Pretoria-based engineering company, Iritron, a specialist in simulation, optimisation, automation and information systems for industrial plants, discusses the contribution simulations can offer.
Establishing the financial gains associated with a process simulator is best achieved by quantifying the losses associated with not having one: loss of design quality, loss of operator competence and loss of production time. The long-term advantages of process simulation then become evident as investments that can save money.
One needs to consider where losses occur, what is available in terms of process simulation, whether it can be used to help prevent these losses, and what the simulation will cost.
First, however, a better understanding of the nature of process simulation will be helpful in making an informed choice. Briefly put, simulation is the process of imitating real phenomena with sets of mathematical formulas and algorithms. This combination of formulas and algorithms represents simplified models of the phenomena.
Process simulation is not all about theoretical modelling however, although this forms an important part. The models have to be solved to get answers, and they have to be solved fast enough for the answers to be useful. In almost all of the cases this implies the use of a powerful computer: the simulation hardware platform. It also implies the necessity of a simulation software platform with tools to build, maintain, extend and solve the models on the computer.
While a process simulation can be worse than the process data available to assist in its modelling, it certainly cannot be better. Process data quality therefore directly determines the level of simulator quality possible. Examples of typical data items are diagrams of the process flow and instrumentation, energy and mass balances, and equipment data such as pump curves, valve characteristics, pipe lengths and tank volumes.
Last but not least, it must be realised that the simulator will probably not stand on its own. A process is usually controlled, either manually or automatically. Since the simulator represents the process, it stands to reason that it will also be controlled by similar means. Control and interface issues must be considered. These issues can range from something as simple as providing a user interface in the simulation environment itself, to communication with an external PLC and/or scada, or maybe an emulation of a complete DCS.
The three most important cost-determining factors as far as process simulation is concerned are:
* The process to be simulated.
* Process control and interfacing.
* The degree of simulation accuracy required and the domain over which the simulation must be valid – ie the simulator fidelity.
These three factors determine the expertise needed, the amount of development and testing time necessary, as well as the choice of the hardware and software platforms. Once the process to be simulated is identified, and the control and interfacing issues have been sorted out, one must carefully consider the degree of fidelity required in order to avoid unnecessary costs. The fidelity determines to a large degree the cost of simulator development, and in turn depends for the most part on what the simulation will be used for.
Process simulators can be classified based on their intended use. In order of increasing fidelity and computing power required – and therefore cost – these categories are:
* Factory acceptance test (FAT) simulators.
* Training simulators.
* Engineering simulators.
There are, of course, varying degrees of complexity possible within each category and the divisions between the categories themselves are not crisply defined. In fact, it is possible to upgrade a FAT simulator to a training simulator, and a training simulator to an engineering simulator. It should be said however, that this intention should be known from the start, and that simulator development must be handled accordingly.
FAT simulators are so-called because they assist in the factory acceptance tests of process control systems and the associated graphical user interfaces. The control system is connected via communication interfaces to the simulation platform. The simulator's job is to keep the control system under the impression that it is, in fact, connected to the plant. It accepts all the control system outputs that would normally go to the plant, and supplies the control system with the inputs it expects.
While FAT simulator fidelity is fairly low, the added value is high. A large part of the control system and its user interface can be tested before connecting it to the plant. This drastically reduces both plant commissioning time and the risk of equipment damage. One can validate control system and user interface function blocks, ensure compliance of the control system with its functional specification, and verify that control sequences are in working order under both normal and abnormal process conditions.
A training simulator can be a valuable tool during operator training. It enables the operators to learn the full functionality of the control system user interface without the pressure of being connected to the real plant. Hands-on training in both normal and emergency operating procedures becomes possible without using the plant itself. By creating malfunctions and abnormal conditions, the instructor can teach the operators the ability to find and correct faults on the plant in a timely and efficient manner.
Training simulator fidelity must be relatively high, certainly when compared to FAT simulators. The response of the simulation to inputs – generated both by the operator and the control system – must be close enough to that of the real process to prevent negative training taking place.
Engineering simulators are used in plant design, process optimisation and control philosophy development. In some cases the plant does not exist and the engineering simulator is the only tool available to predict its behaviour. A very high degree of fidelity is therefore required in order to justify the trust that will be placed in the simulation results. The valid domain of possible values for the simulator inputs, state variables and outputs must be rigorously defined, and the deviation from correct behaviour within this domain must be minimal and well known. In the case of existing plants the simulator can be used to design, test and justify changes to the process or the control thereof.
Are they worth it? Like any equipment – if they are to be made good, effective use of – process simulators are worthwhile investments.
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