Investigation On Multi-Resolution Modelling And Data Rectification For Process Industries

Reliable process data are the key to efficient operation of chemical plants. Data reconciliation is a technique used to reduce the impact of measurement noise by integrating information from both measurements and process models. With the development and application of Manufacturing Execution System(MES) in process industry, data reconciliation has attracted a greater deal of attention. After surveyed major issues in data reconciliation, problems on multi-resolution modelling, measurement operator, data reconciliation algorithms are discussed and a data reconciliation system is designed and developed. The main contributions in this dissertation are listed as follows:1) Based on data reconciliation of a whole factory, problems on building the linear mass balance model for process industry is discussed. The idea of measurement network multi-resolution modelling is proposed for different demands of process data granularity. The mathematic formulation of modelling is also defined.2) Based on the framework of measurement network multi-resolution modelling, the measurement operator is divided into three categories(basic measurement operator, manual measurement operator and compound measurement operator) and defined respectively. Then the estimation of measurement operator error variance-covariance is researched in two aspects: mathematics statistics and engineering application. At the presence of outliers, the performance of the estimation is discussed.3) For hybrid systems which incorporate both dynamical and discrete event models in process industries, the models of material balance will be changed because of frequent scheme switch. The redundancy degree of whole sensor network is time variant so that the traditional data reconciliation methods can hardly be applied inpractical process. So a new approach of data reconciliation for hybrid system is proposed. For those nodes where the discrete scheduling events may happen, scheduling-equations are established and added to the models. The new data reconciliation model with parameters of random scheduling-equations can be constructed. In this way, the model's redundancy is improved, which enhances the solvability of the data reconciliation problem. Then its optimal solution can be obtained by applying a reconciliation algorithm with uncertain models. Some simulations of the simplified model from a refinery are given and a comparison is made between the proposed approach and previous researches. The simulations results demonstrate the efficiency and robustness of the proposed method.4) The application of data reconciliation to an industrial plant is discussed. The whole process includes 30 units and 184 tanks. Serious imbalance problems occur due to frequent (daily) changes in some movements. There are many "temporary flows" that one day have a nonzero value, and in another day becomes zero or the flow is redirected to a different tank or unit. Mistakes in the reconciliation topology input can very easily be produced. The new method includes three parts: measurement network reconstruction, gross error detection and reconciliation algorithm. Using the same mass balance model and the same plant data, a comparison is made between the new approach and the commercial data reconciliation software. The application results demonstrate the efficiency and consistency of the proposed approach.5) A data reconciliation software system-ESP-SupPlant DataPro, is designedand developed. It can realize the functions such as plant modelling, process data integration, equipment data reconciliation, plant materiel balance and yield accounting, energy consumption management and reporting, etc. The system has been implemented in Sinopec YangZi Petrochemical Company Ltd. and achieved the requirements of the project.Finally, the paper is concluded with a summary and prospect of future data reconciliation researches.