Processes flow simulation

Families of finite elements and their corresponding shape functions, schemes for derivation of the elemental stiffness equations (i.e. the working equations) and updating of non-linear physical parameters in polymer processing flow simulations have been discussed in previous chapters. However, except for a brief explanation in the worked examples in Chapter 2, any detailed discussion of the numerical solution of the global set of algebraic equations has, so far, been avoided. We now turn our attention to this important topic. 

Major equipment estimates based on a more detailed given flowsheet that includes all of the equipment of significance roughly sized with approximate costs. Optimization using process flow simulators (refer to Chapter 15) can be employed. Figure B.2 illustrates a typical analysis for a tank. Refer to Brown (2000) for additional details. 

The CASST/CC system has been modelled within the process flow simulation program ASPEN , "Hie major assumptions made for the CASST process are given in table 4. For the combined cycle the assumptions described by Faay et. al. (6) have been used. 

Fuel cell system models have been developed to help understand the interactions between various unit operations within a fuel cell system. Most fuel cell system models are based on thermodynamic process flow simulators used by the process industry (power industry, petroleum industry, or chemical industry) such as Aspen Plus, HYSIS, and ChemCAD. Most of these codes are commercially distributed, and over the past years they have offered specific unit operations to assist modeling fuel cell stacks (or at least a guide for putting together existing unit operations to represent a fuel cell stack) and reformers. Others (16) have developed more sophisticated 2-D... 

The thermal conductivity of polymeric fluids is very low and hence the main heat transport mechanism in polymer processing flows is convection (i.e. corresponds to very high Peclet numbers the Peclet number is defined as pcUUk which represents the ratio of convective to conductive energy transport). As emphasized before, numerical simulation of convection-dominated transport phenomena by the standard Galerkin method in a fixed (i.e. Eulerian) framework gives unstable and oscillatory results and cannot be used. 

Iterative solution methods are more effective for problems arising in solid mechanics and are not a common feature of the finite element modelling of polymer processes. However, under certain conditions they may provide better computer economy than direct methods. In particular, these methods have an inherent compatibility with algorithms used for parallel processing and hence are potentially more suitable for three-dimensional flow modelling. In this chapter we focus on the direct methods commonly used in flow simulation models. 

A future goal for the integration of graphics and process design simulators is to be able to use an interactive graphics program to prepare the input to the process simulator. This capabiHty would allow tme on-line process modification, flow-sheet optimization, and process optimization, and is likely to be one of the key developments in this field in the 1990s (99). 

The creation and analysis of process flow sheets has become much easier because of the availabihty of automated systems to draw and revise them. The goal of the use of the flow sheet as the input for process simulation and for process control is likely to be achieved reasonably soon. The use of interactive graphic displays for process monitoring and control is pervasive today. 

Steps 6 and 7 ate involved with inputting additional specifications about the process being simulated. It is necessary to give an adequate number of specifications for each unit operation, for each calculation unit, and for the overall process flow so that all the degrees of freedom ate taken away and a unique solution can be obtained from the simulator. On the other hand, if mote than the necessary number of specifications ate given, the problem becomes overconstrained for the simulator and no solution can exist. 

Many industrial separations require a series of columns that are connected in specific ways. Some distillation programs can model such a system as a hypothetical single column with arbitrary cross-flows and connections and then carry out the distillation calculations for the modeled hypothetical column. Alternatively, such a system can be modeled as a process flow sheet using a process simulator. 

Figure 3. Shift and methanation process flow diagram. (Used with permission of Simulation Sciences Inc.)...

Computer-aided flow-simulation programs are also available for dies. All the programs can successfully predict a certain amount of shrinkage under specific conditions that can be applied to experience. The actual shrinkage is finally determined after molding or extruding the products. When not in spec process control changes can meet the requirements unless some drastic error had been included in the analysis. 

They simulate the steady-state operation of the process and can be used to draw-up the process flow sheet, and to size individual items of equipment, such as distillation columns. 

First of all, the increased computer power makes it possible to switch to transient simulations and to increase spatial resolution. One no longer has to be content with steady flow simulations on relatively coarse grids comprising 104-105 nodes. Full-scale Large Eddy Simulations (LES) on fine grids of 106—107 nodes currently belong to the possibilities and deliver realistic reproductions of transient flow and transport phenomena. Comparisons with quantitative experimental data have increased the confidence in LES. The present review stresses that this does not only apply to the hydrodynamics but relates to the physical operations and chemical processes carried out in stirred vessels as well. Examples of LES-based simulations of such operations and processes are due to Flollander et al. (2001a,b, 2003), Venneker et al. (2002), Van Vliet et al. (2005, 2006), and Flartmann et al. (2006). 

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