Research On Multi-Layer Data Rectification Algorithm For Process Industry And Its Application

Measurements are inevitablely influenced by errors. Making full use of redundancy information, data rectification technique eliminates the gross errors in the raw data, reduces the random measurement errors and estimates the unmeasured variables. 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 more attention. After a survey of major issues in data reconciliation, problems on multi-layer modeling, data reconciliation algorithms and industrial application, sensor network design strategy are discussed and developed. The main contributions in this dissertation are listed as follows:1) There are many difficulties in data reconciliation industry applications which is strongly dependent on the models, and a method of measurement network multi-layer modeling is proposed for mass balance of a whole factory. The constraints of mass balance model among different layers are discussed and the mathematic formulation is also modeled.2) According to the multi-layer modeling framework of a whole factory, the data rectification can be defined in different layers. The precise mass balance model is the basis of data rectification. However, the process network changes dynamically due to frequently occurring scheduling events. A new method was proposed to update the mass balance model in view of application. Bayesian network was used, its structure was selected according to expert's experiences and its variables were trained by historical data. Consequently, scheduling events were identified based on diagnostic function of Bayesian network, and an updated simplified model was finally established. In this way, the feasibility of data rectification was enhanced.3) The error of measurements can lead to significant deterioration in the quality of petrochemical process monitoring. According to the multi-layer modeling framework, a whole factory two-layer data rectification strategy for mass balance is presented. The approach utilizes the high precision measurements' data rectification result in one layer as constraints to correct other layer's measured variables and improve the whole system's redundancy, and this approach can eliminate the gross error more effectively.4) Given the characteristic of measurement data in Sinopec JiaJiang Refinery, the whole factory two-layer data rectification approach is used. The practical data rectification utilization is introduced and the result of data rectification is analyzed. It is proven by the results of measurement data in the Refinery that with the proposed method the gross error measurements are able to effectively be processed and an estimation of unmeasured variables is provided.5) The flow-rate data of unit-layer are the basis of other layers modeling according to the multi-layer modeling framework of a whole factory. In order to obtain the highly reliable and precise data in unit-layer and other layers, sensor network design and retrofit is introduced in unit-layer. A procedure for the design of a sensor network is reported, the strategy uses the graph theory and takes the reliability objective and cost constraint into account. According to the requirements of industrial application, the approach presented in this paper can obtain an efficient solution even though there are not enough sensors to guarantee the whole system's observability. The proposed algorithm can also provide a solution in such situation where some important variables have to be measured and a portion of them cannot be directly measured technically.In the end, data reconciliation researches are summerized and some problems are presented so as to demonstrate the orientation to deeply study in the next stage.