ASPEN correlations

The optimization of empirical correlations developed from the ASPEN-PLUS model yielded operating conditions which reduced the steam-to-slurry ratio by 33%, increased throughput by 20% while maintaining the solvent residual at the desired level. While very successful in this industrial application the approach is not without shortcomings. The main disadvantage is the inherent assumption that the data are normally distributed, which may or may not be valid. However, previous experience had shown the efficacy of the assumption in other similar situations. 

At the lowest tested severity parameter of 180°C for 8 min, there was almost no inhibition when compared to the control that was run at each experiment. However, at the highest tested severity parameter of 220°C for 32 min, there was an almost 50% reduction in the rate of glucose consumption by the yeast. This division of inhibition as it correlates to severity is well defined for both the aspen wood and corn stover samples. However, between the aspen wood and corn stover samples there appeared to be a difference in inhibition rates, as a comparison of Figs. 1 and 2 shows. However, for this distinction to be conclusive, a side-by-side experiment would need to be conducted, giving a more standardized inoculum between the two experiments. Both Figs. 1 and 2 show the lowest and highest tested severity parameters. The response of mid-level severity was similar to that observed for the low level severities (data not shown). 

ASPEN is supported by a versatile set of physical property correlations representing the current state-of-the-art. Physical property monitors control the property calculations in accordance with methods and models specified by the user. The user is allowed to specify different combinations of physical property calculation methods in different parts of the process. For specialized components such as coal or limestone, a collection of non-conventional property models is available. ASPEN includes data banks from which the required physical property constants and correlation parameters can be retrieved automatically at run... 

Throughout this book, we have seen that when more than one species is involved in a process or when energy balances are required, several balance equations must be derived and solved simultaneously. For steady-state systems the equations are algebraic, but when the systems are transient, simultaneous differential equations must be solved. For the simplest systems, analytical solutions may be obtained by hand, but more commonly numerical solutions are required. Software packages that solve general systems of ordinary differential equations— such as Mathematica , Maple , Matlab , TK-Solver , Polymath , and EZ-Solve —are readily obtained for most computers. Other software packages have been designed specifically to simulate transient chemical processes. Some of these dynamic process simulators run in conjunction with the steady-state flowsheet simulators mentioned in Chapter 10 (e.g.. SPEEDUP, which runs with Aspen Plus, and a dynamic component of HYSYS ) and so have access to physical property databases and thermodynamic correlations. 

Since the studying system is a little complicated, it is difficult for the components to get all the thermodynamic data. We obtain the data from Aspen Plus software. Then coefficients for the phase equilibrium relations of each component are correlated to the... 

Winn (1958) developed another way to handle this problem with a correlation for the relative volatility, which is used by Aspen Plus. Additional details about shortcut methods are available in King (1980) and Perry s Chemical Engineers Handbook (Perry and Green, 1997). 

You expect Aspen Plus to be correct, but there are two possible problems lack of convergence and poor choices of thermodynamic correlations. By using the Nd button to run your problem you will get printed information about the convergence or lack of it. Read the output You get this information from the View/Control Panel menu, too. The proper choice of thermodynamic correlation can only be determined by comparison with experimental data or with experience. (This is one reason why chemical engineers are paid a lot - for their experience.) Naturally, at this point in your career, few of you have that experience. However, you can stUl look at your mass and energy balances and see if they make sense. Every number needs to be examined. 

We wish to thank Dr. Darrell D. Nicholas, Mr. Roy D. Adams and Ms. Susan Mateer of the Institute of Wood Research at Michigan Technological University for their work in conducting the mixed hardwood flakeboard experimental program. We also wish to thank Dr. Michael 0. Hunt of Purdue University and Dr. William F. Lehmann of Weyerhaeuser Corporation for their help in the red oak flake-board work and Mr. Otto G. Udvardy of Borden Chemical for the aspen waferboard study. Finally, we would like to thank Dr. Ronald Taylor of Mobay Chemical Corporation for his considerable advice and help with the multiple correlation analysis. 

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. 

A characteristic feature of phytochemicals in natural populations is the variation in concentration and profile that is found among different individuals. Extensive work by Lindroth and others has documented such genotype-dependant variation in phenolics in P. tremuloides genotypes. " As mentioned above, high levels of phenolics correlate with increased pest resistance in this system. Comparisons of different Populus species and hybrids have also demonstrated a significant variation in levels and types of phenolic phytochemicals.Further variability is often due to environmental conditions, as aspen plants grown with high... 

Research by Kyokong and others [28] lent credibility to Ilcewicz and Wilson s hypothesis. They applied Eringen s nonlocal theory to solid poplar (Populus tremuloides) joints bonded with resorcinol adhesive, substituting the average vessel lumen diameter of aspen (100 pm) as the characteristic dimension. They were able to show that the nonlocal theory using this dimension correlated very closely with the fracture toughness of the joints as determined by classic (local) theory. 

The HEATX subroutine (block) of the ASPEN PLUS simulator is used to make the calculations. It has built-in correlations of the type described above for estimating shell-side and tube-side heat transfer coefficients and pressure drops. The following results are obtained (both streams are liquid) ... 

The expected market price, chemical kinetics and VLE data, and utility costs will be supplied at a later date. Where VLE data are lacking you may use the UNIFAC correlation. Your company has access to ASPEN PLUS which has a reactive distillation subroutine (RADFRAC). 

This problem was also run on the Aspen Plus process simulator (see Problem 4.G1 and chapter appendix). Aspen Plus does not assume CMO and with an appropriate vapor-liquid equilibrium (VLE) correlation (the nonrandom two-liquid model was used) should be more accurate than the McCabe-Thiele diagram, which assumes CMO. With 5 equilibrium stages and feed on stage 4 (the optimum location), = 0.9335 and Xg = 0.08365, which doesn t meet the specifications. With 6 equilibrium stages and feed on stage 5 (the optimum), Xq = 0.9646 and Xg = 0.0768, which is slightly better than the specifications. The differences in the McCabe-Thiele and process simulation results are due to the error involved in assuming CMO and, to a lesser extent, differences in equilibrium. 

G5. a. Aspen Plus automatically generates residue curves for ternary mixtures. Generate the residue curve at 5.0 atm for a mixture propane, n-butane, and n-pentane. Report the VLE correlation used. 

The vapor-liquid equilibrium for the very nonideal systems studied in this chapter may not be fit well with any of the correlations in Aspen Plus if the parameters embedded in Aspen Plus are used. An alternative is to use Aspen Plus to fit the parameter values to give the best fit to VLE data. This procedure is explained in Appendix B at the end of the book. 

Gl. [Note This problem is quite extensive.] A saturated vapor feed at 1000 kmol/h of methanol (5 mol%) and water (95 mol%) is fed to a distillation column with 18 stages plus a kettle reboiler and a total condenser (N = 20 in Aspen Plus notation). Use the NRTL VLE correlation. Operate at 80% of flooding using Fair s diameter calculation method and a tray spacing = 0.4572 m. Use an external reflux ratio of 24. Pressure is 1.0 atm... 

G3. We wish to distill 0.10 kmol/s of a feed at 25°C and 15.0 atm The feed is 0.100 mole fraction ethane, 0.350 mole fraction propane, 0.450 mole fraction n-butane, and 0.100 mole fraction n-pentane. Use the Peng-Robinson VLF correlation. Design a column with N (Aspen notation) = 35 and the feed on stage 16. The column operates at 15.0 atm, has a partial condenser, and produces a vapor distillate with D = 0.0450 kmol/s. A kettle type reboiler is used. Reflux ratio is L/D = 2.4. 

In these equations, a is the interfacial surface tension, N/m ty = (froth height)/ and tL pj ty/py are the average residence time per pass for the vapor and liquid, s. They recommend a correlation of Stichlmair for the interfacial area per volume a, but Aspen Plus 2010 recommends using the Zuiderweg (1982) correlations in conjunction with the Chen and Chuang mass-transfer correlation. 

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