Practical improvements to autocovariance least‐squares

Identifying disturbance covariances from data is a critical step in estimator design and controller performance monitoring. Here, the autocovariance least‐squares (ALS) method for this identification is examined. For large industrial models with poorly observable states, the process noise covariance is high dimensional and the optimization problem is poorly conditioned. Also, weighting the least‐squares problem with the identity matrix does not provide minimum variance estimates. Here, ALS method to resolve these two challenges is modified. Poorly observable states using the singular value decomposition (SVD) of the observability matrix is identified and removed, thus decreasing the computational time. Using a new feasible‐generalized least‐squares estimator that approximates the optimal weighting from data, the variance of the estimates is significantly reduced. The new approach on industrial data sets provided by Praxair is successfully demonstrated. The disturbance model identified by the ALS method produces an estimator that performs optimally over a year‐long period. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1840–1855, 2015