The correct representation of a fuel, in terms of its physical and chemical properties and its combustion kinetics poses, a challenge to modern engine development when state-of-the-art simulation technology is used. In this context, a promising approach is the use of surrogates that emulate the properties of real fuels, where the surrogates are made up of a significantly lower number of components than the original fuels. The goal of this paper is to present an algorithm that can be used to generate surrogates composed of real chemical components, as opposed to pseudo components.The algorithm was developed by simultaneously fitting the True Boiling Point (TBP) curve, the liquid density at 15 ℃ and the cetane number. To illustrate the algorithm, surrogates for four different fuels were generated: a commercially available European diesel and three research diesel proposed by the FACE (Fuels for Advanced Combustion Engines) CRC Research Group. Two of the resulting surrogates were produced on a lab-scale and subjected to laboratory examination. For validation, the experimental data for these two surrogates were compared to those for the target fuels and to data generated by thermodynamic models on the basis of the surrogates' compositions.Both the fitted properties and additional properties, which were not used for fitting, were compared with experimental properties such as the ASTM D86 boiling curve, content of aromatics, flash point, heating value, cloud point, viscosity, and tempera-
Discrete modeling is a concept to establish thermodynamics on Shannon entropy expressed by variables that characterize discrete states of individual molecules in terms of their interacting neighbors in a mixture. To apply this method to condensed-phase lattice fluids, this paper further develops an approach proposed by Vinograd which features discrete Markov-chains for the sequential lattice construction and rigorous use of Shannon information as thermodynamic entropy, providing an in-depth discussion of the modeling concept evolved. The development comprises (1) improved accuracy compared to Monte Carlo data and (2) an extension from a two-dimensional to a three-dimensional simple lattice. The resulting model outperforms the quasichemical approximation proposed by Guggenheim, a frequently used reference model for the simple case of spherical molecules with uniform energetic surface properties. To illustrate its potential as a starting point for developing g E -models in chemical engineering applications, the proposed modeling methodology is extended, using the example of a simple approach for dicelike lattice molecules with multiple interaction sites on their surfaces, to address more realistic substances. A comparison with Monte Carlo simulations shows the model's capability to distinguish between isomeric configurations, which is a promising basis for future g E -model development in view of activity coefficients for liquid mixtures.
Thermodynamic modeling of extensive systems usually implicitly assumes the additivity of entropy. Furthermore, if this modeling is based on the concept of Shannon entropy, additivity of the latter function must also be guaranteed. In this case, the constituents of a thermodynamic system are treated as subsystems of a compound system, and the Shannon entropy of the compound system must be subjected to constrained maximization. The scope of this paper is to clarify prerequisites for applying the concept of Shannon entropy and the maximum entropy principle to thermodynamic modeling of extensive systems. This is accomplished by investigating how the constraints of the compound system have to depend on mean values of the subsystems in order to ensure additivity. Two examples illustrate the basic ideas behind this approach, comprising the ideal gas model and condensed phase lattice systems as limiting cases of fluid phases. The paper is the first step towards developing a new approach for modeling interacting systems using the concept of Shannon entropy.
In this paper a precedently developed surrogate optimization algorithm for fossil fuels, which originally allowed simultaneous fitting of the true boiling point (TBP) curve, the liquid density at 15 °C, and the cetane number, is refined toward its application to biodiesel and its mixtures with fossil diesel. For this purpose, the algorithm is extended (1) to also include fitting of the kinematic viscosity at 40 °C and (2) to account for peculiarities of biodiesel concerning its narrow boiling range and compensation of systematic errors of measured boiling curves. To illustrate these improvements, first, the algorithm is applied to property estimation and surrogate optimization of three different biodiesel fuels, for which surrogates consisting of one to three components are proposed. Second, a surrogate for a commercial European fossil diesel is calculated and produced in lab-scale. Finally, the algorithm is used for surrogate optimization and property estimation of mixtures of biodiesel and fossil diesel, considering fractions of biodiesel of 7% and 20% per volume. It is shown that the improved algorithm is capable of reliably optimizing surrogates for fuels containing both biogenic and fossil components.
For characterization of crude oil and its primary fractions, the generation of substitute mixtures (surrogates) containing only real chemical components is a promising approach. The abandonment of pseudo-components, except for the utmost high-boiling fractions, allows for rigorous application of standard thermodynamic models (e.g., activity coefficients and equations of states), increasing reliability of phase-equilibrium calculations and predictive capabilities using process simulators. In this paper, an improved algorithm for characterization of petroleum fractions with real components is developed and applied to characterization of crude oil and its products through generation of substitute mixtures. The capabilities of emulating the separation behavior of crude oil are verified through a comprehensive analysis of a simulation conducted with real components by comparison to real plant data of an operating crude oil distillation unit (CDU). Additionally, a simulation based on the traditional pseudo-component approach is used for comparison. ■ MOTIVATIONCrude oil is a hydrocarbon mixture containing thousands of individual components ranging from light gases to very heavy, high-boiling components. 1 This mixture of a vast number of components with unknown chemical composition has to be processed in the refineries. Because of the increased need for efficiency, a much deeper understanding of the chemical specificity of refinery streams will be necessary for optimization. 2 A molecular-based characterization of the refinery streams can help to achieve this task. 3 A state-of-the-art approach for crude oil characterization is the pseudo-component approach, which is readily available in commercial simulation programs. Pseudo-components are generated on the basis of measured bulk properties, and all further calculations are based on these artificial components. Within the generation of pseudo-components, especially the estimation of their critical data and the acentric factor is arguable and no single commonly accepted method has been established thus far. 3 Another approach to characterize complex hydrocarbon mixtures is the use of real chemical components instead of pseudo-components. One advantage of using real components is the applicability of rigorous thermodynamic models instead of mainly empirical correlations for property estimation, which might be prone to errors. 4 This allows also for the modeling of fractions and mixtures with non-traditional components, e.g., bio-based products, which are not captured in the original pseudo-component approach. Furthermore, such an approach enables use of reaction kinetics or inclusion of key components within the simulation. 3 Moreover, it is possible to define key components within simulations, where calculations can rely on measured pure component data and interaction parameters. Additionally, the real component approach allows for experimental validation of predictions and applied models. ■ INTRODUCTIONPioneering activities in the generation of substitute mixtures for ...
Common group-contribution and corresponding-state models for the estimation of normal boiling points, vapor pressures, liquid densities, and dynamic viscosities are reviewed in view of their application to fatty acid methyl esters, related fatty acids and triglycerides. Because of the limited representation of measured data for triglycerides, three previously published group-contribution models for normal boiling points, vapor pressures, and dynamic viscosities are extended through the introduction of a new group, representing the backbone structure common to all triglycerides and improving the performance of these models significantly. Conclusions are drawn in view of further refinement of the group-contribution approach for application to complex branched molecules.
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