On-line estimation for process control and optimization applications

Design of Kalman filter type and moving horizon estimators for on-line estimation applications based on first principles models is reviewed. Important design issues are discussed, such as: model development; choice of process noise model and selection of model parameters for on-line estimation; use of asynchronous and delayed measurements; and off-line estimation of fixed but uncertain model parameters. The main conclusion, which is substantiated through application examples, is that robust and reliable estimation applications based on first principles models of considerable complexity, can be designed and implemented for use in an industrial environment.