Optimal measurement combination for local self-optimizing control

Self-optimizing control is a useful method for finding the appropriate set of controlled variables. Recently, some researchers introduced local methods for finding the locally optimal subset or linear combination of measurements, which can be used as controlled variables. [Halvorsen et al. Ind. Eng. Chem. Res. 2003, 42 (14), 3273-3284.] In this paper, we present a method for finding the optimal combination of measurements for local self-optimizing control for nearly optimal steady-state operation. The proposed method only requires the use of singular value decomposition, followed by the determination of the eigenvectors of a matrix. The proposed results are computationally more efficient and are guaranteed to find the optimal solution, in comparison to the available nonlinear optimization-based and the approximate null space methods.