Abstract
Measurement data to achieve the process of design, simulation, optimization and control, and many other engineering and technical work of the foundation and starting point. However, the actual chemical process, the measurement data will inevitably contain a variety of errors,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 errors 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-are of the techniques ni data reconciliation and gross errors detection
2. 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 procedur 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 new model can be used to detect gross errors directly.Simulation results show that the gross errors detection based on the new model is very sensitive to the presence of gross errors.
3. The above improved has been applied to practical data reconciliation for a continuous catalytic reforming unit nian oil refinery.Application results show that the improved data reconciliation model is very effective and can be widely used in industrial processes.
