Estimating uncertainties and parameters for fundamental models used in online monitoring and control

Abstract

AbstractMany model‐based online process monitoring and control applications rely on state estimation techniques that use noisy process data to update states, thereby ensuring that imperfect model predictions are consistent with process behaviour. Techniques for tuning state estimators are reviewed, and their effectiveness and limitations are summarized in this article. A new simultaneous parameter and estimator tuning (SPET) methodology is proposed, one in which parameter estimation techniques for stochastic differential equations (SDEs) are used to simultaneously estimate measurement‐error covariances and model‐error covariances along with the model parameters. The resulting information is then used to compute state‐estimator tuning information. This study shows how SPET can be used, along with old dynamic process data, to obtain reliable tuning information. The proposed methodology is tested using a nonlinear two‐state continuous stirred‐tank reactor (CSTR) model with simulated data. Comparisons are made with a more‐conventional approach that uses WLS to estimate fixed model parameters and autocovariance least squares (ALS) to estimate extended Kalman filter (EKF) tuning factors. The main benefit of the proposed approach is that fixed model parameters and tuning factors for the EKF are estimated simultaneously, resulting in significant improvements to state estimates and online model predictions.