Process Model Representation

A process plant has a static dimension that can be represented as structure and topology (Pohjola et al., 1997). The structure expresses the decomposition of a plant unit. The topology reflects the connectivity between units within the same level of abstraction. The plant can be divided into control group units (CGUs). The concept of CGU is described by Naka (1999). 

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Figure 9-11 Fractionation Process Model Representation in UML Format...

Figure 9-11 shows the fractionation process model representation in... 

It is important to note that the mental model representation elicited by this technique is not a process engineering model, but instead represents the process workers understanding of the various causes and consequences of the disturbance. This may or may not be in accordance with the actual chemistry or dynamics of the physical process. 

IMAS has a facility called EXPLORE allows the analyst to specify which indicators (e.g., temperatures, pressures, valve settings) are present, and which are absent in a particular scenario. EXPLORE then traverses the various links in the mental model representation network and generates a report that simulates the worker s thinking processes. This form of simulation provides useful information to the analyst with regard to the worker s capability to achieve correct diagnoses. Embrey (1985) gives an example of these simulations for the mental model in Figure 4.13. 

The dynamics of the jacket are more complex for the case of steam heating. The model representation of the jacket steam heating process is shown in Fig. 3.5. 

In all of the workshops, but especially in the FAT and Exposure Assessment workshops, the need for better understanding and model representation of soil systems, including both unsaturated and saturated zones, was evident. This included the entire range of processes shown in Table II, i.e., transport, chemical and biological transformations, and intermedia transfers by sorption/desorption and volatilization. In fact, the Exposure Assessment workshop (Level II) listed biological degradation processes as a major research priority for both soil and water systems, since current understanding in both systems must be improved for site-specific assessments. 

Constraints in optimization arise because a process must describe the physical bounds on the variables, empirical relations, and physical laws that apply to a specific problem, as mentioned in Section 1.4. How to develop models that take into account these constraints is the main focus of this chapter. Mathematical models are employed in all areas of science, engineering, and business to solve problems, design equipment, interpret data, and communicate information. Eykhoff (1974) defined a mathematical model as a representation of the essential aspects of an existing system (or a system to be constructed) which presents knowledge of that system in a usable form. For the purpose of optimization, we shall be concerned with developing quantitative expressions that will enable us to use mathematics and computer calculations to extract useful information. To optimize a process models may need to be developed for the objective function/, equality constraints g, and inequality constraints h. 

In this chapter we will discuss several factors that need to be considered when constructing a process model. In addition, we will examine the use of optimization in estimating the values of unknown coefficients in models to yield a compact and reasonable representation of process data. Additional information can be found in textbooks specializing in mathematical modeling. To illustrate the need to develop models for optimization, consider the following example. 

Two extremes are encountered in process simulator software. At one extreme the process model comprises a set of equations (and inequalities) so that the process model equations form die constraints for optimization, exactly the same as described in previous chapters in this book. This representation is known as an equation-... 

We first describe in Section II how the information flow takes place in process models, give a compact method of representation of the system of equations, and point out the correspondence between a system of equations and a linear diagraph. In Section III, methods for finding an output set... 

Figure 21.2 Schematic representation of the absorption processes modelled in Intellipharm PK.

The PBPK model for a chemical substance is developed in four interconnected steps (1) model representation, (2) model parametrization, (3) model simulation, and (4) model validation (Krishnan and Andersen 1994). In the early 1990s, validated PBPK models were developed for a number of toxicologically important chemical substances, both volatile and nonvolatile (Krishnan and Andersen 1994 Leung 1993). PBPK models for a particular substance require estimates of the chemical substance-specific physicochemical parameters, and species-specific physiological and biological parameters. The numerical estimates of these model parameters are incorporated within a set of differential and algebraic equations that describe the pharmacokinetic processes. Solving these... 

Khogeer (2005) developed an LP model for multiple refinery coordination. He developed different scenarios to experiment with the effect of catastrophic failure and different environmental regulation changes on the refineries performance. This work was developed using commercial planning software (Aspen PIMS). In his study, there was no model representation of the refineries systems or clear simultaneous representation of optimization objective functions. Such an approach deprives the study of its generalities and limits the scope to a narrow application. Furthermore, no process integration or capacity expansions were considered. 

An accurate representation of the phase equilibrium behavior is required to design or simulate any separation process. Equilibrium data for salt-free systems are usually correlated by one of a number of possible equations, such as those of Wilson, Van Laar, Margules, Redlich-Kister, etc. These correlations can then be used in the appropriate process model. It has become common to utilize parameters from such correlations to obtain insight into the fundamentals underlying the behavior of solutions and to predict the behavior of other solutions. This has been particularly true of the Wilson equation, which is shown below for a binary system. 

The two-film model representation can serve as a basis for more complicated models used to describe heterogeneously catalyzed RSPs or systems containing suspended solids. In these processes a third solid phase is present, and thus the two-film model is combined with the description of this third phase. This can be done using different levels of model complexity, from quasi-homogeneous description up to the four-film presentations that provide a very detailed description of both vapor/gas/liquid-liquid and solid/liquid interfaces (see, e.g., Refs. 62, 68 and 91). A comparative study of the modeling complexity is given in Ref. 64 for fuel ether synthesis of MTBE and TAME by CD. 

The example CO2 capture process, shown in Figure 8 as an Aspen Plus EO model representation, is part of an ammonia plant. Designed to scrub CO2 from ammonia synthesis gas, it includes an absorber and two solution regeneration columns, one stripping the rich, C02 laden solution leaving the absorber to semilean concentration of absorbed CO2, and the other cleaning the solution even further to lean solution... 

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