KINPTR

Byung C. Choi, KINPTR (Mobil s Kinetic Reforming Model) A Review of Mobil s Industrial Process Modeling Philosophy... 

KINPTR (MOBIL S KINETIC REFORMING MODEL) A REVIEW OF MOBIL S INDUSTRIAL PROCESS MODELING PHILOSOPHY... 

Over 30 man-years of effort were involved in developing the model, which is named KINPTR, an acronym for kinetic platinum reforming model. Since its development, KINPTR has had a major impact in Mobil s worldwide operations. It can be accessed by personnel at each of Mobil s locations throughout the world. Input requirements are simple and convenient making it very user friendly. Only feed characteristics, product quality targets, process configuration information, and process conditions are required for input. Output is informative and detailed. Overall and detailed yields, feed and product properties, and reactor performance data are given in the output. 

In this chapter the following topics will be reviewed KINPTR s start-of-cycle and deactivation kinetics, the overall program structure of KINPTR, the rationale for the kinetic lumping schemes, the model s accuracy, and examples of KINPTR use within Mobil. As an example, the detailed kinetics for the C6 hydrocarbons are provided. 

KINPTR is an overall process model thus it simulates all important aspects of the process which affect performance. In order to lay a foundation for upcoming discussions related to KINPTR development, the important aspects of naphtha reforming—chemistry, catalysis, and reactor/hardware design—will be summarized. More extensive reviews are available in the literature (1-3). 

The validity of these assumptions will be demonstrated by KINPTR s ability to predict the wide range of commercial reformer performance and feedstock with no additional parameters. 

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... 

KINPTR Start-of-Cycle Kinetic Lumps (Index)... 

Complete reforming kinetics have been developed for several commercial catalysts, including those used in Mobil reformers. Since KINPTR affects Mobil s business strategy, the complete reforming kinetics are proprietary. However, as an example, KINPTR C6 kinetics will be presented for UOP s R16H platinum-rhenium-alumina catalyst. Both the hydrocarbon conversion and the deactivation equations [Eqs. (36), (40)] can be directly applied to the C6 system. For the C6 hydrocarbon conversion, Eq. (40) becomes... 

KINPTR Real-Time Activity Parameters for C6 System [Eqs. (50)—(52)] and Absorption Constants... 

With respect to catalyst contact time, the effects of temperature and pressure on the yields are shown in Figs. 18, 19, and 20. Activity (as measured by the C5- gas make) is a strong function of temperature, as shown in Figs. 18 and 19. Again, the higher-temperature operation favors benzene formation. KINPTR s prediction of activity as a function of pressure is shown in Fig. 20. Lower-pressure operation favors the yield of benzene. 

Note that in Fig. 18, KINPTR s prediction of C5- falls below the data points. However, when one considers the large temperature and pressure effect on activity in the C6 system and the fact that these same C6 kinetics are used in KINPTR to make predictions for all reforming feedstocks (full-range naphthas, pure components, etc.), the predictions are certainly acceptable. 

Aging Predictions (KINPTR) H Hexane Aging Condltlone... 

As mentioned in the previous discussions of deactivation, catalyst aging is very composition dependent. Thus, catalyst state at a given time on stream will vary with axial distance in a plug flow reactor. This is shown in Fig. 22. Benzene and methylcyclopentane compositions as a function of time on stream are shown at 20% through the catalyst bed and at the end of the bed. KINPTR predicts the catalyst state gradient in the reactor. 

Fig. 23. KINPTR process model description (with module names).

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