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Process simulation ProSimPlus. Steady-state simulation and optimization of processes ProSimPlus is a process engineering software that performs rigorous mass and energy balance calculations for a wide range of industrial steady-state processes. It is used in design as well as in operation of existing plants for process optimization, units Simulation and optimization of nitric acid plants and nitrous vapors absorption ProSimPlus HNO3 is a unique process engineering tool, specifically designed to model nitric acid production plants and nitrous vapors absorption units.
It allow ProSim Suite. The power of all ProSim simulation software in one convenient, cost effective package! ProSim Suite provides access to any of ProSim's software for steady-state process simulation and optimization, thermodynamic calculations, batch process simulat Simulis Thermodynamics. Mixture properties and fluid phase equilibria calculations Simulis Thermodynamics is a calculation server for thermophysical properties and phase equilibria calculations on pure components and mixtures.
Software of Simulis Thermodynamics Based on Simulis Thermodynamics, ProPhyPlus is a stand-alone calculation software to run all the thermodynamic calculations, without any programming. Simulation of chemical reactors in batch mode Reducing production costs, responding to environmental or safety regulations, saving time in scale-up phases and new products launch Not all simulation tools necessarily lend themselves to the same types of output results, so it is important to clearly define expectations so that tool selection is an informed process.
The next step is to clearly define the required performance of the simulation to be developed. We will focus on three primary dimensions of performance:. Note that these dimensions of performance are often contradictory; not all performance dimensions can be achieved simultaneously. Do you want high fidelity? Then the cost will likely be very high.
In general, you should prioritize those three dimensions of performance. A common pitfall is to begin a modeling and simulation effort with unrealistic expectations. Is it really feasible to model all the process components to every little process detail with high fidelity?
Probably not. Is it possible to model the entire process to every little detail with many simplifying assumptions? Probably, but it is unlikely to be useful. When defining requirements and expectations for a modeling and simulation effort it is recommended to begin by choosing the required fidelity. How accurate result is required? A successful effort will always begin with this question because, without a meaningful degree of fidelity, any model and simulation activity is meaningless.
Once the required fidelity is established, one can then begin placing limitations on simulation capabilities accordingly. Cost is generally bound by an allocation of resources.
So given a known cost constraint and a known fidelity requirement, we can then begin building a conceptual model for the simulation. The target fidelity will mandate the inclusion of particular system characteristics with great detail and inputs with particular degrees of accuracy, and also allow for relaxation on other system details and input accuracy. Note that this exercise requires a strong understanding of the system being modeled and on the underlying concepts.
One of the first decisions that the simulation developer must face is to determine what he or she is attempting to demonstrate through simulation and what is the most simplistic model that captures all necessary components.
The engineering tradeoff is that increased detail can provide higher fidelity output from the model, but at the cost of complexity — potentially introducing error and certainly increasing debugging time and execution time. The designer must also realize that a model is always an abstraction from the real world. Regardless of the level of detail included, a simulation will always be an approximation of the real system; an arbitrarily high degree of fidelity is generally not possible.
Also, the cost of increased fidelity at some point becomes greater than the marginal utility of the additional fidelity. How much detail is sufficient in a simulation to capture the essence of the real world process being modeled? Unfortunately, the answer to this question is that it depends on the particular simulation scenario.
The simulation engineer should first decide exactly what is the problem that he or she seeks to address through simulation. What are the inputs and the outputs of the model? Some outputs may be independent of specific details in the model, while others may be correlated and therefore seriously affected if those components are abstracted. Simulation always takes the form of an abstraction of a system to allow the designer to gain some insight from investigating various operating scenarios of the system.
Yet in other cases, the researcher desires to investigate a process reaction to a single condition that may be unlikely to occur in real life. Perhaps testing the actual system under this condition could be harmful and simulation is the only way to examine the problem. The next step is to decide how much of the system must be implemented for the simulation results to be valid.
Ultimately, the simulation engineer is going to have to decide the level of detail required in his or her simulation. First, the developer must consider the engineering tradeoffs between adding more detail to a model and increased computational time, increased complexity, and increased debugging time. A more abstract approach that focuses only on the basic behavior of a process is generally very flexible, easier to debug, and has a shorter execution time.
But, it may not capture the behavior of interest. The advances in basic knowledge and model-based process engineering methodologies are resulting with an increasing demand for models.
The observations given here are commentaries and considerations about some aspects of modeling with the focus on:. Correctness, reliability and applicability of models are very important. For most engineering purposes, the models must have a broad range of applicability and they must be validated.
If the models are not based on these principles, their range of applicability is usually very narrow, and they cannot be extrapolated. In many modeling and simulation applications in the process industry, kinetic data and thermodynamic property methods are the most likely sources of error.
Errors often occur when and because the models are used outside the scope of their applicability. With the advent and availability of cheap computer power, process modeling has increased in sophistication, and has, at the same time, come within the reach of people who previously were deterred by complex mathematics and computer programming. Simulators are usually made of a huge number of models, and the user has to choose the right ones for the desired purpose.
Making correct calculations is not usually trivial and requires a certain amount of expertise, training, process engineering background and knowledge of sometimes very complex phenomena. The problem with commercial simulators is that, since the simulations can be carried out fairly easily, choosing the wrong models can also be quite easy. Choosing a bad model can result in totally incorrect results. Moreover, with commercial simulators, there is no access to the source code and the user cannot be sure that the calculations are made correctly.
The existing commercial flowsheeting packages are very comprehensive and efficient, but the possibility of misuse and misinterpretation of simulation results is high. In CFD and molecular modeling, the results are often only qualitative. The methods can still be useful, since the results are applied to pre-screen the possible experiments, the synthesis routes and to visualize a particular phenomenon.
This role is not clear, except in the cases of big companies which have their own research and development divisions. The properly developed models and simulators are then frequently used, as we have already shown, during the life-cycle of all the particular processes or fabrications that give the company its profile. The use of modeling and simulation in small and medium size manufacturing companies is quite limited. Since small manufacturing companies and university researchers do not cooperate much, awareness and knowledge about modern Computer Aided Process Engineering tools are also limited.
There are of course exceptions among manufacturing companies. Some small and medium size engineering and consulting companies are active users of modeling and simulation tools, which allows them to better justify the solutions they propose to their clients.
Modeling and simulation are usually regarded as support tools in innovative work. They allow fast and easy testing of innovations. The use of simulators also builds a good basis for understanding complex phenomena and their interactions. In addition, it also builds a good basis for innovative thinking. It is indeed quite important to understand what the simulators really do and what the limitations of the models are. As a consequence, access to source codes is the key to the innovative use of models and simulators.
Many commercial programs are usually stuck in old thinking and well-established models, and then, the in-house-made simulators are quite often better innovative tools. Molecular modeling can be used, for example, in screening potential drug molecules or synthesis methods in order to reduce their number.
The existing molecular modeling technology is already so good that there are real benefits in using it. Molecular modeling can be a very efficient and invaluable innovative tool for the industry. The computers are not creative, which means that these tools cannot be innovative. However, they can be used as tools in innovative development work. While most of the modeling and simulation methods are just tools, in innovative work, process synthesis can be regarded as an innovation generator, i.
Models are not only made for specific problem solving. They are also important as databases and knowledge management or technology transfer tools. For example, an in-house-made flowsheet simulator is typically a huge set of models containing the most important unit operation models, reactor models, physical property models, thermodynamics models and solver models from the literature as well as the models developed in the company over the years or even decades.
Ideally, an inhouse-made simulator is a well-organized and well-documented historical database of models and data. A model is also a technology transfer tool through process development and process life cycle. The problem is that the models developed in earlier stages are no longer used in manufacturing. The people in charge of control write simple models for control purposes and the useful models from earlier stages are simply forgotten.
Ideally, the models developed in earlier stages should be used and evaluated in manufacturing, and they should provide information to the research stage conceptual design stage and detailed design stage. Different tools are used in each process life cycle stage. However, simulators with integrated steady-state simulation, dynamic simulation and control and operator-training tools are already being developed. The problem is that the manufacturing people are not always willing to use the models, even though the advantages are clear and the models are made very easy to use.
The importance of modeling and simulation for industrial use is generally promoted, in each factory, by the youngest engineers. The importance of computer-aided tools to the factory level is best understood when the application of modeling and simulation has a history. The importance of modeling and simulation is not understood so well in the sectors that do not use computer-aided tools. Technical universities have a key role in the education of engineers as well as in research and development.
Indeed, in the future, the work of a process engineer will be more and more concerned with modeling and computation. Moreover, the work will be all the more demanding so that process engineers will need to have an enormous amount of knowledge not only of physics and chemistry, but also of numerical computation, modeling and programming.
Chemical engineering can be defined from many different aspects. However, all the scientists and professionals agree that the process is the center of it. Process simulation as discipline uses mathematical models as basis for analysis, prediction, testing, detection of a process behavior unrelated to whether the process is existing in reality or not. Process simulation is there to increase the level of knowledge for a particular process and chemical engineering in general.
So, when those two concepts are put together, we can look into the chemical engineering as a discipline defining how the process should be developed and simulation as the tool helping us to explore the options.
Chemical engineering needs to know how the process should be designed while chemical engineers use the simulation to explore all the process design options and define the optimal one.
Process simulation is today applied in almost all disciplines of chemical engineering and engineering in general.
It is the inevitable part of disciplines from process design, research and development, production planning, optimization, training and education to decision-making which makes it one of the most important disciplines of engineering.
A wide palette of simulation solutions is mentioned below. Process design represents one of the traditional applications of mathematical modeling and simulation.
Process synthesis and process design use steady state models to define process flowsheet accompanied with material and heat balance. The objectives of process design are to find the best process flowsheet and optimum design conditions.
This can be a complex task which needs to explore great number of options and is not possible without the usage of mathematical models and process simulation.
Chemical engineering is like a fountain of challenges producing a continuous inspiration for researchers on their projects. No research project is possible without certain amount of mathematical modeling and process simulation involved. Thus, it can minimize the amount of experimental research.
There are certain parts of the process which continuously need evaluation and improvement. Reactor sections are very often that particular part of the process, especially if the catalyst is involved in the reactions. Engineers working with research and development are involved with detailed mathematical models which include a huge number of physical and thermodynamic properties to help them evaluate current or improved process conditions.
Production planning and scheduling accompanied with economic calculations represent important discipline which is placing a chemical process or industry on the market. Ones the process is running, its profitability becomes one of the most important tasks for a chemical engineer.
Process profitability is explored and defined through production planning and scheduling models which are used to provide the answers to the questions how to define optimal production and operation. Change of market, change in feeds and products need constant evaluation in order to guarantee profitability. Mathematical models are used for simulation of all the possibilities as the guidance on the way to the optimum solution. This is done in order to help management to make the right decisions.
Dynamic simulation is analyzing an optimal process operation, safety, environmental constraints and controllability to help define control strategies, goals and control parameters.
Dynamic simulation is first used during process design phase to help define control strategies. When the process is in operation, it is used to analyze, test and optimize operating conditions. This type of analysis can give answers about process bottlenecks and how to resolve them. Simulation is of great support to enable training and education of engineers and operators. It is present in the form of the Operator Training Simulator. As education of operators and engineers is becoming more and more important challenge due to modern and more complex technologies, OTS is the powerful learning tool which enables natural feeling of a process control in the virtual reality.
Training of defined scenarios, process start-up and shut-down is representing the enormous impact on process safety and competence of operators to be able to handle unexpected conditions. It is also giving them more knowledge to deal with daily operation challenges. Dynamic models are enabling chemical engineers to continuously run the unit with defined optimization strategy, having the process knowledge transformed in the shape of the mathematical model hidden inside the control algorithm, called Advanced Process Control APC.
This approach is giving engineers and operators the ability and operators to almost run the unit such as operating the plane on auto pilot, constantly taking care of economical benefits.
Decision-making process supported by different kinds of calculations, models and simulations is far more efficient one than the one built on assumptions. There is a whole formulation of how different models can support the decision-making process to make it less exasperating and difficult. In conclusion, the short survey about process simulation is giving us a message: there is almost no discipline of chemical engineering that can afford to ignore the importance of process simulation.
It is the inevitable part of chemical engineering and engineering in general. Process simulation is like a flashlight in the hands of a chemical engineer guiding one to the best engineering solution. The story about thermodynamics can hardly be a simple one.
If it somehow could be a simple one and the world would be ideal — we would have only one nice equation that is good enough to describe any system. But in the unideal world, things are far from that. When developing a process model, a chemical engineer should have enough knowledge to be able to choose from a large number of thermodynamic systems. Chemical engineering as a field is progressing day by day and as a result of that the number of thermodynamic equations and parameters is increasing too with the aim to improve mathematical descriptions of different systems.
With more complex thermodynamic systems, there comes a joint problem of more and more challenging mathematical operations. So, our knowledge becomes very important in selecting the most appropriate thermodynamics package. We will try to give some simple and practical instructions through this highly complex field of chemical engineering and process simulation. In this case we are not looking into details of the system, only using the data to build the model.
Examples are applications of artificial neural networks, linear regression etc. However, when talking about process simulation, most of the time we do refer to rigorous models and simulation tools, such as Hysys, Chemcad, Pro II etc. This approach is based on traditional chemical engineering laws and thermodynamics represent the essence of it.
Therefore, when building a model in any process simulator, we need to make a selection of the proper thermodynamic system. One thing to have in mind: taking the wrong way while developing a process model can cause a huge waste of time and misleading results. So, try to be careful! Thermodynamics finds its origin in experience and experiment, from which are formulated a few major postulates that form the foundation of the subject. Among those are 1st and the 2nd law of thermodynamics, the definition of enthalpy, entropy, equilibrium etc.
Selection of the appropriate thermodynamic package is one of the first steps when building the mathematical model. It is also one of the most important steps because a simple click of a mouse in most of the simulation programs will have the critical impact on simulation results.
We might even not get the results. The choice of a thermodynamic package will have an impact on:. Thermodynamic packages consist of different sets of data and equations systems which represent a group of methods to perform all necessary thermodynamic calculations. Thermodynamic packages consist of all chemical and physical component properties together with different thermodynamic models which are applicable for different systems dependent on components and working conditions of the process pressure, temperature.
It is our task to select one of them when building a model. Also, it has to be noted that most applications require only one set, but complex flowsheets may be modeled best with several. Overview of the modeled process refers to a review of the component list and expected working conditions: are components liquids or gasses, are they mostly hydrocarbons, is there any specific components such as H 2 S for example etc.
When looking into temperature and pressure characteristics, it should be noted if the expected temperatures and pressures are around atmospheric or are they significantly higher. Some simulation programs may suggest what thermodynamic system suits best for a defined component list.
However, it is always good to review the default selection. The table summarizes the priorities when choosing the thermodynamic model. The best selection is defined with « 1 », a little bit less appropriate but still possible with « 2 » and so on. Attention should be paid on operating pressure. Upon the selection of the thermodynamic model, you can continue your work. In case you are facing any problems related to model accuracy or convergence, save your work, do another copy of your simulation and try to use another thermodynamic model for your simulation.
Selecting the proper thermodynamics can be a challenge many times, especially while modelling more complex process or a process with many different types of components.
Use this information as a general guidance and in case of facing difficulties, you can refer to some of the following books:. In literature, there can be found definitions such as « an image of reality from a particular viewpoint ». A mathematical model of a real chemical process is a mathematical description combining experimental facts and establishing relationships between the process variables Babu. Definitions are differing almost as the models: in viewpoint, in the level of details and in the goal of development.
From practical point of view, these 3 points are important to have in mind while developing a model and analyzing the relationship between the model and the reality:. There is no way that two people who work independently could develop the model which would look the same and function the same, no matter what tool they would use. As we perceive the colors differently, every person looks into a problem differently with the different knowledge level and different previous experience and it is inevitable that the developed model is different too.
When developing a model of a particular operation, such as a distillation column, the different approach is employed when model is developed with the purpose to define the sizing parameters of the column or when the column has to be analyzed to explore the control strategy or product quality. Dependent on the purpose, different mathematical method and different level of details have to be applied. Preparation for any project that involves the process simulation and employment of process models requires answers to these questions:.
Dependant on the model type, the simulation tool and adequacy of the data — one can build more or less accurate model. Is it a model then an art of science? In a way, it can be accepted as a creative perspective of science and engineering.
The model needs to take into account properties of all the materials and other physical characteristics defined by temperature, flows, pressures and composition. A good model should reflect the important factors affecting a process and must not be crowded with minor, secondary factors that will complicate the mathematical analysis and might render the investigation difficult to evaluate. Depending on the process under investigation, a mathematical model may be a system of algebraic or differential equations or a mixture of both.
It is important that the model should also represent with sufficient accuracy both quantitative and qualitative properties. Models are used for a variety of applications, such as the study of the dynamic behavior, process design, model-based control, optimization, controllability study, operator training, and prediction. These models are usually based on physical fundamentals, conservation balances, and additional equations. Because process simulation has become one of the most important disciplines of process industry with globally growing market, the aim of this portal is to bring it closer to engineers and industry, connect the vendors and the community it serves via the web-site, e-newsletters, social networking, written and video lessons, demonstrations, product reviews, events and other digital media offerings.
We will be promoting knowledge and induce communication among the professionals on subjects related to process simulation, operator training simulators and trainings, process design and optimization, advanced process control and real time optimization, process analysis and monitoring.
We are here to serve you! You are very welcome to give us your feedback, let us know what are topics of your interest, what are the products and vendors of your interest and to share with us your stories! Peer reviewed Direct link. An Exercise in Validation of Computational Results.
Robert; Cutlip, Michael B. Chemical Engineering Education , v42 n1 p Win Applications of several of these packages to typical chemical engineering problems have been demonstrated by Cutlip, et al. Much has been written about this popular software, more than books serving more than 1 million users. Its operation is based on the use of. A script is basically a number of operations that we want to perform in a certain sequence. Functions can be user-defined or typical operations such as equation solving or differential equations.
It is an environment for dynamic simulation and process control. Each of the blocks can contain a subsystem inside, which is helpful for big problems. We only need to select a number of blocks and with the right button of the mouse, click and select create subsystem. Simulink is easier to used for engineers because it does not require any programming skills, therefore models can be build using blocks instead of defining functions.
The simulation, design, and optimization of a chemical process plant, which comprises several processing units interconnected by process streams, are the core activities in process engineering. These tasks require performing material and energy balancing, equipment sizing, and costing calculation. A computer package that can accomplish these duties is known as a computer-aided process design package or simply a process simulator.
The process simulation market underwent severe transformations in the — decade. The CHEMCAD suite includes six products that can be purchased individually or bundled as needed for specific industries, projects, and processes. Two similar software packages with all the functionalities that process simulator should have are also the most widespread among chemical engineers. AspenTech has a wide portfolio of modeling tools, among them most important and most known are process simulation tools Aspen Hysys and Aspen Plus.
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