Process simulation thermodynamics

This book aims is to treat the most important conceptual aspects of Process Design and Simulation in a unified frame of principles, techniques and tools. Accordingly, the material is organised in five sections. Process Simulation, Thermodynamic Methods, Process Synthesis, Process Integration, Design Project, and covered in 17 Chapters. Numerous examples illustrate both theoretical concepts and design issues. The work refers also to the newest scientific developments in the field of Computer Aided Process Engineering. 

Thermodynamics model, process simulation. See Process simulation, thermodynamic model. Thermosiphon reboilers, 40-41 3-D diagrams... 

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. 

AH the foregoing faciUties form part of the spectmm of options that, in addition to the permanent system data bank, enable the engineer to get the most out of a flow-sheeting system. The following Hst shows the physical properties that are often required for process simulation. The methods of estimating these properties, when direct measurements are not available, are indicated in the references following the properties (also see Thermodynamic properties). 

To tackle these problems successfully, new concepts will be required for developing systematic modeling techniques that can describe parts of the chemical supply chain at different levels of abstraction. A specific example is the integration of molecular thermodynamics in process simulation computations. This would fulfill the objective of predicting the properties of new chemical products when designing a new manufacturing plant. However, such computations remain unachievable at the present time and probably will remain so for the next decade. The challenge is how to abstract the details and description of a complex system into a reduced dimensional space. 

Finally, we should mention that in addition to solving an optimization problem with the aid of a process simulator, you frequently need to find the sensitivity of the variables and functions at the optimal solution to changes in fixed parameters, such as thermodynamic, transport and kinetic coefficients, and changes in variables such as feed rates, and in costs and prices used in the objective function. Fiacco in 1976 showed how to develop the sensitivity relations based on the Kuhn-Tucker conditions (refer to Chapter 8). For optimization using equation-based simulators, the sensitivity coefficients such as (dhi/dxi) and (dxi/dxj) can be obtained directly from the equations in the process model. For optimization based on modular process simulators, refer to Section 15.3. In general, sensitivity analysis relies on linearization of functions, and the sensitivity coefficients may not be valid for large changes in parameters or variables from the optimal solution. 

In Figure 2.4, data for the equilibrium constants of esterification/hydrolysis and transesterification/glycolysis from different publications [21-24] are compared. In addition, the equilibrium constant data for the reaction TPA + 2EG BHET + 2W, as calculated by a Gibbs reactor model included in the commercial process simulator Chemcad, are also shown. The equilibrium constants for the respective reactions show the same tendency, although the correspondence is not as good as required for a reliable rigorous modelling of the esterification process. The thermodynamic data, as well as the dependency of the equilibrium constants on temperature, indicate that the esterification reactions of the model compounds are moderately endothermic. The transesterification process is a moderately exothermic reaction. 

Design of extraction processes and equipment is based on mass transfer and thermodynamic data. Among such thermodynamic data, phase equilibrium data for mixtures, that is, the distribution of components between different phases, are among the most important. Equations for the calculations of phase equilibria can be used in process simulation programs like PROCESS and ASPEN. 

Eriksson, G., Sippola, H. and Sundman, B. (1994) A Proposal fora General Thermodynamic Software Interface, Proceedings of The Colloquium on Process Simulation, ed. Jokilaakso, H. (Helsinki University of Technology, Finland), p. 67. 

It would be at least interesting to use COSMO-RS directly as the thermodynamic model in process simulations. This would be especially useful if simpler empirical models are unlikely to be able to describe more complicated molecular interactions such as... 

Technically, COSMO-RS meets all requirements for a thermodynamic model in a process simulation. It is able to evaluate the activity coefficients of the components at a given mixture composition vector, x, and temperature, T. As shown in Appendix C of [Cl 7], even the analytic derivatives of the activity coefficients with respect to temperature and composition, which Eire required in many process simulation programs for most efficient process optimization, can be evaluated within the COSMO-RS framework. Within the COSMOt/ierra program these analytic derivatives Eire available at negligible additionEd expense. COSMOt/ierra can Eilso be csdled as a subroutine, Euid hence a simulator program can request the activity coefficients and derivatives whenever it needs such input. 

S 2] With the steady-state process simulator Aspen Plus , thermodynamic models for the sulfur-iodine cycle given in [132] are combined with chemistry models which describe the dissociation and precipitation reactions. 

In 2006, GA participated in a study conducted by the Savannah River National Laboratory (Summers, 2006). The S-I process was coupled to a VHTR with a required helium return temperature near 600°C. To efficiently match temperature requirements with available heat, a design was developed to supply HI decomposition section energy with recovered heat from the sulphuric acid decomposition section. For the purposes of comparison and analysis in this paper, the GA flow sheets will refer to this design, and CEA flow sheets will refer to a design in which helium supplies heat to both acid decomposition sections. CEA uses ProSimPlus for flow sheet analysis, and GA uses Aspen Plus . A previous study (Buckingham, 2008) showed that the two process simulators give similar calculated results when the same unit operations and stream compositions are modelled, although different thermodynamic models are used for the calculations. 

Barrett, A. Walsh, J. J., "Improved Chemical Process Simulation Using Local Thermodynamic Approximations", CACE 79, EFCE, Montreux,... 

For the calculation of thermodynamic properties the cubic equations of state have become the workhorse of the process simulation business. In particular, the equations of state of Soave (1972) [SRK] and of Peng and Robinson (1976) [PR] and modifications of these original forms are the most commonly used. 

Unsteady-state or dynamic simulation accounts for process transients, from an initial state to a final state. Dynamic models for complex chemical processes typically consist of large systems of ordinary differential equations and algebraic equations. Therefore, dynamic process simulation is computationally intensive. Dynamic simulators typically contain three units (i) thermodynamic and physical properties packages, (ii) unit operation models, (hi) numerical solvers. Dynamic simulation is used for batch process design and development, control strategy development, control system check-out, the optimization of plant operations, process reliability/availability/safety studies, process improvement, process start-up and shutdown. There are countless dynamic process simulators available on the market. One of them has the commercial name Hysis [2.3]. 

Professor Wakeham is interested in the relationship between the bulk thermophysical properties of fluids and the intermolecular forces between the molecules that comprise them. Thus, at one extreme, he is involved in the determination of intermolecular forces from measurements of macroscopic properties and the development and application of the statistical mechanics and kinetic theory that interrelate them. He is also actively involved in the measurement of the thermophysical properties of fluids under a very wide variety of thermodynamic states. The same thermophysical properties find application in the process industries within the design of a plant. A part of Professor Wakeham s activities are therefore concerned with the representation and extension of a body of accurate information on thermophysical properties in a fashion that allows their use with software packages for process simulation. 

Tabulated data for experimental adsorption isotherms are fitted with analytical equations for the calculation of thermodynamic properties by integration or differentiation. These thermodynaunic properties expressed as a function of temperature, pressure, and composition are input to process simulators of atdsorption columns. In addition, anaJytical equations for isotherms are useful for interpolation and cautious extrapolation. Obviously, it is desirable that the Isotherm equations agree with experiment within the estimated experimental error. The same points apply to theoretical isotherms obtained by molecular simulation, with the requirement that the analytical equations should fit the isotherms within the estimated statistical error of the molecular simulation. 

Throughout this book, we have seen that when more than one species is involved in a process or when energy balances are required, several balance equations must be derived and solved simultaneously. For steady-state systems the equations are algebraic, but when the systems are transient, simultaneous differential equations must be solved. For the simplest systems, analytical solutions may be obtained by hand, but more commonly numerical solutions are required. Software packages that solve general systems of ordinary differential equations— such as Mathematica , Maple , Matlab , TK-Solver , Polymath , and EZ-Solve —are readily obtained for most computers. Other software packages have been designed specifically to simulate transient chemical processes. Some of these dynamic process simulators run in conjunction with the steady-state flowsheet simulators mentioned in Chapter 10 (e.g.. SPEEDUP, which runs with Aspen Plus, and a dynamic component of HYSYS ) and so have access to physical property databases and thermodynamic correlations. 

Most flowsheet calculations are carried out using commercial process simulation programs. The process simulation programs contain models for most unit operations as well as thermodynamic and physical property models. All the commercial programs feature some level of custom modeling capability that allows the designer to add models for nonstandard operations. 

The multiphase method provides a practical screening tool for industrial process research and development, even though under many circumstances the nonequilibrium effects such as supersaturation of solutions, retarded mass transfer or reaction kinetics and inhomogeneity of suspensions limit the applicability of the thermodynamic calculations. When the thermodynamic multiphase models are developed towards process simulation tools, one should incorporate such methods that include the effects of these non-equilibrium factors. They must be based on... 

Thermophysical properties, phase equilibria, and solution chemistries are the underlying physical and chemical phenomena of industrial chemical processes. Rigorous thermodynamic modeling of such phenomena establishes a sound scientific foundation for simulation of industrial chemical processes and subsequent process development, optimization, and control. 

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