Optimising and Predicting Performance of Industrial Filtrations using Process Data

Franz David Bähner, Paloma A. Santacoloma, Jens Abildskov, Jakob Kjøbsted Huusom

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

Abstract

Industrial cake filtration is non-trivial from an operational point of view. Discrete events such as the removal of filter cake occur on a frequent but irregular basis. These events tend to upset the steady state of the incorporating line, which may constrain plantwide optimisation. A case study has been carried out with an industrial partner where changes in the biological feedstock act as strong disturbances on a series of manually reinitialised dead-end pressure leaf filters. This renders production planning a challenging task which,so far, is carried out by experienced operators. We look for shortcomings in the current, heuristically grown manner of operating the filters and present guidelines for a superior strategy. A predictive process model is required for a deterministic scheduling algorithm, and two approaches at modelling the filtrations are presented and compared.
Original languageEnglish
Title of host publicationProceedings of the 27th European Symposium on Computer Aided Process Engineering (ESCAPE 27)
EditorsAntonio Espuña, Moisès Graells, Luis Puigjaner
Volume40
PublisherElsevier
Publication date2017
Edition1
Pages1471-1478
ISBN (Print)9780444639653
ISBN (Electronic)9780444639707
DOIs
Publication statusPublished - 2017
Event27th European Symposium on Computer Aided Process Engineering - Barcelona, Spain
Duration: 1 Oct 20175 Oct 2017
Conference number: 27
https://www.elsevier.com/books/27th-european-symposium-on-computer-aided-process-engineering/espuna/978-0-444-63965-3

Conference

Conference27th European Symposium on Computer Aided Process Engineering
Number27
CountrySpain
CityBarcelona
Period01/10/201705/10/2017
Internet address

Keywords

  • Biosystems and Bioprocesses
  • Downstream processing
  • Parameter and state estimation
  • Data mining tools
  • Modeling and identification
  • Pressure leaf filtration

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