'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
The selection of controlled variables (CVs) from available measurements through
exhaustive search is computationally forbidding for large-scale processes. We
have recently proposed novel bidirectional branch and bound (B-3) approaches for
CV selection using the minimum singular value (MSV) rule and the local worst-
case loss criterion in the framework of self-optimizing control. However, the
MSV rule is approximate and worst-case scenario may not occur frequently in
practice. Thus, CV selection by minimizing local average loss can be deemed as
most reliable. In this work, the B-3 approach is extended to CV selection based
on local average loss metric. Lower bounds on local average loss and, fast
pruning and branching algorithms are derived for the efficient B-3 algorithm.
Random matrices and binary distillation column case study are used to
demonstrate the computational efficiency of the proposed method
