen HYSYS. We discuss a process to verify these lumps later. 

After saving the Calibration, we shordd put the solver in holding mode to make sure that Aspen HYSYS exported the calibration factors properly. We will return the Reformer Sub Flowsheet environment We recommend that users go through each one of the tabs in Reformer Sub Flowsheet environment to make sure that the input data has not changed. It is also important to make sure that the basis for the kinetic lumps is same as what was chosen initially (In this work, we always use wt.%, see Figure 5.93). We can release the solver to allow Aspen HYSYS to solve the model as shown in Figure 5.92. 

In a model for catalytic reforming of gasoline, cited in problem P2.03.26, some 300 chemical species are identified, broken up in one case into 13 lumps characterized by carbon number and hydrocarbon class. The kinetic characteristics of such lumps are proprietary information. 

The expanded set of 34 lumps necessary to define the reforming process is shown in Table VIII. Note that this 34-lump set is sufficient for both start-of-cycle and aging kinetics. 

Although the kinetics of reforming of pure paraffins may be satisfactorily represented by LHHW-type models, such models are difficult to apply for real fuels, such as diesel or gasoline. For such complex systems, it may be more practical to use pseudo-homogeneous kinetics. For example, hydrocarbon fuel components can be lumped in groups with similar properties and kinetic behaviors for example, paraffins are also grouped into lumped reactions. However, the levels of simplification must be carefully evaluated to make them consistent with the final aim of the kinetic model. 

A kinetic model for the naphtha catalytic reforming process, which utilizes lumped mathematical representation of the reactions that take place, is presented. The reaction are written in terms of isomers of the same nature, which range from 1 to 11 atoms of carbon for paraffins, and from 6 to 11 carbon atoms for naphthenes and aromatics. The kinetic parameters values were estimated using experimental information obtained in a fixed-bed pilot plant. The pilot reactor was loaded with different amounts of catalyst in order to simulate a series of three reforming reactors. The reformate composition calculated with the proposed model agrees very well with experimental information. 

The last sets of correlations we will address are composition correlations. These correlations identify chemical composition in terms of total paraffin, naphthene and aromatic (PNA) content of a particular feed based on key bulk measurements. These correlations are useful in two respects. First, we use these correlations to screen feeds to different refinery reaction units. For example, we may wish to send a more paraffinic feed to a reforming process when we want to increase the yield of aromatic components from the refinery. Secondly, these types of correlations form the basis of more detailed lumping for kinetic models that we will discuss at great length in subsequent chapters of this book. We will use these types of correlations to build extensive component lists that we can use to model refinery reaction processes. 

Mechanistic and experimental studies generally result in the creation of a kinetic network that quantitatively describes the path a particular reactant takes. Given the complexity of the reforming reactions and the number of species involved, many researchers have taken a lumped approach towards describing the kinetics. In a lumped approach, many different molecules are placed into a single group or lump. The reaction kinetics then assumes that all species in a lump behave identically. Recently, some researchers have presented models that involve hundreds of reaction species and thousands of reactions [16,18], However, there is little published information about these complex kinetic models validated against industrial operation. 

Table 5.5 summarizes the key features in reported unit-level models (using lumped kinetics) applied to reforming processes. We have only included studies where the authors compare their results to pilot-plant or industrial data. In addition, we include those studies where the authors use the model for case studies and plant optimization. 

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