The autocovariance least-squares method for estimating covariances: application to model-based control of chemical reactors

Abstract

This paper demonstrates the autocovariance least-squares (ALS) technique on two chemical reactor control problems. The method uses closed-loop process data to recover the covariances of the disturbances entering the process, which are required for state estimation. The data used for this purpose may be collected with or without the controllers running. We do not assume that the plant is at steady state nor that only nonzero disturbances are affecting the plant at the time of data collection. The ALS method also accounts for integrated white noise disturbances, which are required for offset-free control. Two examples are provided in this paper: a carefully controlled laboratory reactor and an industrial reactor controlled by a state-of-the-art advanced model predictive control (MPC) system. A variety of control scenarios are tested and the results demonstrate that the ALS method has the potential to improve the best industrial practice of process control by a factor of three to five.