Design process Simulation

Process design, 20 710-733. See also Chemical process design Process simulation... 

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. 

Developments. A variety of process modifications aimed at improving surface finish or weld line integrity have been described. They include gas assisted, co-injection, fusible core, multiple Hve feed, and push—pull injection mol ding (46,47). An important development includes computer-aided design (CAD) methods, wherein a proposed mold design is simulated by a computer and the melt flow through it is analy2ed (48). 

Prediction of reverse osmosis performance is usefiil to the design of RO processes. Simulation of RO processes can be separated iato two categories. The first is the predictioa of membrane module performance. The second is the simulation of a network of RO processes, ie, flow sheet simulations, which can be used to determine the optimum placement of RO modules to obtain the overaH process objective. 

Given the first type of simulation, it is advantageous to be able to design a system of RO modules that can achieve the process objective at a minimal cost. A model has been iategrated iato a process simulation program to predict the stream matrix for a reverse osmosis process (132). In the area of waste minimization, the proper placement of RO modules is essential for achieving minimum waste at a minimum cost. Excellent details on how to create an optimal network of RO modules is available (96). 

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. 

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). 

A process-simulation program almost always contains a physical property service, because the quaflty of process design ultimately depends on the way in which the laws of physics and chemistry are appfled to the problem. Accordingly, the quaflty of this service is an important consideration to the user of a flow-sheeting system. 

Process simulators stop generally at the process specifications for the equipment. For the detailed mechanical design of the equipment, such as heat exchangers and distillation columns, stand-alone programs are often used. They make process calculations, size the equipment, calculate thermal and mechanical stresses, design mechanical support of the parts of the equipment, design inlet and outlet nozzles, etc. 

Whereas process simulation includes quantitative analysis of a design given the stmcture of the design, process synthesis involves determining the stmcture that will meet the requirements of the design as well as finding the best stmcture for the requirements. For example, if components A, B, C, and D whose relative volatOities were in the order D, C, B, and A were to be separated by distillation for which each column produced a top and a bottom fraction, five schemes of three columns arise as possible stmctures (53) (Fig. 8). 

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). 

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. 

Flash-drum specifications in addition to (T9, T9) and (T9, adiabatic) are also possible but must be applied with care, as discussed by Michelsen [Comp. Chem. Engng., 17,431 (1993)]. Most computer-aided process design and simulation programs permit a wide variety of flash specifications. 

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-... 

Intended Use The intended use of the model sets the sophistication required. Relational models are adequate for control within narrow bands of setpoints. Physical models are reqiiired for fault detection and design. Even when relational models are used, they are frequently developed bv repeated simulations using physical models. Further, artificial neural-network models used in analysis of plant performance including gross error detection are in their infancy. Readers are referred to the work of Himmelblau for these developments. [For example, see Terry and Himmelblau (1993) cited in the reference list.] Process simulators are in wide use and readily available to engineers. Consequently, the emphasis of this section is to develop a pre-liminaiy physical model representing the unit. 

Thus, methods are now becoming available such that process systems can be designed to manufacture crystal products of desired chemical and physical properties and characteristics under optimal conditions. In this chapter, the essential features of methods for the analysis of particulate crystal formation and subsequent solid-liquid separation operations discussed in Chapters 3 and 4 will be recapitulated. The interaction between crystallization and downstream processing will be illustrated by practical examples and problems highlighted. Procedures for industrial crystallization process analysis, synthesis and optimization will then be considered and aspects of process simulation, control and sustainable manufacture reviewed. 

Tavare, N.S., 1995. Industrial crystallization Process simulation, analysis and design. New York Plenum. 

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