Researches On Modeling Of Data Reconciliation And Gross Errors Detection Technology

Measurement data from industrial processes are the basis of much technical work, and data reliability and accuracy will directly affect the results of research and decision-making. However, the original measurement data often contain various errors, so the basic balanced relations, such as energy balance or conservation of mass, cannot be satisfied. Measurement errors can be mainly divided into two types: random error and gross error. Data rectification is a modern technique to improve the quality of measurement data, and its main purpose is to eliminate the random errors and gross errors included in original data by making use of applied statistics, identification, optimization and other techniques.Base on the existing techniques of data reconciliation and gross error detection, this thesis presented some new problems from real industrial processes and proposed the corresponding schemes. The main contributions include:1. Review the development and the state-of-the-art of the techniques in data reconciliation and gross error detection2. For the existing test based on measurement residual, the main problem is that the data reconciliation procedure tends to spread the gross errors overall the measurements, which results in the detection failure. For the methods based on constraint residual, they can be used to only tell which node is imbalance but cannot identify where the gross error is. In order to avoid these problems, this thesis proposed a new test method. The new method combines an F-statistic with constraint residual statistic to detect gross errors in steady state processes. Simulation results show that the new method is very sensitive to the presence of gross errors and has a great probability of correctly finding one or several gross errors.3. In order to avoid the drawback of the traditional data reconciliation model, an improved model is proposed in this thesis. Some new constraints for the ratio of measurement data are added to the new model, and the constraints of mass balance are transformed into soft constraints by using the method of penalty function. The data reconciliation procedure based on the improved model tends to make the measurements having gross errors get more modification than the others. Therefore, the new data reconciliation model is much more robust than the traditional. Besides, the results of the newmodel can be used to detect gross errors directly. Simulation results show that the gross error detection based on the new model is very sensitive to the presence of gross errors.4. The above improved model has been applied to practical data reconciliation for a continuous catalytic reforming unit in an oil refinery. Application results show that the improved data reconciliation model is very effective and can be widely used in industrial processes.