Adaptive Kalman filter for estimation of environmental performance variables in an acid gas removal process

In this paper, adaptive Kalman filter (KF) algorithms are implemented in an acid gas removal (AGR) process for estimating the key environmental performance variables. It was found that by using a KF where the measurement noise covariance matrix (R) is adopted based on the residual sequence, the composition of the top and bottom streams from the H₂S absorber in the AGR process could be estimated accurately even in the presence of large noise-to-signal ratio and poor initial guesses for R. Estimation accuracy of a KF, where the process noise covariance matrix (Q) is adopted, is found to be superior in comparison to the traditional KF, even in the presence of large mismatches between the linear and nonlinear models and a poor initial guess for Q.