New Method for Designing of Data Reconciliation Block in RTO System

In chemical processes, there are hundreds or thousands of variables. Measurement of such variables is always accompanied with errors. In this article, a new method is presented for dynamic and linear reconciliation of process data for real time optimization (RTO) of the process. In this method, a linear parameter model with constant structure is considered and then using input and output noisy data of process, system model parameters are estimated by a recursive estimator. To estimate model parameters online, recursive least square identification method is used. By using these parameters, errorless process data are generated through Kalman filter method. This method is implemented using Simulink tool box and Matlab software package. In this study, a rigorous model for the demercaptanization distillate (DMD) process is developed using the commercial software, HYSYS. Data generated by a DMD Process simulation is used for evaluation of adaptive data reconciliation. First data is artificially contaminated to errors (white noise) in Simulink environment and then filtered by the proposed method. Comparison of the outlet data for the actual and filtered process data shows great improvement and the results obtained through the new technique are satisfactory by current standards.

کلیدواژه ها:

Data Reconciliation ، adaptive ، Kalman Filter ، Simulink ، RTO