Simulation processes

Process simulation in Matlab Simulink is explained to enable the user to develop linear and non-linear models and compare them. First, the use of Simulink is introduced by designing a model for a tank with free outflow of liquid. Next, the simulation of a chemical reactor dscribed by a component balance and energy balance is developed and explained. 

For the ethanol production process (including the CHP), the non-random two liquid (NRTL) property method with Henry components was used, which was also recommended by the Aspen Plus guidelines, as it is suitable for, among others, liquid-phase reactions and azeotropic alcohol separation. Some compounds involved in the ethanol production do not exist in the conventional Aspen Plus database. Therefore, physical properties of these components were taken from a database developed by the National Renewable Energy Laboratory (NREL) for biofuel components (Wooley et al., 1999 Aspentech, 2011). 

For simulation of the ethanol dehydration plant, the Peng-Robinson property method was applied for all non-electrolyte systems. This method is suitable for handling light hydrocarbons, which are very typical in an ethylene production facUity. For simulations involving electrolytes (caustic tower), the electrolyte NRTL (ELECNRTL) property method was applied as this property method is more suitable (Aspentech, 2011). 

Production of ethanol from Ugnocellulosic materials such as wood is still at the research stage. Today, only pilot plants are in operation (Jones et al., 2010). A variety of process configurations are suggested in the literature. 

Pretreatment The size-reduced Ugnocellulosic feedstock is pretreated to disintegrate the complex wood matrix consisting of cellulose, hemicelluloses and lignin. Thereby, the yield of the downstream hydrolysis stage can be increased from 20 up to 90%. In order to increase the hydrolysis yield, a two-step pretreatment is applied, since the optimal conditions for hemicellulose and cellulose sugar recovery differ. Two-step pretreatment shows a higher 

Reactor Stoichiometric reactor model (RStoic) for reactions, tubular kinetic reactor (RPlug) to estimate temperature drop. Aluminium-oxide catalyst dominating reaction ethanol conversion 

The real payoff of the simulation of chemical processes is in the intelligent interpretation of results by the engineer. At this point, the engineer must ascertain whether the model is a valid representation of the actual process or whether it needs revision and updating. The engineer must make sure that the results seem reasonable. Decisions have to be made on whether or not the simulated process achieves the objectives stated in the definition of the problem. Also, reasonable alternatives should be investigated in an effort to improve performance. 

There are some definite limitations of process simulation of which the engineer must be aware. These include the following  

Lack of good data and knowledge of process mechanisms The success of process simulation depends heavily on the basic information available to the engineer. 

The character of the computational tools There are certain types of equation sets that still pose a problem for numerical methods. These include some nonlinear algebraic and certain nonlinear partial differential equation sets. 

The danger of forgetting the assumptions made in modeling the process This can lead to placing too much significance on the model results. 

1 Phase Equilibrium Modeling for Nylon-6 Process Simulation 

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 

The book is organized into 11 chapters followed by six appendices, as listed in Table 1.1. Each chapter treats a type of chemical engineering phenomenon, such as process simulation or convective diffusion. The six appendices give additional details about each computer program. 

As a modem chemical engineering student, many of you are computer-sawy. This book assumes that you are not a complete beginner, but have some experience with spreadsheet programs such as Excel. The chapters provide examples and step-by-step instractions for using the computer programs to solve chemical engineering problems. If necessary, you can find more detailed information about the individual programs in the Appendices. 

Chapters 2-5 deal with chemical engineering problems that are expressed as algebraic equations - usually sets of nonlinear equations, perhaps thousands of them to be solved together. In Chapter 2 you can study equations of state that are more complicated than the perfect gas law. This is especially important because the equation of state provides the thermodynamic basis for not only volume, but also fugacity (phase equilibrium) and enthalpy (departure from ideal gas enthalpy). Chapter 3 covers vapor-liquid equilibrium, and Chapter 4 covers chemical reaction equilibrium. All these topics are combined in simple process simulation in Chapter 5. This means that you must solve many equations together. These four chapters make extensive use of programming languages in Excel and MATE AB. 

Chapter 6 introduces mass transfer problems such as distillation and absorption. Chapter 7 

TABLE 1.1. Computer Programs Used in Different Chapters 

Apply the knowledge gained from previous chapters together. 

Simulate an entire process using Hysys, PRO/II, Aspen Plus, and SuperPro software packages. 

Ethyl chloride is produced by the gas-phase reaction of HCI with ethylene over a copper chloride catalyst supported on silica. The feed stream is composed of 50% HCI, 48 mol% C2H4, and 2 mol% Nj at 100 kmol/h, 25°C, and 1 atm. Since the reaction achieves only 90 mol% conversion, the ethyl chloride product is separated from the unreacted reagents, and the latter is recycled. The separation is achieved using a distillation column. To prevent accumulation of inerts in the system, a portion of the distillate is withdrawn in a purge stream. Design a process for this purpose using Hysys, PRO/II, Aspen Plus, and SuperPro software packages. 

The reaction of ethylene (C2H4) and hydrogen chloride (HCI) over a copper chloride catalyst supported on silica to produce of ethylene chloride (CjHjCI) is a highly exothermic reaction. In this example, the reaction is assumed to take place in an isothermal conversion reactor. The heat evolved from the reaction is removed from the reactor to keep the reaction at constant temperature. The reactor effluent stream is compressed, cooled, and then separated in a flash unit followed by a distillation column. The flash and distillation top products are collected and then recycled to the reactor after a portion of the stream is purged to avoid accumulation of an inert component (Nj). The recycled stream is depressurized and heated to the fresh feed stream conditions. The liquid from the bottom of the flash enters a distillation column where ethyl chloride is separated from unreacted HCI and ethylene. The entire process is simulated using Hysys/Unisim, PRO/II, Aspen, and SuperPro Designer software packages. 

Process flow sheet for the production of ethylene chloride. 

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Under certain conditions of temperature and pressure, and in the presence of free water, hydrocarbon gases can form hydrates, which are a solid formed by the combination of water molecules and the methane, ethane, propane or butane. Hydrates look like compacted snow, and can form blockages in pipelines and other vessels. Process engineers use correlation techniques and process simulation to predict the possibility of hydrate formation, and prevent its formation by either drying the gas or adding a chemical (such as tri-ethylene glycol), or a combination of both. This is further discussed in SectionlO.1. 

The above example is a simple one, and it can be seen that the individual items form part of the chain in the production system, in which the items are dependent on each other. For example, the operating pressure and temperature of the separators will determine the inlet conditions for the export pump. System modelling may be performed to determine the impact of a change of conditions in one part of the process to the overall system performance. This involves linking together the mathematical simulation of the components, e.g. the reservoir simulation, tubing performance, process simulation, and pipeline behaviour programmes. In this way the dependencies can be modelled, and sensitivities can be performed as calculations prior to implementation. 

Simulation tools are available for sizing and analyzing plants. However, these tools do not replace the designer as the architect of the plant because selection of process and the sequenciag of units are the designers choices. The same is tme for heat-exchanger networks. Most of the commercial process simulator companies market computer modules that perform some of the tedious steps ia the process but none is able to remove the designer from the process. 

Prediction of reverse osmosis performance is usefiil to the design of RO processes. Simulation of RO processes can be separated iato two categories. The first is the predictioa of membrane module performance. The second is the simulation of a network of RO processes, ie, flow sheet simulations, which can be used to determine the optimum placement of RO modules to obtain the overaH process objective. 

Given the first type of simulation, it is advantageous to be able to design a system of RO modules that can achieve the process objective at a minimal cost. A model has been iategrated iato a process simulation program to predict the stream matrix for a reverse osmosis process (132). In the area of waste minimization, the proper placement of RO modules is essential for achieving minimum waste at a minimum cost. Excellent details on how to create an optimal network of RO modules is available (96). 

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. 

A future goal for the integration of graphics and process design simulators is to be able to use an interactive graphics program to prepare the input to the process simulator. This capabiHty would allow tme on-line process modification, flow-sheet optimization, and process optimization, and is likely to be one of the key developments in this field in the 1990s (99). 

The creation and analysis of process flow sheets has become much easier because of the availabihty of automated systems to draw and revise them. The goal of the use of the flow sheet as the input for process simulation and for process control is likely to be achieved reasonably soon. The use of interactive graphic displays for process monitoring and control is pervasive today. 

A process-simulation program almost always contains a physical property service, because the quaflty of process design ultimately depends on the way in which the laws of physics and chemistry are appfled to the problem. Accordingly, the quaflty of this service is an important consideration to the user of a flow-sheeting system. 

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). 

Process simulators stop generally at the process specifications for the equipment. For the detailed mechanical design of the equipment, such as heat exchangers and distillation columns, stand-alone programs are often used. They make process calculations, size the equipment, calculate thermal and mechanical stresses, design mechanical support of the parts of the equipment, design inlet and outlet nozzles, etc. 

A wide array of general-purpose distillation packages are available to the engineer. Some of the distillation software is stand-alone, whereas other packages are a part of a general-purpose flow sheet or process-simulation system. Because distillation is so universal, all process simulators have one or more distillation program modules for this unit operation. Often the nature of the distillation modules determines the suitabiUty of or the preference for the use of a specific simulator for an appHcation. 

Many industrial separations require a series of columns that are connected in specific ways. Some distillation programs can model such a system as a hypothetical single column with arbitrary cross-flows and connections and then carry out the distillation calculations for the modeled hypothetical column. Alternatively, such a system can be modeled as a process flow sheet using a process simulator. 

Many process simulators come with optimizers that vary any arbitrary set of stream variables and operating conditions and optimize an objective function. Such optimizers start with an initial set of values of those variables, carry out the simulation for the entire flow sheet, determine the steady-state values of all the other variables, compute the value of the objective function, and develop a new guess for the variables for the optimization so as to produce an improvement in the objective function. 

Mathematically speaking, a process simulation model consists of a set of variables (stream flows, stream conditions and compositions, conditions of process equipment, etc) that can be equalities and inequalities. Simulation of steady-state processes assume that the values of all the variables are independent of time a mathematical model results in a set of algebraic equations. If, on the other hand, many of the variables were to be time dependent (m the case of simulation of batch processes, shutdowns and startups of plants, dynamic response to disturbances in a plant, etc), then the mathematical model would consist of a set of differential equations or a mixed set of differential and algebraic equations. 

Whereas process simulation includes quantitative analysis of a design given the stmcture of the design, process synthesis involves determining the stmcture that will meet the requirements of the design as well as finding the best stmcture for the requirements. For example, if components A, B, C, and D whose relative volatOities were in the order D, C, B, and A were to be separated by distillation for which each column produced a top and a bottom fraction, five schemes of three columns arise as possible stmctures (53) (Fig. 8). 

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). 

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

In process simulation it is necessary to calculate enthalpy as a function of state variables. This is done using the following formulas, derived from the above relations by considering S and H as functions of T and p. 

Classification Process simulation refers to the activity in which mathematical models of chemical processes and refineries are modeled with equations, usually on the computer. The usual distinction must be made between steady-state models and transient models, following the ideas presented in the introduction to this sec tion. In a chemical process, of course, the process is nearly always in a transient mode, at some level of precision, but when the time-dependent fluctuations are below some value, a steady-state model can be formulated. This subsection presents briefly the ideas behind steady-state process simulation (also called flowsheeting), which are embodied in commercial codes. The transient simulations are important for designing startup of plants and are especially useful for the operating of chemical plants. 

Design and Operation of Azeotropie Distillation Columns Simulation and design of azeotropic distiUation columns is a difficult computational problem, but one tnat is readily handled, in most cases, by widely available commercial computer process simulation packages [Glasscock and Hale, Chem. Eng., 101(11), 82 (1994)]. Most simida-... 

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. 

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