**2.3 Simulation and the plant lifecycle**

The use of simulation throughout the plant life cycle, from the design until training and operation stages, has the potential of providing significant benefits. The cumulative effect of cost savings and improved operating rate can be substantial and typically return the initial simulation investment within the first year of operation. It will also continue to contribute to profitability based on better operation practices. Ahmad et al. (2010) present a case study where the use of an operator training simulator and the incorporation of advanced process control reduce the cost of a project in 49 Millions USD. The application of dynamic simulation throughout the plant lifecycle can provide the following benefits:


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documents describe the objectives of the training session, the instructor actions (initial condition, malfunctions, etc) and the required actions to operate the unit (to turn on pumps, to open valves, etc), all this information helps to build a very complete knowledge base. These documents include normal operation (start-up, normalization and shutdown operations, for each one of the power plant systems) and in many cases abnormal operations. Additional tests can be performed in the simulator with the aim of getting supplementary information, mainly

The acquired knowledge must be formalized and ordered with the aim of being useful to the ES; this process is named "Knowledge representation". One of the most common methods to represent knowledge is the production rules. In this method, the knowledge is divided into small fractions of knowledge or rules. A rule is a conditional structure that logically relates the information contained in the part of antecedent with other information contained in the part of the consequent. A very important feature is that the knowledge base is independent of the inference mechanism used to solve problems. Thus, when the stored knowledge become obsolete, or when new knowledge is available, it is relatively easy to add new rules, delete old ones or correct existing errors. Therefore, there is no need of reprogramming all the expert system. The rules are stored in hierarchical sequence logic, but this is not strictly necessary. It may be in any sequence and the inference engine will use them in the right order to solve a problem. This approach is also called IF-THEN rules and some of its main benefits are their modularity and that each rule defines a relatively small and independent piece of knowledge. However, the process of coding the rules can be a cumbersome chore for personnel little familiar with this kind of responsibilities. Tavira-Mondragón et al. (2010b) describes a graphic tool which serves to build training exercises for a combined cycle power plant simulator, with no guidance of a human instructor. This editor contains a group of blocks where each block represents a rule (or a group of rules), and each block is customized by their characteristic parameters. Figure 10 shows in a schematic way, the graphic representation of the malfunction insertion during a training

in the cases of malfunctions, where there are not enough documented records.

Fig. 10. Knowledge representation in CLIPS.

Besides the referred documents of the ISA, IAEA and EPRI, additional information about simulators and training programmes can be founded in EPRI (1998) and IAEA (1996, 2003). The documents published by IAEA deal about the training of operators of nuclear power plants but many of the key concepts are applicable to the fossil industry.
