Plant Behavior Model Representation

The behavior of the plant process can be represented as states and transition among the different states. Also messages and collaboration among the different classes/objects can represent behavioral aspects of the plant. Equations controlling the transition from one state to another can be represented using object constraint language (OCL) as an associated code with the different classes and states. 

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As discussed and illustrated in the introduction, data analysis can be conveniently viewed in terms of two categories of numeric-numeric manipulation, input and input-output, both of which transform numeric data into more valuable forms of numeric data. Input manipulations map from input data without knowledge of the output variables, generally to transform the input data to a more convenient representation that has unnecessary information removed while retaining the essential information. As presented in Section IV, input-output manipulations relate input variables to numeric output variables for the purpose of predictive modeling and may include an implicit or explicit input transformation step for reducing input dimensionality. When applied to data interpretation, the primary emphasis of input and input-output manipulation is on feature extraction, driving extracted features from the process data toward useful numeric information on plant behaviors. 

A many number of modelling and simulation systems have been developed to aid in process and product engineering. In this paper the knowledge based process plant simulation model was developed. On the model development side, the issues of knowledge representation in the form of systematic component composition, ontology, and interconnections were illustrated. As a case study a plant for starch sweet syrup production was used. The system approach permits the evaluation of feasibility and global plant integration, and a predicted behavior of the reaction systems. The obtained results of the this paper have shown the variety quality of syrups simulation for different products. 

The requirements for expert systems for process control have inspired new designs based on real-time knowledge base inferencing. Object oriented representation of plant equipment, knowledge representation of the interactions of processes and models of process behavior - heuristic as well as analytical --are incorporated into a real-time expert system for process control. The application of inference in real-time requires using metaknowledge to focus the inferencing resources of the expert system. Finally truth maintenance requires a temporal model of the time dependence of the truth of data and inferred results. 

Best-estimate thermal hydraulics and neutronic codes are used to perform the analysis for the LOCA and other DBAs. It is required to identify and assess the associated uncertainties in the analytical models and input data in such a way that the uncertainties in the calculations can be quantified when the results are compared with the acceptance criteria, giving adequate assurance that the acceptance criteria are met. Mature best-estimate codes are widely available aroimd the world, an extensive database exists for nearly all power reactor designs, and best-estimate plant calculations are well documented. The analysis of accidents with best-estimate codes using combinations of best-estimate and conservative inputs is particularly appropriate since this approach provides some estimates of the uncertainties in the overall plant behavior. These estimates can then be compared with the uncertainty estimates developed through relevant activities in different countries in code validation, as well as studies on representation and uncertainties in plant data, to help establish confidence in the predicted behavior of the plant. This approach is dependent on the continued emphasis on activities in development and validation of best-estimate codes to ensure that such codes can be used with a high degree of confidence. 

Figuie 4A shows the dependency p(T) for four different liquids, and Figure 4B depicts the standard representation of this behavior. This confirms that propene, toluene, and CCI4 behave similarly with regard to p(T), whereas water behaves differently. This implies that water cannot be used in model experiments if one of the other three liquids will be employed in the industrial plant. 

When connected in small amounts, the impact of distributed generation on distribution system stability will be negligible. However, if its penetration level becomes higher, distributed generation may start to influence the dynamic behavior of the system as a whole. This chapter presents a mathematical representation of a SOFC plant that is suitable for use in distribution system stability studies. The model is applied to a distributed utility grid that uses a solid oxide fuel cell plant as distributed resource. Examinations include transient stability and voltage stability of the system. 

To be able to use the developed model in design, control, and optimization, it has to be verified against the real system. The behavior of the system is compared with the behavior of a real system (laboratory scale, pilot plant, or industrial units). This important aspect of developing model (design) equations of real value by verifying its representation of real units will be discussed and analyzed in full detail later. 

Objeet-oriented modeling of plant process where plant model is represented in object-oriented manner. This allows the representation of plant operation, behavior, as well as aeeumulating other plant lifecycle activities, such as safety, within central plant object-oriented model. 

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