Computer methods process simulation

This chapter deals with the fundamental concepts that allow a unified approach of the thermodynamic computations in process simulation, both in the field of physical properties and of phase equilibria. The last aspect is examined in more detail in the Chapter 6. The material is presented as a synthesis of a much more elaborated treatment that may be found in dedicated textbooks on Chemical Engineering Thermodynamics, as Smith and van Ness (1997), Kyle (2000), Sandler (2001). The description of the major methods for estimating fundamental properties, mostly being implemented in simulation packages, may be found in the thesaurus book of Reid and al. (1987) updated recently by Poling, Prausnitz and O Connell (2001). 

Clearly, computational methods for simulating the combinatorial discovery process, predicting ADMET, and exploring chemical space are necessary. Specifically, modeling methods are required for ... 

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

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). 

An alternative method of solving the equations is to solve them as simultaneous equations. In that case, one can specify the design variables and the desired specifications and let the computer figure out the process parameters that will achieve those objectives. It is possible to overspecify the system or to give impossible conditions. However, the biggest drawback to this method of simulation is that large sets (tens of thousands) of nonlinear algebraic equations must be solved simultaneously. As computers become faster, this is less of an impediment, provided efficient software is available. 

CFD may be loosely thought of as computational methods applied to the study of quantities that flow. This would include both methods that solve differential equations and finite automata methods that simulate the motion of fluid particles. We shall include both of these in our discussions of the applications of CFD to packed-tube simulation in Sections III and IV. For our purposes in the present section, we consider CFD to imply the numerical solution of the Navier-Stokes momentum equations and the energy and species balances. The differential forms of these balances are solved over a large number of control volumes. These small control volumes when properly combined form the entire flow geometry. The size and number of control volumes (mesh density) are user determined and together with the chosen discretization will influence the accuracy of the solutions. After boundary conditions have been implemented, the flow and energy balances are solved numerically an iteration process decreases the error in the solution until a satisfactory result has been reached. 

Process simulators contain the model of the process and thus contain the bulk of the constraints in an optimization problem. The equality constraints ( hard constraints ) include all the mathematical relations that constitute the material and energy balances, the rate equations, the phase relations, the controls, connecting variables, and methods of computing the physical properties used in any of the relations in the model. The inequality constraints ( soft constraints ) include material flow limits maximum heat exchanger areas pressure, temperature, and concentration upper and lower bounds environmental stipulations vessel hold-ups safety constraints and so on. A module is a model of an individual element in a flowsheet (e.g., a reactor) that can be coded, analyzed, debugged, and interpreted by itself. Examine Figure 15.3a and b. 

The use of molecular dynamics and Monte Carlo simulations to study electrochemical processes at the interface between two phases is only in its preliminary stages. The need to provide a molecular-level understanding of structure and dynamics at the interface to help in interpreting the new microscopic level of experimental data will increase. However, many important basic issues remain to be understood before these computational methods become routine research tools. 

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