Robust EKF based on the framework of dynamic data reconciliation for state estimation of chemical processes with Gaussian/non-Gaussian measurement noise

State estimation plays a critical role in modern industry. The extended Kalman filter (EKF) is effective for nonlinear chemical processes with Gaussian noise, but it struggles when gross errors are present. This paper addresses this limitation by reformulating the EKF within the dynamic data reconciliation (DDR) framework, resulting in a robust DDR-based EKF tailored for state estimation in chemical processes with non-Gaussian measurement noise. The combination of random and gross errors is modeled using a contaminated Gaussian distribution. Model predictions are incorporated as prior knowledge, and a fixed-point iterative strategy is employed to update the posterior probability. Additionally, a first-order linearization technique is applied for convergence analysis. The robustness and effectiveness of the DDR-based EKF are demonstrated through both a classic mathematical example and a styrene polymerization reaction. Simulation results show that the DDR-based EKF effectively mitigates gross errors, achieving reliable state estimation.