A Wavelet Enhanced Integral Approach to Linear Dynamic Data Reconciliation

An integral approach for dynamic data reconciliation that combines a direct numerical integration via Simpson’s rule and data smoothing via discrete wavelet decomposition is presented. By simple numerical integration, the differential-algebraic equations governing the material balances are transformed into algebraic constraints to formulate the reconciliation problem. The frequency responses and the frequency contents of the measured variables are considered to determine the cut-off frequencies for data smoothing. Repetitious solutions for reconciliation using a moving data window are then used to generate the dynamic reconciled data for gross error detection. Compare with the other methods such as the Kalman filter and another sophisticated integration approach, this proposed method is simpler and has better results.