The process simulation is used for the design, development, analysis, and optimization of technical processes and is mainly applied to chemical plants, but also to power stations, and similar technical facilities.
The process simulation is a model-based representation of chemical, physical, biological, and other technical processes and unit operations in software. Basic prerequisites are a thorough knowledge of chemical and physical properties of pure components and mixtures, of reactions, and of mathematical models which, in combination, allow the calculation of a process in computers.
Process simulation software describes processes in flow diagrams where unit operations are positioned and connected by product or educt streams. The software has to solve the mass and energy balance to find a stable operating point. The goal of a process simulation is to find optimal conditions for an examined process. This is essentially an optimization problem which has to be solved in an iterative process.
The process simulation always uses models which introduce approximations and assumptions but allow the description of a property over a wide range of temperatures and pressures which might not be covered by real data. Models also allow to interpolate and extrapolate - within certain limits - and enables the search for conditions outside the range of known properties.
The development of models for a better representation of real processes is the core of the further development of the simulation software. Model development is done on the chemical engineering side but also in control engineering and for the improvement of mathematical simulation techniques. Process simulation is therefore one of the few fields where scientists from chemistry, physics, computer science, mathematics, and several engineering fields work together.
Aa lot of efforts are made to develop new and improved models for the calculation of properties. This includes for example the description of
- thermophysical properties like vapor pressures, viscosities, caloric data, etc. of pure components and mixtures
- properties of different apparatuses like reactors, distillation columns, pumps, etc.
- chemical reactions and kinetics
- environmental and safety-related data
Two main different types of models can be distinguished:
- Rather simple equations and correlations where parameters are fitted to experimental data.
- Predictive methods where properties are estimated.
The equations and correlations are normally preferred because they describe the property (almost) exactly. To obtain reliable parameters it is necessary to have experimental data which are usually obtained from factual data banks or, if no data are publically available, from measurements.
Using predictive methods is much cheaper than experimental work and also than data from data banks. Despite this big advantage predicted properties are normally only used in early steps of the process development to find first approximate solutions and to exclude wrong pathways because these estimation methods normally introduce higher errors than correlations obtained from real data.
The history of process simulation is strongly related to the development of the computer science and of computer hardware and programming languages. First working simple implementations of partial aspects of chemical processes have been made in the 1970 where, for the first time, suitable hardware and software (here mainly the programming languages FORTRAN and C) have been available. The modelling of chemical properties has been started already much earlier, notably the cubic equation of states and the Antoine equation are developments of the 19th century.
Steady state and dynamic process simulation
For the first years the process simulation only has been used to calculate steady state processes. The simulation retrieved a mass and energy balance of a stationary process but any changes over time had to be ignored.
This static process simulation has later been extended by a dynamic simulation. Dynamic means in this context that the time-depending description. prediction and control of real processes in real time has become possible. This includes the description of starting up and shutting down a plant, changes of conditions during a reaction, holdups, and more.
The dynamic simulation needs much more calculation time and is mathematically more complex that a steady state simulation. It can be seen as a multiply repeated steady state simulation with constantly changing parameters.
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