---
_id: '11605'
abstract:
- lang: eng
  text: The recovery of beer from surplus yeast is to date an economical business
    case only for large breweries. In this work, a here novel process with rotating
    ceramic microfiltration membranes is used. This allows a very high lift force
    to be achieved while still maintaining a small transmembrane pressure to reduce
    the formation of a fouling layer. The results show that long running times (between
    cleanings) are possible, limited only by the change in the rheological properties
    of the suspension due to thickening. From a so-called "Inflexion Point" (IF),
    the filtration behavior changes abruptly. The aim of the work was therefore to
    use machine learning aided modeling to predict the IF from experimental data in
    order to optimize the process and to achieve the most economical conditions. The
    economic efficiency depends on the space-time yields. The results show that a
    significant improvement in economic efficiency could be possible with the help
    of modeling and this special kind of filtration technology. However, the economic
    efficiency depends finally on the conditions in each individual brewery.
author:
- first_name: Marc
  full_name: Trilling-Haasler, Marc
  id: '81622'
  last_name: Trilling-Haasler
  orcid: 0000-0002-3685-6383
- first_name: Jörn
  full_name: Tebbe, Jörn
  id: '85958'
  last_name: Tebbe
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Jan
  full_name: Schneider, Jan
  id: '13209'
  last_name: Schneider
  orcid: 0000-0001-6401-8873
citation:
  ama: Trilling-Haasler M, Tebbe J, Lange-Hegermann M, Schneider J. <i>Yeast Filtration
    with Rotating Membrane Filtration –  a New Approach for an Economical Recovery
    of Beer Form Surplus Yeast </i>.; 2024.
  apa: Trilling-Haasler, M., Tebbe, J., Lange-Hegermann, M., &#38; Schneider, J. (2024).
    <i>Yeast filtration with rotating membrane filtration –  a new approach for an
    economical recovery of beer form surplus yeast </i>. 39th EBC Congress 2024, Lille.
  bjps: <b>Trilling-Haasler M <i>et al.</i></b> (2024) <i>Yeast Filtration with Rotating
    Membrane Filtration –  a New Approach for an Economical Recovery of Beer Form
    Surplus Yeast </i>. .
  chicago: Trilling-Haasler, Marc, Jörn Tebbe, Markus Lange-Hegermann, and Jan Schneider.
    <i>Yeast Filtration with Rotating Membrane Filtration –  a New Approach for an
    Economical Recovery of Beer Form Surplus Yeast </i>, 2024.
  chicago-de: Trilling-Haasler, Marc, Jörn Tebbe, Markus Lange-Hegermann und Jan Schneider.
    2024. <i>Yeast filtration with rotating membrane filtration –  a new approach
    for an economical recovery of beer form surplus yeast </i>.
  din1505-2-1: '<span style="font-variant:small-caps;">Trilling-Haasler, Marc</span>
    ; <span style="font-variant:small-caps;">Tebbe, Jörn</span> ; <span style="font-variant:small-caps;">Lange-Hegermann,
    Markus</span> ; <span style="font-variant:small-caps;">Schneider, Jan</span>:
    <i>Yeast filtration with rotating membrane filtration –  a new approach for an
    economical recovery of beer form surplus yeast </i>, 2024'
  havard: M. Trilling-Haasler, J. Tebbe, M. Lange-Hegermann, J. Schneider, Yeast filtration
    with rotating membrane filtration –  a new approach for an economical recovery
    of beer form surplus yeast , 2024.
  ieee: M. Trilling-Haasler, J. Tebbe, M. Lange-Hegermann, and J. Schneider, <i>Yeast
    filtration with rotating membrane filtration –  a new approach for an economical
    recovery of beer form surplus yeast </i>. 2024.
  mla: Trilling-Haasler, Marc, et al. <i>Yeast Filtration with Rotating Membrane Filtration
    –  a New Approach for an Economical Recovery of Beer Form Surplus Yeast </i>.
    2024.
  short: M. Trilling-Haasler, J. Tebbe, M. Lange-Hegermann, J. Schneider, Yeast Filtration
    with Rotating Membrane Filtration –  a New Approach for an Economical Recovery
    of Beer Form Surplus Yeast , 2024.
  ufg: '<b>Trilling-Haasler, Marc u. a.</b>: Yeast filtration with rotating membrane
    filtration –  a new approach for an economical recovery of beer form surplus yeast
    , o. O. 2024.'
  van: Trilling-Haasler M, Tebbe J, Lange-Hegermann M, Schneider J. Yeast filtration
    with rotating membrane filtration –  a new approach for an economical recovery
    of beer form surplus yeast . 2024.
conference:
  end_date: 2024-05-30
  location: Lille
  name: 39th EBC Congress 2024
  start_date: 2024-05-26
date_created: 2024-06-28T12:57:01Z
date_updated: 2025-10-17T18:36:48Z
ddc:
- '600'
department:
- _id: DEP4028
- _id: DEP5023
- _id: DEP4018
- _id: DEP1308
has_accepted_license: '1'
keyword:
- surplus yeast
- membrane filtration
- microfiltration
language:
- iso: eng
publication_status: published
quality_controlled: '1'
status: public
title: 'Yeast filtration with rotating membrane filtration –  a new approach for an
  economical recovery of beer form surplus yeast '
type: conference_poster
user_id: '81304'
year: '2024'
...
---
_id: '12815'
abstract:
- lang: eng
  text: Active learning of physical systems must commonly respect practical safety
    constraints, which restricts the exploration of the design space. Gaussian Processes
    (GPs) and their calibrated uncertainty estimations are widely used for this purpose.
    In many technical applications the design space is explored via continuous trajectories,
    along which the safety needs to be assessed. This is particularly challenging
    for strict safety requirements in GP methods, as it employs computationally expensive
    Monte-Carlo sampling of high quantiles. We address these challenges by providing
    provable safety bounds based on the adaptively sampled median of the supremum
    of the posterior GP. Our method significantly reduces the number of samples required
    for estimating high safety probabilities, resulting in faster evaluation without
    sacrificing accuracy and exploration speed. The effectiveness of our safe active
    learning approach is demonstrated through extensive simulations and validated
    using a real-world engine example.
author:
- first_name: Jörn
  full_name: Tebbe, Jörn
  id: '85958'
  last_name: Tebbe
- first_name: Christoph
  full_name: Zimmer, Christoph
  last_name: Zimmer
- first_name: Ansgar
  full_name: Steland, Ansgar
  last_name: Steland
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Fabian
  full_name: Mies, Fabian
  last_name: Mies
citation:
  ama: Tebbe J, Zimmer C, Steland A, Lange-Hegermann M, Mies F. <i>Efficiently Computable
    Safety Bounds for Gaussian Processes in Active Learning</i>. MLResearchPress ;
    2024:1333-1341.
  apa: Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M., &#38; Mies, F. (2024).
    Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning.
    In <i>International Conference on Artificial Intelligence and Statistics (AISTATS),
    Vol. 238</i> (pp. 1333–1341). MLResearchPress .
  bjps: <b>Tebbe J <i>et al.</i></b> (2024) <i>Efficiently Computable Safety Bounds
    for Gaussian Processes in Active Learning</i>. MLResearchPress .
  chicago: Tebbe, Jörn, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann,
    and Fabian Mies. <i>Efficiently Computable Safety Bounds for Gaussian Processes
    in Active Learning</i>. <i>International Conference on Artificial Intelligence
    and Statistics (AISTATS), Vol. 238</i>. Proceedings of Machine Learning Research.
    MLResearchPress , 2024.
  chicago-de: Tebbe, Jörn, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann
    und Fabian Mies. 2024. <i>Efficiently Computable Safety Bounds for Gaussian Processes
    in Active Learning</i>. <i>International Conference on Artificial Intelligence
    and Statistics (AISTATS), Vol. 238</i>. Proceedings of Machine Learning Research.
    MLResearchPress .
  din1505-2-1: '<span style="font-variant:small-caps;">Tebbe, Jörn</span> ; <span
    style="font-variant:small-caps;">Zimmer, Christoph</span> ; <span style="font-variant:small-caps;">Steland,
    Ansgar</span> ; <span style="font-variant:small-caps;">Lange-Hegermann, Markus</span>
    ; <span style="font-variant:small-caps;">Mies, Fabian</span>: <i>Efficiently Computable
    Safety Bounds for Gaussian Processes in Active Learning</i>, <i>Proceedings of
    Machine Learning Research</i> : MLResearchPress , 2024'
  havard: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently
    Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress
    , 2024.
  ieee: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, and F. Mies, <i>Efficiently
    Computable Safety Bounds for Gaussian Processes in Active Learning</i>. MLResearchPress
    , 2024, pp. 1333–1341.
  mla: Tebbe, Jörn, et al. “Efficiently Computable Safety Bounds for Gaussian Processes
    in Active Learning.” <i>International Conference on Artificial Intelligence and
    Statistics (AISTATS), Vol. 238</i>, MLResearchPress , 2024, pp. 1333–41.
  short: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently
    Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress
    , 2024.
  ufg: '<b>Tebbe, Jörn u. a.</b>: Efficiently Computable Safety Bounds for Gaussian
    Processes in Active Learning, o. O. 2024 (Proceedings of Machine Learning Research).'
  van: Tebbe J, Zimmer C, Steland A, Lange-Hegermann M, Mies F. Efficiently Computable
    Safety Bounds for Gaussian Processes in Active Learning. International Conference
    on Artificial Intelligence and Statistics (AISTATS), Vol. 238. MLResearchPress
    ; 2024. (Proceedings of Machine Learning Research).
conference:
  location: Valencia, SPAIN
  name: 27th International Conference on Artificial Intelligence and Statistics (AISTATS)
  start_date: 2024-05-02
date_created: 2025-04-17T07:58:19Z
date_updated: 2025-06-25T12:47:19Z
department:
- _id: DEP5000
- _id: DEP5023
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://proceedings.mlr.press/v238/tebbe24a.html
oa: '1'
page: 1333-1341
publication: International Conference on Artificial Intelligence and Statistics (AISTATS),
  Vol. 238
publication_identifier:
  issn:
  - 2640-3498
publication_status: published
publisher: 'MLResearchPress '
series_title: Proceedings of Machine Learning Research
status: public
title: Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
type: conference_editor_article
user_id: '83781'
year: '2024'
...