Multicomponent process simulation

A better insight into composition of phases along the separation process is provided by multicomponent process simulation as it can be carried out with commercial process simulating programs, such as ASPEN-h. As usual, the process is separated into theoretical stages. Normally, ASPEN+ provides thermodynamic models and calculates thermodynamic properties such as the distribution coefficients and separation factors. As the accuracy of these results is not sufficient for a design analysis in many cases, distribution coefficients (and if necessary solubilities) can be provided by a user-defined module which uses empirical correlations for these values. 

In order to determine the height of a column, the height (length) of a theoretical stage must be known. 

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The local composition model (LCM) is an excess Gibbs energy model for electrolyte systems from which activity coefficients can be derived. Chen and co-workers (17-19) presented the original LCM activity coefficient equations for binary and multicomponent systems. The LCM equations were subsequently modified (1, 2) and used in the ASPEN process simulator (Aspen Technology Inc.) as a means of handling chemical processes with electrolytes. The LCM activity coefficient equations are explicit functions, and require computational methods. Due to length and complexity, only the salient features of the LCM equations will be reviewed in this paper. The Aspen Plus Electrolyte Manual (1) and Taylor (21) present the final form of the LCM binary and multicomponent equations used in this work. 

For given a set of data, which isotherm equation (or equations) fits best And what is the impact of the quality of fit on predicted performance Unfortunately, neither qnestion cam be answered fully. It is fair to say that the greater the number of parameters in an equation, the more likely it is to fit well and the better it fits, the more valid will be snbseqnent process simulations. That should be balanced against the statistical significance of the parameters. Finally, the isotherm fit that best accommodates heat effects and multicomponent aspects, if any, will be superior. An example that illustrates different degrees of quality of fit of four equations to one set of data is provided in Section 14.5.4. Specialized programs are available that fit equations and plot the results. 

A set of equilibrium data, determined experimentally, in combination with mass and energy balances enables the calculation concentration profiles along a separation device operated in the countercurrent mode for multicomponent mixtures. ASPEN-h is a commercially available process simulation program. 

Today the design of separation processes is performed almost exclusively with the aid of process simulators by solving the balance equations. In the case of separation processes, apart from the data of the pure substances, reliable information on the phase equilibria of the multicomponent system that is to be separated is required. 

A multicomponent mixture is boiled in a flask at 1 atm. The vapors ate condensed and recovered as a liquid product It is desired to examine the mole fractions of the residual liquid in the flask as vaporization proceeds. Although sketches of the residue curve maps are called for in (b)-(d), a process simulator can be used to prepare the drawings accurately. 

Multicomponent flash distillation is a good place to start learning how to use a process simulator. The problems can easily become so conplicated that you don t want to do them by hand, but are not so complicated that the working of the simulator is a mystery. In addition, the simulator is unlikely to have convergence problems. Although the directions in this appendix are specific to Aspen Plus, the procedures and problems are adaptable to any process simulator. The directions were written for Aspen Plus V 7.2, 2010 but will probably apply with little change to newer versions when they are released. Additional details on operation of process simulators are available in the book by Seider et al. (2009) and in the manual and help for your process simulator. 

Although binary distillation problems can be done conveniently on a McCabe-Thiele diagram. Chapter 6 will show that multicomponent distillation problems are easiest to solve as matrix solutions for simulation problems (the number of stages and feed locations are known). Commercial simulators typically solve all problems this way. Lab 3 in this appendix provides an opportunity to use a process simulator for binary distillation. Although the instructions discuss Aspen Plus, other simulators will be similar. 

In some ways, the most difficult part of writing a multicomponent distillation program has not been discussed. This is the development of a physical properties package that will accurately predict equilibrium and enthalpy relationships IBarnicki. 2002 Carlson. 1996 Sadeq eta1 1997 Schad. 19981. Sadaq et al. (1997) compared three process simulators and found that relatively small differences in the parameters and in the VLE correlation can cause major errors in the results. Fortunately, a considerable amount of research has been done (see Table 2-2 and Fredenslund etal.. 1977 and Walas. 19851 to develop accurate physical property correlations. Very detailed physical properties packages can be purchased commercially and are included in the commercial process simulators. 

Due to its availability for a long time, the widely developed parameter matrix in the commercial process simulators and its compatibility with the usual NRTL equation, the NRTL electrolyte model from Chen [15, 16] has become the most widely used electrolyte model. Nevertheless, it gives considerable errors for multicomponent systems, that is, if two or more electrolyte components or a solvent mixture is considered. In these cases, the results of the NRTL electrolyte model can only be taken as a qualitative estimation. 

In most commercial process simulators, model parameters for pure component properties and binary parameters can be found for a large number of compounds and binary systems. However, the simulator providers repeatedly warn in their software documentations and user manuals that these default parameters should be applied only after careful examination by the company s thermodynamic experts prior to process simulation. For verification of the model parameters again, a large factual data bank like the DDE is the ideal tool. The DDE allows checking all the parameters used for the description of the pure component properties as a function of temperature and of the binary parameters of a multicomponent system by access to the experimental data stored. On the basis of the results for the different pure component properties and phase equilibria, excess enthalpies, activity coefficients at infinite dilution, separation factors, and so on, the experienced chemical engineer can decide whether all the data and parameters are sufficiently reliable for process simulation. 

Besides the pure component parameters, in particular the mixture parameters, for example of a g -model or an equation of state, should be checked carefully prior to process simulation. The procedure is shown in Figure 11.4 for the binary system acetone-cyclohexane, which may be one of the binary key systems of a multicomponent mixture. From the results shown in Figure 11.4, it can be concluded that the VLE behavior of the binary system can be reliably described in the temperature range 0-50 C with the Wilson parameters used. But from the poor -results, it seems that an extrapolation to higher or lower temperature may be dangerous, as already can be seen from the solid-liquid equilibrium (SLE) results of the eutectic system in the temperature range 0 to —lOO C and also from the incorrect temperature dependence of the calculated azeotropic data. 

A number of attempts have been made [11,21-33] however, none of them fulfills the particular demands of a process simulator, that is, extension to multicomponent mbctures, proved enthalpy description, and derivation of a fugacity coefficient. 

Nevertheless, one should be careful with the fitting of the a parameter. For multicomponent systems, experience shows that the application of NRTL in process simulation is somewhat easier if all the a-values are close to each other. The larger the a, the more probable are multiple solutions for the LLE (see below). 

Fourth, a multicomponent simulation of the separation process can be carried out using a commercial process simulator. The simulation includes different methods and conditions of product recovery and gas cycles. 

From the standpoint of using multicomponent diffusion in a numerical simulation, it can be beneficial to pose the multicomponent diffusion in terms of an equivalent Fickian diffusion process [72,422]. To do this, imagine that a new mixture diffusion coefficient can be defined such that the first term (summation) in Eq. 12.166 can be replaced with the right-hand side of Eq. 12.162. An advantage of the latter is that the diffusion of the fcth species depends on its own mole fraction gradient, rather than on the gradients of all the other species the Jacobian matrix is more diagonally dominant, which can sometimes facilitate numerical solution. 

Kenig EY, Gorak A. A film model based approach for simulation of multicomponent reactive separation. Chem Eng Process 1995 34 97-103. 

Common catalytic systems are characterized by the presence of reagent molecules only, whereas the enzymatic system is multicomponent and possesses low concentrations of the substrates in water. The interaction between a substrate with an oxidant or a reducer is most often considered. This makes unnecessary simulation of the enzyme selectivity. However, free contact of reagent molecules with active sites preserves the possibility of various mechanism realizations which is the reason for decrease of the process selectivity. Apparently, a compromise should be found in resolving the question of selectivity in biomimics development in suggesting that, though complex gap mechanism is the effective method for distance and mutual orientation control of reactive groups in the enzyme, it may hardly be implemented in synthetic systems. 

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