Because of the way the model is specified, you must take into account the following additional equations as constraints in the column model ... [Pg.446]

If necessary, the implicit nature of the calculation may, however, be avoided by a reformulation of the holdup relationship into an explicit form. The resulting calculation procedure then becomes much more straightforward and the variation of holdup in the column may be combined into a fuller extraction column model in which the inclusion of the hydrodynamics now provides additional flexibility. The above modelling approach to the column hydrodynamics, using an explicit form of holdup relationship, is illustrated by the simulation example HOLDUP. [Pg.153]

The procedure has been tested primarily on realistic distillation column models. This choice was deliberate because most industrial processes have similar gain, deadtime, and lag transfer functions. Undoubtedly some pathological transfer functions can be found that the procedure cannot handle. But we are interested in a practical engineering tool, not elegant, rigorous all-inclusive mathematical theorems. [Pg.595]

For process optimization problems, the sparse approach has been further developed in studies by Kumar and Lucia (1987), Lucia and Kumar (1988), and Lucia and Xu (1990). Here they formulated a large-scale approach that incorporates indefinite quasi-Newton updates and can be tailored to specific process optimization problems. In the last study they also develop a sparse quadratic programming approach based on indefinite matrix factorizations due to Bunch and Parlett (1971). Also, a trust region strategy is substituted for the line search step mentioned above. This approach was successfully applied to the optimization of several complex distillation column models with up to 200 variables. [Pg.203]

The field experiments from the Bet-Dagan site were used to test different theoretical models for field-scale chemical transport. One study expanded a simple column model for flow and transport in partially saturated soils (Bresler and Dagan 1983),... [Pg.252]

Haywood, J. M., and K. P. Shine, Multi-Spectral Calculations of the Direct Radiative Forcing of Tropospheric Sulphate and Soot Aerosols Using a Column Model, Q. J. R. Meteorol. Soc., 123, 1907-1930 (1997). [Pg.834]

For numerical simulation of skewed bandshapes, see S. Sugata and Y. Abe, An Analogue Column Model for Nonlinear Isotherms The Test Tube Model, ... [Pg.680]

FIG. 14-65 Parallel-columns model. (From Lockett and Billingham, Trans. IChemE 80, Part A, p. 373, May 2002 reprinted courtesy of IChemE.)... [Pg.70]

Refer to Appendix G.2 for more details of the absorption column model and associated calculations. [Pg.165]

First, a few observations regarding the sensitivity of the column model to the various operating parameters. The formulae presented so far indicate the direct relationship between operating pressure and temperature for the absorption process. Higher pressures and lower temperatures increase the nitrous gas absorption. The amount of nitrous gases dissolved within the acid solution also increases markedly with decreasing temperature. [Pg.289]

We study the separation of 77-hexane-ethyl acetate mixture by using acetonitrile as a heavy heterogeneous entrainer. The simulation of the process is performed with the batch process simulator ProSimBatch [10]. It enables to evaluate operational parameters like the entrainer amount that are not provided by the feasibility and synthesis analysis The column model consists of usual plate by plate Material balance, Equilibrium, Summation of fractions and Heat balance... [Pg.134]

A comparison of the steady-state profiles predicted by the wave model and those predicted by a rigorous tray-by-tray column model is shown in Fig. 5.16 for a coupled column system which serves for the separation of a mixture of methanol, ethanol, and 1-propanol. The approximation by the wave model in Fig. 5.16 is fairly good, although the reduction of the system order is considerable. The state variables of the rigorous model are the concentration and temperatures on each column tray. In contrast to this the state variables of the wave model are only the front positions. [Pg.175]

By definition, however, we have 2 -C 1. We can thus proceed with the time-scale decomposition and model reduction of the high-purity distillation column model as outlined in Section 7.3, by defining the stretched, fast time scale 72 = t/e2, in which the model becomes... [Pg.189]

Let us now consider the limiting case of an infinitely high energy throughput and set e2 = 0, for which the column model becomes... [Pg.190]

The column was modeled with AspenPlus / using the rigorous radf rac column model, in conjunction with the Redlich-Kwong-Soave equation of state for property estimation. Steady-state calculations indicated a reflux ratio of 87.67. This is a consequence of the difficult separation problem posed by the two closeboiling components. [Pg.196]

In the past researchers, using simple models, reported timesaving for the time optimal solutions of more than 10% compared to constant reflux operations. Mujtaba (1989) reported a batch timesaving of 30 - 45% compared to constant reflux operations for a number of example problems using detailed dynamic column models (Type IV-CMH). [Pg.120]

For single separation duty, Bernot et al. (1991) presented a method to estimate batch sizes, operating times, utility loads, costs, etc. for multicomponent batch distillation. The approach is similar to that of Diwekar et al. (1989) in the sense that a simple short cut technique is used to avoid integration of a full column model. Their simple column model assumes negligible holdup and equimolal overflow. The authors design and, for a predefined reflux or reboil ratio, minimise the total annual cost to produce a number of product fractions of specified purity from a multicomponent mixture. [Pg.154]

The problem of choosing whether and when to recycle each off-cut and the size of the cut is a difficult one. Liles (1966) considered dynamic programming approach and Luyben (1988) considered repetitive simulation approach to tackle this problem. Mayur et al. (1970) and Christensen and Jorgensen (1987) tackled it as a dynamic optimisation problem using Pontryagin s Maximum Principle applied to very simplified column models as mentioned in Chapters 4 and 5. [Pg.233]

Mujtaba (1989) used the measure of the degree of difficulty of separation proposed by Christensen and Jorgensen (1987) to decide whether or not an off-cut is needed. The optimal control algorithm of Morison (1984) was then used to develop operational policies for reflux ratio profiles and amount and timing of off-cuts which minimise the total batch time. A more realistic dynamic column model (type IV as presented in Chapter 4) was used in the optimisation framework. [Pg.233]

Both the steady state and dynamic column models (for CBD only) used by Mujtaba (1997) are based on the assumptions of constant relative volatility and equimolal overflow and include detailed plate-to-plate calculations. This will allow a direct comparison between CBD and continuous column operation. The continuous column model is presented in section 4.3.1 and the CBD model (Type III) is presented in section 4.2.3. Some of the modelling assumptions, for example, constant molar holdup, constant pressure, equimolal overflow, etc., can be relaxed, if needed, by replacing them with more realistic assumptions and therefore by adding the relevant equations (as presented in Chapter 4). [Pg.339]

Mujtaba (1997) used the minimum time to evaluate the performance of continuous column operation under multiple separation duties. However, time does not explicitly appear in continuous column model equations but the feed rate is a measure of the batch time (t = BJF). Note, maximisation of the feed rate will therefore ensure minimisation of the batch time. [Pg.347]

Tung, L.S. and Edgar, T. F., "Development and Reduction of a Multivariable Distillation Column Model with Tray Hydraulics,"... [Pg.112]

Column model. A rigorous method describes a column as a group of equations and solves these equations to calculate the operating conditions of the column. All flows are usually expressed in terms of moles/ hour. Also, when a rigorous calculation is performed, the following is usually specified ... [Pg.136]

Figure 4.1 Stage and column models, (a) Overall column model (6) simple stage model... |

Figure 4.1 (Continued) Stage and column models, (c) feed sage model (d) sidestream product withdrawal stage model. |

To implement these simulation approaches, the value of the liquid film mass transfer coefficient Kf is required, which for nonporous and porous HPLC particles, can be calculated from literature correlations derived for bath357,400,408 or column models.407,408 For the case with porous particles, the apparent pore liquid mass transfer coefficient Kp can be expressed as an effective pore diffusivity over an average effective diffusion path length, such that... [Pg.201]

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