Optimization Based Partitioning Selection for Improved Contaminant Detection Performance

Conference paper (2019)

Authors

Alexis Kyriacou University of Cyprus
Stelios Timotheou University of Cyprus
V. Reppa KIOS Research and Innovation Center of Excellence and the, University of Cyprus
Francesca Boem University College London
Christos G. Panayiotou University of Cyprus
Marios M. Polycarpou University of Cyprus
Thomas Parisini University College London, University of Trieste
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Published Date

2019

Language

English

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Abstract

Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance.