Computers process simulators

Spreadsheet Applications. The types of appHcations handled with spreadsheets are a microcosm of the types of problems and situations handled with fuU-blown appHcation programs that are mn on microcomputers, minis, and mainframes and include engineering computations, process simulation, equipment design and rating, process optimization, reactor kinetics—design, cost estimation, feedback control, data analysis, and unsteady-state simulation (eg, batch distillation optimization). 

Design and Operation of Azeotropie Distillation Columns Simulation and design of azeotropic distiUation columns is a difficult computational problem, but one tnat is readily handled, in most cases, by widely available commercial computer process simulation packages [Glasscock and Hale, Chem. Eng., 101(11), 82 (1994)]. Most simida-... 

Equation (9.1) is the preferred method of describing membrane performance because it separates the two contributions to the membrane flux the membrane contribution, P /C and the driving force contribution, (pio — p,r). Normalizing membrane performance to a membrane permeability allows results obtained under different operating conditions to be compared with the effect of the operating condition removed. To calculate the membrane permeabilities using Equation (9.1), it is necessary to know the partial vapor pressure of the components on both sides of the membrane. The partial pressures on the permeate side of the membrane, p,e and pje, are easily obtained from the total permeate pressure and the permeate composition. However, the partial vapor pressures of components i and j in the feed liquid are less accessible. In the past, such data for common, simple mixtures would have to be found in published tables or calculated from an appropriate equation of state. Now, commercial computer process simulation programs calculate partial pressures automatically for even complex mixtures with reasonable reliability. This makes determination of the feed liquid partial pressures a trivial exercise. 

This chapter has introduced computer process simulation—using computers to carry out material and energy balance calculations on processes at steady state. When thinking about the chapter material, remember the following points ... 

Since the relief condition is at ID% overpressure for non-Hre case, and 21% overpressure for fire case, it is necessary to use computer (process simulator) or other means to calculate the physical properties of process fluid at these pressures. These properties will be different from the simulation at normal operation. 

Figure 21.5 Comparison of (a) one-stage, (b) two-stage, and (c) two-step membrane processes, aU producing the same volume of inert gas purge (54.4 scfm). These calculations are performed using a computer process simulation package (ChemCAD 5.0, Chemstations, Inc., Houston, TX) modified with code written at MTR for the membrane separation step.

Simulation tools are available for sizing and analyzing plants. However, these tools do not replace the designer as the architect of the plant because selection of process and the sequenciag of units are the designers choices. The same is tme for heat-exchanger networks. Most of the commercial process simulator companies market computer modules that perform some of the tedious steps ia the process but none is able to remove the designer from the process. 

The use of the computer in the design of chemical processes requires a framework for depiction and computation completely different from that of traditional CAD/CAM appHcations. Eor this reason, most practitioners use computer-aided process design to designate those approaches that are used to model the performance of individual unit operations, to compute heat and material balances, and to perform thermodynamic and transport analyses. Typical process simulators have, at their core, techniques for the management of massive arrays of data, computational engines to solve sparse matrices, and unit-operation-specific computational subroutines. 

Many process simulators come with optimizers that vary any arbitrary set of stream variables and operating conditions and optimize an objective function. Such optimizers start with an initial set of values of those variables, carry out the simulation for the entire flow sheet, determine the steady-state values of all the other variables, compute the value of the objective function, and develop a new guess for the variables for the optimization so as to produce an improvement in the objective function. 

Ramirez, W. F. Computational Methods for Process Simulations. Butter-worths, Boston (1989). 

Classification Process simulation refers to the activity in which mathematical models of chemical processes and refineries are modeled with equations, usually on the computer. The usual distinction must be made between steady-state models and transient models, following the ideas presented in the introduction to this sec tion. In a chemical process, of course, the process is nearly always in a transient mode, at some level of precision, but when the time-dependent fluctuations are below some value, a steady-state model can be formulated. This subsection presents briefly the ideas behind steady-state process simulation (also called flowsheeting), which are embodied in commercial codes. The transient simulations are important for designing startup of plants and are especially useful for the operating of chemical plants. 

For 25 years, molecular dynamics simulations of proteins have provided detailed insights into the role of dynamics in biological activity and function [1-3]. The earliest simulations of proteins probed fast vibrational dynamics on a picosecond time scale. Fifteen years later, it proved possible to simulate protein dynamics on a nanosecond time scale. At present it is possible to simulate the dynamics of a solvated protein on the microsecond time scale [4]. These gains have been made through a combination of improved computer processing (Moore s law) and clever computational algorithms [5]. 

Analysis of neutron data in terms of models that include lipid center-of-mass diffusion in a cylinder has led to estimates of the amplitudes of the lateral and out-of-plane motion and their corresponding diffusion constants. It is important to keep in mind that these diffusion constants are not derived from a Brownian dynamics model and are therefore not comparable to diffusion constants computed from simulations via the Einstein relation. Our comparison in the previous section of the Lorentzian line widths from simulation and neutron data has provided a direct, model-independent assessment of the integrity of the time scales of the dynamic processes predicted by the simulation. We estimate the amplimdes within the cylindrical diffusion model, i.e., the length (twice the out-of-plane amplitude) L and the radius (in-plane amplitude) R of the cylinder, respectively, as follows ... 

Finally, in this Introduction, it is worthwhile to reproduce one of the several current definitions, in the Oxford English Dictionary, of the word simulate To imitate the conditions or behaviour of (a situation or process) by means of a model, especially for the purpose of study or training specifically, to produce a computer model of (a process) . The Dictionary quotes this early (1958) passage from a text on high-speed data processing A computer can simulate a warehouse, a factory, an oil refinery, or a river system, and if due regard is paid to detail the imitation can be very exact . Clearly, in 1958 the scientific uses of computer simulation were not yet thought worthy of mention, or perhaps the authors did not know about them. 

For most field gas units it is not necessary to specify a stripper size. Vendors have standard design amine circulation packages for a given amine circulation rate, acid-gas loading, and reboiler. These concepts can be used in a preliminary check of the vendor s design. However, lor detailed design and specification of large units, a process simulation computer model should be used. 

The purification of value-added pharmaceuticals in the past required multiple chromatographic steps for batch purification processes. The design and optimization of these processes were often cumbersome and the operations were fundamentally complex. Individual batch processes requires optimization between chromatographic efficiency and enantioselectivity, which results in major economic ramifications. An additional problem was the extremely short time for development of the purification process. Commercial constraints demand that the time interval between non-optimized laboratory bench purification and the first process-scale production for clinical trials are kept to a minimum. Therefore, rapid process design and optimization methods based on computer aided simulation of an SMB process will assist at this stage. 

This interplay of the many variables is extremely complex and involves a matrix of the many variables. As an example in the molding simulation TMconcept system programmed Molding Cost Optimization (MCO) of Plastics Computer Inc., Dallas, TX, there are well over 300 variables. It is not reasonable to expect a person using manual methods to calculate these complex interactions even if molding only a modest shaped product without omissions or errors. Computerized process simulation is a practical tool to monitor the influence of design alternatives on the processability of the product and to select molding conditions that ensure the required product quality (3). 

Example 11.7 hints at the complications that are possible in reactive gas absorption. Gas absorption is an important unit operation that has been the subject of extensive research and development. Large, proprietary computer codes are available for purchase, and process simulation tools such as Aspen can do the job. However, as shown in Example 11.8, simple but useful approximations are sometimes possible. 

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