Correlations Riazi

Riazi-Whitson They presented a generahzed correlation in terms of viscosity and molar density that was apphcable to both gases and liqmds. The average absolute deviation for gases was only about 8 percent, while for liquids it was 15 percent. Their expression relies on the Chapman-Enskog correlation [Eq. (5-194)] for the low-pressure diffusivity and the Stiel-Thodos correlation for low-pressure viscosity ... 

The flash of 27-multicomponent mixtures in the high pressure hydrocracking separator were calculated in this paper. To those polar components, special treatment were used. The properties M, Tc, Pc of petroleum fractions were calculated by Riazi and Daubeit [11](1980) correlations, and co was calculated by Lee and Keslefs correlation. A group of typical results... 

Critical properties and acentric factor. The correlations for critical properties discussed in Chapter 3 are based on the properties of light hydrocarbons (general correlation of critical properties and acentric factors for PNA species. Pan, et al. (1997b) have modified the correlation of Riazi and Al-Sahhaf for the critical pressure of PNA species with a molecular weight of more than 300 g/gmole ... 

Riazi, M.R. Al-Sahhaf, T., Physical Properties of n-Alkanes and n-Alkyl Hydrocarbons Application to Petroleum Mixtures , Ind. Eng. Chem. Res., 1995, 34(11), 4145 148. Marano, J.J. Holder, G.D. General Equation for Correlating the Thermophysical Properties of n-Paraffms, n-Olefms, and Other Homologous Series. 2. Asymptotic Behavior Correlations for PVT Properties , Ind. Eng. Chem. Res., 1997, 36(5), 1895-1907. 

In addition thermophysical properties required for modeling purposes, a complete model must also make predictions regarding several fuel properties routinely measured at the refinery. Typically these fuel or product properties include measurements such as flash point, freeze point, cloud point and paraffin-naphthene-aromatic (PNA) content. These properties not only serve as indicators of product quality and distribution, but may also be limited by government or internal refinery regulations. We can often justify the use of process modeling in the refinery by making sure that models also include predictions of these useful fuel properties. We will briefly discuss two approaches in this area and give concrete examples with flash point, freeze point and PNA content We choose these particular properties because they display characteristics common to many types of fuel property correlation methods. We refer the reader to API standards [35] and Riazi [4] for more detailed expositions on various types of correlations for fuel properties not discussed in this section. 

Compositional information is quite useful to the refiner and many correlations are available in the literature that attempt to correlate PNA content to various bulk measurements. In general, these correlations rely on density or specific gravity, molecular weight, distillation curve and one or more viscosity measurements. The n-d-M (refractive index, density, and molecular weight) [4], API/Riazi-Daubert [35,4], and TOTAL [19] correlations are just a few of the correlations available. 

The Riazi-Daubert correlation relies on the most directly observed information and we expect it to show the smallest deviation from measured values. The other correlations require parameters (aniline point, etc.) that may not be routinely measured for all feeds. The Riazi-Daubert correlation takes the form ... 

We have extended the correlation by Riazi [4] to include the specific gravity, refractive index and the stream viscosity. Our updated correlation is given by ... 

The reader may find additional correlations for other fuel properties from the API handbook [2] and work by Riazi [4],... 

Once we create the blend in Aspen HYSYS Oil Manager, we can make several observations about the calculated properties and methods used to develop constants (critical properties, heat capacity, etc.). Figure 2.20 shows the relevant methods used to calculate the hypothetical component properties. Documentation from AspenTech [13] and work by Riazi [2,4] summarizes most of these correlations. In general, the default values work for a variety of different crudes. If necessary, it is also possible to change these correlations to reflect the non-standard crude. We may update the correlations by creating a new correlation set as shown in Figure 2.21. We have also supplied a spreadsheet to calculate these critical properties by various methods. 

As mentioned earlier, we must also be able to infer the paraffin, naphthene and aromatic (PNA) composition of each boiling-point range given certain measured bulk properties to completely map feed information to kinetic lumps. The API (Riazi-Daubert) [54, 55] is a popular chemical composition correlation that takes the form ... 

We present an alternate correlation in Eqs. (4.11) and (4.12). Our correlation extends the original correlation from Riazi [54,55] by including specific gravity (SG) as an additional parameter and providing different sets of correlation coefficients (a, b, c and d) for different boiling-point ranges. 

Calculate Pseudocomponent molecular weight using the correlation of Riazi... 

Use correlations from Riazi [55] to estimate the viscosity of the lump. ( )... 

If the reactor lumps resemble real measured products (e.g., A8), then it is sufficient to use the known properties of one of the compounds comprising the lump as the properties of the lump. The kinetic lumps in this work resemble real lumps, so we use known compound properties. If this information is not available, we can use Riazi s correlations [42] to estimate the relevant critical properties for... 

Pre-compute the individual properties of each of the lumps (i.e. associate each lump with normal boiling point, standard liquid density, molecular weight, etc.). It is possible to compute each of properties using correlations from Riazi [42]. 

There are various correlations to estimate pseudocomponent molecular weight based on standard liquid density and TBP. Riazi [37] presents a comprehensive review and comparison of published correlations. 

Estimate critical temperatures (T ), critical pressures (P ), critical volumes (V ) and acentric factors ([Pg.395]

For V(, to be consistent with the estimations of and P, we also apply the correlation developed by Riazi and Daubert [41] ... 

Several books have been published during the years, dealing with detailed explanations about the physical and chemical characterization of petroleum (Speight, 1999, 2001) as well as methods and correlations for prediction of petroleum properties (Riazi, 2004). Therefore, this section will not describe in detail all the properties of heavy crude oils, but only those relevant issues that need to be taken into consideration when processing these heavy materials. First, a general overview of the most common physical and chemical properties is summarized. And later, more details are given on those properties and how to calculate them that are crucial for heavy petroleum. 

Most of the correlations for calculating viscosity fail when used for heavy petroleum, because they were developed for light distillates. Glas0 (1980) reported a correlation applicable for petroleum fractions having API gravity higher than or equal to 20 (Riazi, 2005), which is perhaps the most appropriate correlation for petroleum fractions. For heavier oils, errors about 100% could be expected by using the same approach (Dennison, 1998 Riazi, 2005). 

Riazi and Vera (2005) proposed a parafflnic/naphthenic/aromatic compositional model for calculating the solubility of light gases such as methane, ethane, carbon dioxide, and hydrogen in various petroleum and coal liquid fractions under different conditions of temperature and pressure. The proposed method derived correlations where the solubility of a gas in a liquid mixture, in terms of its mole fraction, can be calculated from the vapor-liquid equilibrium. 

---