Physical data flow diagram

During the framework design phase (Fig. 30.4), first principles, process type conceptions and process expertise are combined to form a physical framework. In addition, the parameters that will be modeled by sub-models have to be identified. The output of each sub-model is the parameter concerned. However, independent variables are not always clear. It may be necessary that those input variables are determined by means of a sensitivity analysis. The conceivable candidates for these independent variables are all state and control variables used in the framework. In this example fuzzy logic will be used to build these sub-models, but other black-box modehng techniques, which are discussed in the previous chapters, such as, for example neural networks, may also be appropriate. The result of the framework design phase is the framework, which can be represented by a behavioral model. This is a data flow diagram of the model stmcture. 

Process data sheets supplement the process flow diagram. These sheets give physical data required of the process equipment, such as vapor-liquid proportions of tower trays, physical data, safety factors for pumps, detailed furnace flow conditions, etc. 

DFDs are drawn with just four symbols data sources and sinks, processes, data flows and data stores (see Figure 5). Data sources are entities that provide data to the system (a source) or receive information from the system (a sink). These sources lie outside the boundary of the system and are not active participants in the processing that occurs within the system. A process is any activity that converts data into information. Generally, processes are named with a verb of the action in the process and are numbered at the top of the symbol. Numbers are useful for cross-referencing processes on lower-level diagrams to the activities in higher-level diagrams. Processes are interconnected with other processes by data flows. Data flows are symbols that represent data in motion. Each data flow is named with a noun that describes the data represented in the flow. A data flow must be attached to a process at some point, either as a source or as a sink. A data store is a representation of the data at rest. Examples of data stores could include electronic data files, a database table, or a physical file folder. 

The outermost structure for our KNODE is illustrated in Figure 1, which shows the major functional components and flow of data. Scientific data, in the form of numbers specifications of chemistry, physics, or geometry algorithms code or remote host output, generally flows clockwise around the diagram as indicated by solid arrows. Control flow is generally outward from the System Supervisor module. Data in the form of knowledge may flow in various directions as indicated by shaded arrows. 

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