Research On Model And Data Management In Process Industry

In the last decades, advantages of using models and data to support decision making have became more and more recognizable for process industries due to the improvement of computer technology and the spread of computer integrated manufacturing system. Previous model and data management researches have encountered problems such as model base structure, integration of model and data management system and model representation. After a survey of researches on model management, data rectification, data management and related applications under the framework of enterprise control system integration, we eliminate these problems by showing how to build model bases for process industry and how to design a model and data management system. The main content and major contributions in this dissertation are listed as follows:(1) Previous model base structure and model management approach have encountered problems when they are applied in process industry. After analysis of the current model base structure and model management approach, a new idea of building descriptive model base and analysis model base is proposed. In order to facilitate the modeling process, interfaces between data, models and tools are designed and developed along with the prototype of visualized model management system.(2) Data management system in the process industries is composed of real-time database and relational database. Guided by the standard of enterprise control system integration, conceptual, logical and physical data model for hierarchical process data storage and application is developed with the consideration of future extension. Interfaces between real-time data and relational data are implemented and became the foundation of effective data exchange.(3) Redundant data in the data management system are used in the data rectification. The improved MILP method uses both temporal and spatial redundancies to generate gross error candidates and calculate prior probabilities. By reducing the binary variables in the MILP framework, efficiency and accuracy of the original MILP algorithm is remarkably improved.(4) An effective scheduling simulation strategy for refinery is proposed and implemented on the model and data management system. With the help of model mapping, dynamic-steady simulation and expert rules, this strategy is able to validate optimization methods and generate simulation data in the hierarchical levels. And the using of model and data management system makes the modeling and analyzing process more convenience and adjustable.At the end of this dissertation, promising future researches on model and data management are introduced based on the conclusion of this dissertation.