Reactor Process Simulations

Model simulations of reactor operation are convenient in order to study  

In this study the steady-state kinetic model for the methanol synthesis on a commercial Cu/Zn0/Al203 catalyst developed by Vanden Bussche and Proment [13] are used. 

The chemistry mechanism is based on three overall reactions  

The first and the second reaction are hydrogenation reactions of CO2 and CO, respectively. The last reaction is the water gas shift reaction (WGS). 

The reaction steps considered developing the reaction kinetics were given, and the rate determining steps (RDS) are indentified  

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Fig. 2. High speed photos of organic film—high velocity air dynamics in falling film sulfonation process (a) and (b) are vertical flat plate organic—air dynamics where (a) shows BAB—air at top, (b) BAB sulfonic acid—air at bottom of reactor (c) simulated AUied-type concentric reactor inner cylindrical...

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). 

The second classification is the physical model. Examples are the rigorous modiiles found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the ac tual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters oearing some relation to theoiy (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These modds provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

In the Monsanto/Lummus Crest process (Figure 10-3), fresh ethylbenzene with recycled unconverted ethylbenzene are mixed with superheated steam. The steam acts as a heating medium and as a diluent. The endothermic reaction is carried out in multiple radial bed reactors filled with proprietary catalysts. Radial beds minimize pressure drops across the reactor. A simulation and optimization of styrene plant based on the Lummus Monsanto process has been done by Sundaram et al. Yields could be predicted, and with the help of an optimizer, the best operating conditions can be found. Figure 10-4 shows the effect of steam-to-EB ratio, temperature, and pressure on the equilibrium conversion of ethylbenzene. Alternative routes for producing styrene have been sought. One approach is to dimerize butadiene to 4-vinyl-1-cyclohexene, followed by catalytic dehydrogenation to styrene ... 

Isik M, Sponza DT (2005) Substrate removal kinetics in an upflow anaerobic sludge blanket reactor decolorising simulated textile wastewater. Process Biochem 40 1189-1198... 

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. 

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. 

Figure 2.11 Equilibrium constant for the formation of DEG from EG as a function of temperature, calculated by using the Gibbs Reactor model of the commercial process simulator Chemcad (Chemstations)...

Before leaving this section we consider a slightly different optimization problem that may also be expensive to solve. In flowsheet optimization, the process simulator is based almost entirely on equilibrium concepts. Separation units are described by equilibrium stage models, and reactors are frequently represented by fixed conversion or equilibrium models. More complex reactor models usually need to be developed and added to the simulator by the engineer. Here the modular nature of the simulator requires the reactor model to be solved for every flowsheet pass, a potentially expensive calculation. For simulation, if the reactor is relatively insensitive to the flowsheet, a simpler model can often be substituted. For process optimization, a simpler, insensitive model will necessarily lead to suboptimal (or even infeasible) results. The reactor and flowsheet models must therefore be considered simultaneously in the optimization. 

Possibly the chemical industry does not have as much need for mathematical models in process simulation as does the petroleum refining industry. The operating conditions for most chemical plants do not seem subject to as broad a choice, nor do they seem to require frequent reappraisals. However, this is a matter which must be settled on the basis of individual circumstances. Sometimes the initial selection of operating conditions for a new plant is sufficiently complex to justify development of a mathematical model. Gee, Linton, Maire, and Raines describe a situation of this sort in which a mathematical model was developed for an industrial reactor (Gl). Beutler describes the subsequent programming of this model on the large-scale MIT Whirlwind computer (B6). These two papers seem to be the most complete technical account of model development available. However, the model should not necessarily be thought typical since it relies more on theory, and less on empiricisms, than do many other process models. 

Another important modeling aspect is the simulation of catalytic process parameters and reactor configurations. Such models are typically associated with process engineering, and involve computational fluid dynamics and heat- and mass-transfer calculations. They are essential in the process planning and scale-up. However, as this book deals primarily with the chemical aspects of catalysis, the reader is referred to the literature on industrial catalysis and process simulations for further information [49,56]. 

Figure 6.1 summarizes the key areas of computer applications in catalysis research. Note that this chapter does not cover reactor and process simulations, although of course these are performed using computers, and are essential for designing industrial catalytic processes. Textbooks on chemical engineering discuss these subjects in depth [2]. 

Abstract In this paper, we discuss the results of a preliminary systematic process simulation study the effect of operating parameters on the product distribution and conversion efficiency of hydrocarbon fuels in a reforming reactor. The ASPEN One HYSYS-2004 simulation software has been utilized for the simulations and calculations of the fuel-processing reactions. It is desired to produce hydrogen rich reformed gas with as low as possible carbon monoxide (CO) formation, which requires different combinations of reformer, steam to carbon and oxygen to carbon ratios. Fuel properties only slightly affect the general trends. 

Keywords hydrogen production, reformer, efficiency, process simulation, reactor... 

Software tools are applied in every step of process development. Tools for individual reactor simulations such as computational fluid dynamic simulations are not the topic in this chapter. These tools supply only numerical data for specific defined reactor geometry and defined specific process conditions. A change of parameter would demand a complete recalculation, which is often a very time-consuming process and not applicable to a parameter screening. Methods for reactor optimization by CFD are described in detail in the first volume of this series. Tools for process simulation allow the early selection of feasible process routes from a large... 

S //Asa mediator between CFD calculations and macro-scale process simulations, the reactor geometry is represented by a relatively small number of cells which are assumed to be ideally mixed. The basic equations for mass, impulse and energy balance are calculated for these cells. Mass transport between the cells is considered in a network-of-cells model by coupling equations which account for convection and dispersion. The software is capable of optimizing a process in iterative simulation cycles in a short time on a standard PC, but it also requires experimentally-based data to calibrate the software modules to a specific micro reactor. 

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 costs of erection and commissioning of even a very complex micro structured reactor plant are, of course, comparably small. This means that the basic design of a micro structured reactor plant is extremely expensive and attention should be given to minimizing costs, for example, by the application of calculation sheets, the standardization of complete process units or the use of process simulation or planning tools such as AspenPlus . The reduction of this cost factor remains a future task of micro technology. 

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