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Article overview
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A sensitivity-based approach to optimal sensor selection for process networks | | Siyu Liu
; Xunyuan Yin
; Zhichao Pan
; Jinfeng Liu
; | | Date: |
1 Aug 2022 | | Abstract: | Sensor selection is critical for state estimation, control and monitoring of
nonlinear processes. However, evaluating the performance of each possible
combination of $m$ out of $n$ sensors is impractical unless $m$ and $n$ are
small. In this paper, we propose a sensitivity-based approach to determine the
minimum number of sensors and their optimal locations for state estimation. The
local sensitivity matrix of the measured outputs to initial states is used as a
measure of the observability. The minimum number of sensors is determined in a
way such that the local sensitivity matrix is full column rank. The subset of
sensors that satisfies the full-rank condition and provides the maximum degree
of observability is considered as the optimal sensor placement. Successive
orthogonalization of the sensitivity matrix is conducted in the proposed
approach to significantly reduce the computational complexity in selecting the
sensors. To validate the effectiveness of the proposed method, it is applied to
two processes including a chemical process consisting of four continuous
stirred-tank reactors and a wastewater treatment plant. In both cases, the
proposed approach can obtain the optimal sensor subsets. | | Source: | arXiv, 2208.00584 | | Services: | Forum | Review | PDF | Favorites | |
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