Plant Operation Model Representation

The operation of each equipment will be represented as an associated method with the class that represents this equipment. This will enable the operational models to be associated with the static model of the plant. This means that the P ID can be represented along with the operational models of the different equipments, which supports the concept of operational design approach (Naka, 1995). This can be useful for the 

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Flow-sheet models are used at all stages in the life cycle of a process plant during process development, for process design and retrofits, and for plant operations. Input to the model consists of information normally contained in the process flow sheet. Output from the model is a complete representation of the performance of the plant, including the composition, flow, and properties of all intermediate and product streams and the performance of the process units. 

The general framework presented here on the model development side, the issues of knowledge representation in the form of systematie eomposition, ontology, and quantity representaion was involved. On the model analysis side issues involving the automatie evaluation and presentation of simulation results. The plant simulation model should mirror the behaviour of a eomplex plant subjeet to eonstraints in feedstoek, produets, equipment eapaeities, operational parameters, and utilities eonsumptions. The life eyele eoneept may lead to a reliable and maintainable tool. 

As the existing check-lists already provided a detailed and structured representation of the process, they were used to create a first version of the WOMS model. Subsequently, discussions with the operating personnel and the plant engineer addressed the shortcomings of the process as specified in the old check lists and the variants actually followed during plant operation. 

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. 

Focus For the purposes of this discussion, a model is a mathematical representation of the unit. The purpose of the model is to tie operating specifications and unit input to the products. A model can be used for troubleshooting, fault detection, control, and design. Development and refinement of the unit model is one of the principal results of analysis of plant performance. There are two broad model classifications. 

The overall system that we will analyze comprises the unbleached Kraft pulp line, chemicals and energy recovery zones of a specific paper mill (Melville and Williams, 1977). We will employ a somewhat simplified but still realistic representation of the plant, originally developed in a series of research projects at Purdue University (Adler and Goodson, 1972 Foster et al., 1973 Melville and Williams, 1977). The records of simulated operation data, used to support the application of our learning architecture, were generated by a reimplementation, with only minor changes, of steady-state models (for each individual module and the system as a... 

Undoubtedly, this new kind of integrated approach is well representative of what should be membrane engineering, with final objectives clearly defined, the right hypothesis and choice of simple equations for modeling, a realistic representation of real complex solutions and the set-up of efficient simulation tools involving successive intra- and extrapolation steps. It appears to be easily extended to other membrane operations, in other fields of applications. It should provide stakeholders with information needed to make their decision costs, safety, product quality, environment impact, and so on of new process. Coupled with the need to check the robustness of the new plant and the quality of final output, it should constitute the right way to develop the use of membranes as essential instruments for process intensification with industrial units at work. 

Implementation of the improved start-up process. As the plant will further on be operated manually, the implementation of the improved start-up process had to ensure that the operating personnel will follow the procedures defined there. To this end, the relevant information must be passed to the staff. As the representation of the start-up process by means of check lists is widely established, it has been decided to transform the final WOMS model into a set of check lists. 

The selection of diagnostic engine symptom inputs was guided by several goals (1) provide arealistic representation of the plant information readily available to the control room operators, (2) ensure the number of symptoms was consistent with the limitations of the operator s short term memory and (3) provide adequate discrimination among initiating events. Ten symptom groups are used for the current PWR plant model ... 

An event tree is a graphical representation of event sequences with probabilities, following an initiating event. An event tree provides the systematic model of the time sequence of event propagation through a series of barriers, normal plant actions, and/ or operator intervention and incidental consequences. Now, short discussions have been put forward to see how an event tree is constructed with the help of a number of steps there are basically seven steps Involved, and these are defined in Table V/2.2-1. 

The RTN representation considers two types of nodes Tasks and Resources. The tasks, are operations that consume/produce a specific set of resources while resources model the different types of resources needed in the plant. 

Using an example proposed by Barbosa-P6voa and Macchietto (1994), the above representations are again explored. In here, a plant must be designed at a maximum profit so as to produce three final products, S4, S5 and S6, with production capacities between [0 80] ton for S4 and S5, and [0 60] for S6, fi om two raw materials, SI and S2. The process operates in a non-periodic mode over a time horizon of 12 hours. The results in terms of model statistics are shown in Table 3. 

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