Systematic observability and detectablity analysis of industrial batch crystallizers

Research output: Contribution to journalConference articleAcademicpeer-review

4 Citations (Scopus)

Abstract

Motivated by the lack of hardware analysers for particle size distribution (PSD) and solute concentration measurements in industrial crystallizers, this work investigates the feasibility of designing alternative monitoring tools based on state observers. The observability and detectability properties of the discretized population balance equation accounting for crystal growth, attrition and agglomeration coupled with energy and solute mass balances are studied. A systematic methodology for sensor selection based on nonlinear observability and detectability principles is proposed and applied. Results are corroborated by a machine learning technique (the self-organizing map), leading to the fact that the solute concentration is distinguishable with temperature measurements, while the PSD is not. The results represent the starting point for future detector design where temperature measurements are used to infer composition, while the estimation of the PSD is done in "open loop" fashion.

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Crystallizers
Observability
Particle size analysis
Temperature measurement
Self organizing maps
Crystal growth
Learning systems
Agglomeration
Detectors
Hardware
Monitoring
Sensors
Chemical analysis

Cite this

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title = "Systematic observability and detectablity analysis of industrial batch crystallizers",
abstract = "Motivated by the lack of hardware analysers for particle size distribution (PSD) and solute concentration measurements in industrial crystallizers, this work investigates the feasibility of designing alternative monitoring tools based on state observers. The observability and detectability properties of the discretized population balance equation accounting for crystal growth, attrition and agglomeration coupled with energy and solute mass balances are studied. A systematic methodology for sensor selection based on nonlinear observability and detectability principles is proposed and applied. Results are corroborated by a machine learning technique (the self-organizing map), leading to the fact that the solute concentration is distinguishable with temperature measurements, while the PSD is not. The results represent the starting point for future detector design where temperature measurements are used to infer composition, while the estimation of the PSD is done in {"}open loop{"} fashion.",
author = "M. Porru and L. Ozkan",
year = "2016",
month = "6",
doi = "10.1016/j.ifacol.2016.07.391",
language = "English",
volume = "49",
pages = "496--501",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "Elsevier",
number = "7",

}

Systematic observability and detectablity analysis of industrial batch crystallizers. / Porru, M.; Ozkan, L.

In: IFAC-PapersOnLine, Vol. 49, No. 7, 06.2016, p. 496-501.

Research output: Contribution to journalConference articleAcademicpeer-review

TY - JOUR

T1 - Systematic observability and detectablity analysis of industrial batch crystallizers

AU - Porru,M.

AU - Ozkan,L.

PY - 2016/6

Y1 - 2016/6

N2 - Motivated by the lack of hardware analysers for particle size distribution (PSD) and solute concentration measurements in industrial crystallizers, this work investigates the feasibility of designing alternative monitoring tools based on state observers. The observability and detectability properties of the discretized population balance equation accounting for crystal growth, attrition and agglomeration coupled with energy and solute mass balances are studied. A systematic methodology for sensor selection based on nonlinear observability and detectability principles is proposed and applied. Results are corroborated by a machine learning technique (the self-organizing map), leading to the fact that the solute concentration is distinguishable with temperature measurements, while the PSD is not. The results represent the starting point for future detector design where temperature measurements are used to infer composition, while the estimation of the PSD is done in "open loop" fashion.

AB - Motivated by the lack of hardware analysers for particle size distribution (PSD) and solute concentration measurements in industrial crystallizers, this work investigates the feasibility of designing alternative monitoring tools based on state observers. The observability and detectability properties of the discretized population balance equation accounting for crystal growth, attrition and agglomeration coupled with energy and solute mass balances are studied. A systematic methodology for sensor selection based on nonlinear observability and detectability principles is proposed and applied. Results are corroborated by a machine learning technique (the self-organizing map), leading to the fact that the solute concentration is distinguishable with temperature measurements, while the PSD is not. The results represent the starting point for future detector design where temperature measurements are used to infer composition, while the estimation of the PSD is done in "open loop" fashion.

U2 - 10.1016/j.ifacol.2016.07.391

DO - 10.1016/j.ifacol.2016.07.391

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EP - 501

JO - IFAC-PapersOnLine

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JF - IFAC-PapersOnLine

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