Operating regime model based multi-objective sensor placement for data reconciliation

G. Dorgo, Mate Haragovics, J. Abonyi

Research output: Chapter


Although the number of sensors in chemical production plants is increasing thanks to the IoT revolution, it is still a crucial problem what to measure and how to place the sensors as such the resulted sensor network be robust and cost-effectively provide the required information. This problem is especially relevant inflexible multi-purpose, multi-product production plants when there are significant differences among the operating regions. The present work aims the development of a sensor placement methodology that utilizes the advantages of local linear models. Realizing the often conflicting nature of the key objectives of sensor placement, the problem is formulated as a multi-objective optimization task taking into consideration the cost, estimation accuracy, observability and fault detection performance of the designed networks and simultaneously seeking for the optimal solutions under multiple operating regimes. The effectiveness of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II)-based solution of the defined problem is demonstrated through benchmark examples.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Number of pages6
Publication statusPublished - 2019

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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    Dorgo, G., Haragovics, M., & Abonyi, J. (2019). Operating regime model based multi-objective sensor placement for data reconciliation. In Computer Aided Chemical Engineering (pp. 1027-1032). (Computer Aided Chemical Engineering; Vol. 46). Elsevier B.V.. https://doi.org/10.1016/B978-0-12-818634-3.50172-7