Novel strategies for predictive particle monitoring and control using advanced image analysis

Rasmus Fjordbak Nielsen, Nasrin Arjomand Kermani, Louise la Cour Freiesleben, Krist V. Gernaey, Seyed Soheil Mansouri*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Processes including particles, like fermentation, flocculation, precipitation, crystallization etc. are some of the most frequently used operations in the bio-based industries. These processes are today typically monitored using sensors that measure on liquid and gas phase properties. The lack of knowledge of the particles itself has made it difficult to monitor and control these processes. Recent advances in continuous in-situ sensors, that can measure a range of particle properties using advanced image analysis, have now however opened up for implementing novel monitoring and modeling strategies, providing more process insights at a relatively low cost. In this work, an automated platform for particle microscopy imaging is proposed. Furthermore, a model based deep learning framework for predictive monitoring of particles in various bioprocesses using images is suggested, and demonstrated on a case study for crystallization of lactose.
Original languageEnglish
Title of host publicationProceedings of the 29th European Symposium on Computer Aided Process Engineering
EditorsKiss Anton, Edwin Zondervan, Richard Lakerveld, Leyla Özkan
PublisherElsevier
Publication date2019
Pages1435-1440
ISBN (Print)9780128186343
DOIs
Publication statusPublished - 2019
Event29th European Symposium on Computer Aided Process Engineering - Eindhoven, Netherlands
Duration: 16 Jun 201919 Jun 2019

Conference

Conference29th European Symposium on Computer Aided Process Engineering
CountryNetherlands
CityEindhoven
Period16/06/201919/06/2019
SeriesComputer Aided Chemical Engineering
Volume46
ISSN1570-7946

Keywords

  • Bioprocess monitoring
  • Advanced image analysis
  • Modeling framework

Cite this

Nielsen, R. F., Arjomand Kermani, N., Freiesleben, L. L. C., Gernaey, K. V., & Mansouri, S. S. (2019). Novel strategies for predictive particle monitoring and control using advanced image analysis. In K. Anton, E. Zondervan, R. Lakerveld, & L. Özkan (Eds.), Proceedings of the 29th European Symposium on Computer Aided Process Engineering (pp. 1435-1440). Elsevier. Computer Aided Chemical Engineering, Vol.. 46 https://doi.org/10.1016/B978-0-12-818634-3.50240-X
Nielsen, Rasmus Fjordbak ; Arjomand Kermani, Nasrin ; Freiesleben, Louise la Cour ; Gernaey, Krist V. ; Mansouri, Seyed Soheil. / Novel strategies for predictive particle monitoring and control using advanced image analysis. Proceedings of the 29th European Symposium on Computer Aided Process Engineering. editor / Kiss Anton ; Edwin Zondervan ; Richard Lakerveld ; Leyla Özkan. Elsevier, 2019. pp. 1435-1440 (Computer Aided Chemical Engineering, Vol. 46).
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abstract = "Processes including particles, like fermentation, flocculation, precipitation, crystallization etc. are some of the most frequently used operations in the bio-based industries. These processes are today typically monitored using sensors that measure on liquid and gas phase properties. The lack of knowledge of the particles itself has made it difficult to monitor and control these processes. Recent advances in continuous in-situ sensors, that can measure a range of particle properties using advanced image analysis, have now however opened up for implementing novel monitoring and modeling strategies, providing more process insights at a relatively low cost. In this work, an automated platform for particle microscopy imaging is proposed. Furthermore, a model based deep learning framework for predictive monitoring of particles in various bioprocesses using images is suggested, and demonstrated on a case study for crystallization of lactose.",
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author = "Nielsen, {Rasmus Fjordbak} and {Arjomand Kermani}, Nasrin and Freiesleben, {Louise la Cour} and Gernaey, {Krist V.} and Mansouri, {Seyed Soheil}",
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Nielsen, RF, Arjomand Kermani, N, Freiesleben, LLC, Gernaey, KV & Mansouri, SS 2019, Novel strategies for predictive particle monitoring and control using advanced image analysis. in K Anton, E Zondervan, R Lakerveld & L Özkan (eds), Proceedings of the 29th European Symposium on Computer Aided Process Engineering. Elsevier, Computer Aided Chemical Engineering, vol. 46, pp. 1435-1440, 29th European Symposium on Computer Aided Process Engineering , Eindhoven, Netherlands, 16/06/2019. https://doi.org/10.1016/B978-0-12-818634-3.50240-X

Novel strategies for predictive particle monitoring and control using advanced image analysis. / Nielsen, Rasmus Fjordbak; Arjomand Kermani, Nasrin; Freiesleben, Louise la Cour; Gernaey, Krist V.; Mansouri, Seyed Soheil.

Proceedings of the 29th European Symposium on Computer Aided Process Engineering. ed. / Kiss Anton; Edwin Zondervan; Richard Lakerveld; Leyla Özkan. Elsevier, 2019. p. 1435-1440 (Computer Aided Chemical Engineering, Vol. 46).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - Processes including particles, like fermentation, flocculation, precipitation, crystallization etc. are some of the most frequently used operations in the bio-based industries. These processes are today typically monitored using sensors that measure on liquid and gas phase properties. The lack of knowledge of the particles itself has made it difficult to monitor and control these processes. Recent advances in continuous in-situ sensors, that can measure a range of particle properties using advanced image analysis, have now however opened up for implementing novel monitoring and modeling strategies, providing more process insights at a relatively low cost. In this work, an automated platform for particle microscopy imaging is proposed. Furthermore, a model based deep learning framework for predictive monitoring of particles in various bioprocesses using images is suggested, and demonstrated on a case study for crystallization of lactose.

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Nielsen RF, Arjomand Kermani N, Freiesleben LLC, Gernaey KV, Mansouri SS. Novel strategies for predictive particle monitoring and control using advanced image analysis. In Anton K, Zondervan E, Lakerveld R, Özkan L, editors, Proceedings of the 29th European Symposium on Computer Aided Process Engineering. Elsevier. 2019. p. 1435-1440. (Computer Aided Chemical Engineering, Vol. 46). https://doi.org/10.1016/B978-0-12-818634-3.50240-X