Process simulation pseudocomponents

The process model was built using PETROX, a proprietary sequential-modular process simulator from PETROBRAS. The simulation comprises 53 components and pseudocomponents and 64 unit operation modules, including 7 distillation columns and a recycle stream. All modules are built with rigorous, first-principles models. For optimization applications, PETROX was linked to NPSOL, an SQP optimisation algorithm. 

Table 1.2 Typical boiling-point ranges for pseudocomponents in commercial process simulators.

For any process simulation that involves only vapor-liquid phases, certain key physical and thermodynamic properties must be available for each phase. Table 1.3 lists these properties for all phases. We can typically obtain these properties for pure components (i.e. n-hexane, n-heptane, etc.) from widely available databases such as DIPPR [2]. Commercial process simulation software (including Aspen HYSYS) also provides a large set of physical and thermodynamic properties for a large number of pure components. However, using these databases requires us to identify a component by name and molecular structure first, and use experimentally measured or estimated values from the same databases. Given the complexity of crude feed, it is not possible to completely analyze the crude feed in terms of pure components. Therefore, we must be able to estimate these properties for each pseudocomponent based on certain measured descriptors. 

Once we have obtained the boiling point, density or specific gravity, molecular weight and critical properties of a particular pseudocomponent, we can also generate estimates for other required properties for process simulation shown in Table 1.3. The accuracy of these predictions is largely a function of the accuracy of the molecular weight and critical property predictions. In addition, depending on the thermodynamic method chosen, we may not require any correlations for certain properties. For example, if we choose an equation-of state approach, we do not require any additional correlations for the vapor pressure (Pvap) heat of vaporization (AHvap), since these values will be calculated directly by the equation... 

Fit a new cumulative beta distribution to the updated FCC effluent TBP curve using the initial set of cumulative distribution parameters as a starting guess. Cut this new TBP curve into petroleum pseudo components using methods commonly available in process simulations. In addition, Riazi [55] discusses several strategies to cut a TBP curve into pseudocomponents suitable for fractionation models. 

In process simulation software during the creation of pseudocomponents, which are used together with quadrature techniques for determining the optimal number of pseudocomponents for simulation purposes, for example, characterization of petroleum fractions (Whitson, 1983 Whitson et al.,... 

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