Simulation unit operation models

Steady-state process simulation or process flowsheeting has become a routine activity for process analysis and design. Such systems allow the development of comprehensive, detailed, and complex process models with relatively little effort. Embedded within these simulators are rigorous unit operations models often derived from first principles, extensive physical property models for the accurate description of a wide variety of chemical systems, and powerful algorithms for the solution of large, nonlinear systems of equations. 

Modular simulators are frequently constructed on three levels. The lowest level consists of thermodynamics and other physical property relations that are accessed frequently for a large number of flowsheeting utilities (flash calculations, enthalpy balances, etc.). The next level consists of unit operations models as described above. The highest level then deals with the sequencing and convergence of the flowsheet models. Here, simultaneous... 

A library of generalized models is supplied in ASPEN to allow the user to simulate coal conversion processes as well as chemical and petroleum processes. A listing of ASPEN s unit operations models is given in Table I. Space does not permit descriptions of the models here, however, the ASPEN project reports (2) discuss their capabilities. 

Flowsheet simulators consist of unit operation models, physical and thermodynamic calculation models and databanks. Consequently, the simulation results are only as good as the underlying physical properties and engineering models. Many steady-state commercial simulators [2.1, 2.2] have some dynamic (batch) models included, which can be used in steady-state simulations with intermediate storage buffer tanks. 

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]. 

These concepts are implemented in the integration platform CHEOPS Component-Based Hierarchical Explorative Open Process Simulator) [252, 409, 462]. The platform provides generic component prototypes and interfaces for the integration of models, solvers, and tools. The generic components are instantiated at run-time by concrete software components and classes representing actual unit operation models, solvers, etc. That way, arbitrary components from the list of available components can be used in the simulation. The list of model and solver components can easily be extended with the components that comply with the abstract structure and interface definitions. 

The basic element in a modular simulator is the unit operation model. A simulation model is obtained by means of conservation equations for mass, energy and momentum. These lead finally to a system of non-linear algebraic equations as ... 

Davis, R.A. (2002) Simple gas permeation and pervaporation membrane unit operation models for process simulators. Chemical Engineering Technology, 25 (7), 717-722. 

The development and the design of new processes are today extensively based on mathematical modelling and simulation. The entire process is modelled through combining individual unit operation models. Several flowsheeting programs (Aspen+ , HySim , ChemCAD Pro II etc.) have already found their place in engineering... 

Reaction models are necessary in the chemical process industries for a number of purposes which are most often related to the modeling, simulation and control of production processes process synthesis, process simulation, plant optimization and production control are typically some of the domains concerned with the use of reaction models within unit operation models. To provide interoperability of reaction models within a number of software applications, a specific part of the CAPE-OPEN standard has been devoted to these simulation components called Reactions Packages. CAPE-OPEN Reactions Packages are described in terms of the interfaces that they must support, their interaction with a process modelling environment and the functionality they are expected to support. The interfaces defined support both kinetic and electrolyte reactions. 

A reaction model is a typical component of simulation systems, along with unit operations, thermodynamic servers, physical properties databanks, etc. The reaction model may provide information on how it is built, or can choose not to provide such information but Just to provide computation mainly of reaction rates so that these terms may be readily used in mass balances within unit operation models. The same applies for energy terms. By implementing a common interface standard, a reaction model component may be deployed on its own, independently of the process simulator it is used in. That develops the reusability of reaction models throughout unit operations and process simulators. A reaction model is contained by a Reactions Package software component exhibiting the specific CAPE-OPEN interfaces discussed here. 

Another example is the systematic analysis undertaken by Palsson et al. on combined SOFC and gas turbine cycles [36]. In combination with a robust and accurate 2-D SOFC model, the system-level model attempts to provide an unbiased evaluation of performance prospects and operational behaviours of such systems. The 2-D SOFC model was integrated into a process simulation tool. Aspen Plus , as a user-defined model, whereas other components constituting the system are modelled as standard unit operation models. Parametric studies can be carried out to gain knowledge of stack and system behaviour such as the influence of fuel and air flow rate on the stack performance and the mean temperature and the effects of cell voltage and compressor pressure on the system efficiency. The pressure ratio is shown to have a large impact on performance and electrical efficiencies of higher than 65% are possible at low-pressure ratios. 

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. 

As with troubleshooting, parameter estimation is not an exact science. The facade of statistical and mathematical routines coupled with sophisticated simulation models masks the underlying uncertainties in the measurements and the models. It must be understood that the resultant parameter values embody all of the uncertainties in the measurements, underlying database, and the model. The impact of these uncertainties can be minimized by exercising sound engineering judgment founded upon a famiharity with unit operation and engineering fundamentals. 

Catalytic crackings operations have been simulated by mathematical models, with the aid of computers. The computer programs are the end result of a very extensive research effort in pilot and bench scale units. Many sets of calculations are carried out to optimize design of new units, operation of existing plants, choice of feedstocks, and other variables subject to control. A background knowledge of the correlations used in the "black box" helps to make such studies more effective. 

Yang et al. (1995) described the application of this scale-up approach. Comprehensive testing programs were performed on two relatively large-scale simulation units for a period of several years a 30-cm diameter (semicircular) Plexiglas cold model and a 3-m diameter (semicircular) Plexiglas cold model, both operated at atmospheric pressure. 

The older modular simulation mode, on the other hand, is more common in commerical applications. Here process equations are organized within their particular unit operation. Solution methods that apply to a particular unit operation solve the unit model and pass the resulting stream information to the next unit. Thus, the unit operation represents a procedure or module in the overall flowsheet calculation. These calculations continue from unit to unit, with recycle streams in the process updated and converged with new unit information. Consequently, the flow of information in the simulation systems is often analogous to the flow of material in the actual process. Unlike equation-oriented simulators, modular simulators solve smaller sets of equations, and the solution procedure can be tailored for the particular unit operation. However, because the equations are embedded within procedures, it becomes difficult to provide problem specifications where the information flow does not parallel that of the flowsheet. The earliest modular simulators (the sequential modular type) accommodated these specifications, as well as complex recycle loops, through inefficient iterative procedures. The more recent simultaneous modular simulators now have efficient convergence capabilities for handling multiple recycles and nonconventional problem specifications in a coordinated manner. 

The principle of integral process development [26] covers much more than just the optimization of a process. This approach begins with computer-aided decision procedures in the conception phase. Tools are available in which the process structure is suggested, for example should the process be a batch or a continuous operation The software tool for process synthesis PROSYN uses databases which include knowledge of experts, material data and calculation models for unit operations. Interfaces to process simulation tools such as ASPENPLUS and material databases are also supplied. PROSYN also delivers an economic evaluation of the future production process. 

As a more critical example concerning the transfer of macroscopic modeling to micro-scale applications, the following example of a simulation of a homogeneous catalytic reaction is described [133], This example also represents a typical approach in process simulation if a new reactor model or a model for a new unit operation... 

The composition of poplar wood was usedasamodel for the feedstock composition however, as used in this simulation, the poplar is modeled as consisting of only cellulose, xylan, and lignin, with compositions of 49.47, 27.26, and 23.27%, respectively. Laboratory results for carbonic acid pretreatment are relatively scarce, so for the purpose of this comparative study, stoichiometry of pretreatment reactions was assumed to be equal to those used in the comparison model (3) cellulose conversion to glucose 6.5% xylan conversion to xylose 75 and lignins solubilized 5%. Thus, economic comparisons made with this model assess different equipment and operating costs but not product yields. For the successful convergence of the carbonic acid model, the simulation required initial specification of several variables. These variables included initial estimates for stream variables and inputs for the unit operation blocks. 

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. 

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