---
_id: '9697'
abstract:
- lang: eng
text: Continuous processes offer more environmentally friendlier beer production
compared to the batch production. However, the continuous production of mashing
has not become state-of-the-art in the brewing industry. The controllability and
flexibility of this process still has hurdles for practical implementation, but
which are necessary to react to changing raw materials. Once overcome, a continuous
mashing can be efficiently adapted to the raw materials. Both mean residence time
and temperature were investigated as key parameters to influence the extract and
fermentable sugar content of the wort. The continuous mashing process was implemented
as continuous stirred tank reactor (CSTR) cascade consisting of mashing in (20°C),
protein rest (50°C), β-amylase rest (62-64°C), saccharification rest (72°C) and
mashing out (78°C). Two different temperature settings for the β-amylase rest
were investigated with particular emphasis on fermentable sugars. Analysis of
Variance (ANOVA) and a post-hoc analysis showed that the mean residence time and
temperature settings were suitable control parameters for the fermentable sugars.
In the experimental conditions, the most pronounced effect was with the β-amylase
rest. These results broaden the understanding of heterogenous CSTR mashing systems
about assembly and selection of process parameters
author:
- first_name: Patrick
full_name: Wefing, Patrick
id: '68976'
last_name: Wefing
- first_name: Marc
full_name: Trilling, Marc
id: '81622'
last_name: Trilling
orcid: 0000-0002-3685-6383
- first_name: Arthur
full_name: Gossen, Arthur
id: '76446'
last_name: Gossen
- first_name: Peter
full_name: Neubauer, Peter
last_name: Neubauer
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Wefing P, Trilling M, Gossen A, Neubauer P, Schneider J. A continuous mashing
plant controlled by mean residence time. Journal of The Institute of Brewing.
2023;129(1):1-23. doi:10.58430/jib.v129i1.7
apa: Wefing, P., Trilling, M., Gossen, A., Neubauer, P., & Schneider, J. (2023).
A continuous mashing plant controlled by mean residence time. Journal of The
Institute of Brewing, 129(1), 1–23. https://doi.org/10.58430/jib.v129i1.7
bjps: Wefing P et al. (2023) A Continuous Mashing Plant Controlled
by Mean Residence Time. Journal of The Institute of Brewing 129,
1–23.
chicago: 'Wefing, Patrick, Marc Trilling, Arthur Gossen, Peter Neubauer, and Jan
Schneider. “A Continuous Mashing Plant Controlled by Mean Residence Time.” Journal
of The Institute of Brewing 129, no. 1 (2023): 1–23. https://doi.org/10.58430/jib.v129i1.7.'
chicago-de: 'Wefing, Patrick, Marc Trilling, Arthur Gossen, Peter Neubauer und Jan
Schneider. 2023. A continuous mashing plant controlled by mean residence time.
Journal of The Institute of Brewing 129, Nr. 1: 1–23. doi:10.58430/jib.v129i1.7,
.'
din1505-2-1: 'Wefing, Patrick ; Trilling, Marc ; Gossen,
Arthur ; Neubauer, Peter
; Schneider, Jan: A continuous mashing
plant controlled by mean residence time. In: Journal of The Institute of Brewing
Bd. 129, Wiley (2023), Nr. 1, S. 1–23'
havard: P. Wefing, M. Trilling, A. Gossen, P. Neubauer, J. Schneider, A continuous
mashing plant controlled by mean residence time, Journal of The Institute of Brewing.
129 (2023) 1–23.
ieee: 'P. Wefing, M. Trilling, A. Gossen, P. Neubauer, and J. Schneider, “A continuous
mashing plant controlled by mean residence time,” Journal of The Institute
of Brewing, vol. 129, no. 1, pp. 1–23, 2023, doi: 10.58430/jib.v129i1.7.'
mla: Wefing, Patrick, et al. “A Continuous Mashing Plant Controlled by Mean Residence
Time.” Journal of The Institute of Brewing, vol. 129, no. 1, 2023, pp.
1–23, https://doi.org/10.58430/jib.v129i1.7.
short: P. Wefing, M. Trilling, A. Gossen, P. Neubauer, J. Schneider, Journal of
The Institute of Brewing 129 (2023) 1–23.
ufg: 'Wefing, Patrick u. a.: A continuous mashing plant controlled by mean
residence time, in: Journal of The Institute of Brewing 129 (2023), H.
1, S. 1–23.'
van: Wefing P, Trilling M, Gossen A, Neubauer P, Schneider J. A continuous mashing
plant controlled by mean residence time. Journal of The Institute of Brewing.
2023;129(1):1–23.
date_created: 2023-04-12T07:26:12Z
date_updated: 2025-01-30T15:33:02Z
ddc:
- '600'
department:
- _id: DEP4018
- _id: DEP1308
- _id: DEP4028
doi: 10.58430/jib.v129i1.7
has_accepted_license: '1'
intvolume: ' 129'
issue: '1'
keyword:
- ontinuous mashing
- continuous stirred tank reactor
- mean residence time
- fermentable sugar
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
page: 1-23
publication: Journal of The Institute of Brewing
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: A continuous mashing plant controlled by mean residence time
type: scientific_journal_article
user_id: '83781'
volume: 129
year: '2023'
...
---
_id: '6689'
abstract:
- lang: eng
text: "Free amino nitrogen (FAN) concentrations in beer mash can be determined with
machine learning algorithms\r\nfrom near-infrared (NIR) spectra. NIR spectroscopy
is an alternative to a classical chemical analysis and\r\nallows for the application
of inline process quality control. This study investigates the capabilities of\r\ndifferent
machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision
Tree\r\nRegressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR),
K-nearest neighbours (KNN)\r\nregression as well as Support Vector Regression
(SVR) to predict the FAN content in beer mash from NIR\r\nspectra. Various pre-processing
strategies such as principal component analysis (PCA) and data\r\nstandardization
were used to process NIR data that were used to train the machine learning algorithms.\r\nAlgorithm
training was conducted with NIR data obtained from 16 beer mashes with varying
FAN\r\nconcentrations. The trained models were then validated with 4 beer mashes
that were not used for model\r\ntraining. Machine learning algorithms based on
linear regression showed the highest prediction accuracy on\r\nunpre-processed
data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L
(R2 = 0.96)\r\nand a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The
FAN concentration range of the investigated\r\nsamples was between approx. 180
and 220 mg/L. Machine learning based NIR spectra analysis is an alternative\r\nto
classical chemical FAN level determination methods and can also be used as inline
sensor system."
article_type: original
author:
- first_name: Patrick
full_name: Wefing, Patrick
id: '68976'
last_name: Wefing
- first_name: Florian
full_name: Conradi, Florian
id: '68967'
last_name: Conradi
- first_name: Johannes
full_name: Rämisch, Johannes
last_name: Rämisch
- first_name: Peter
full_name: Neubauer, Peter
last_name: Neubauer
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free
amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation
by machine learning algorithms. Brewing science . 2021;74(9/10):107-121.
doi:https://doi.org/10.23763/BrSc21-10wefing
apa: Wefing, P., Conradi, F., Rämisch, J., Neubauer, P., & Schneider, J. (2021).
Determination of free amino nitrogen in beer mash with an inline NIR transflectance
probe and data evaluation by machine learning algorithms. Brewing Science ,
74(9/10), 107–121. https://doi.org/10.23763/BrSc21-10wefing
bjps: Wefing P et al. (2021) Determination of Free Amino Nitrogen
in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine
Learning Algorithms. Brewing science 74, 107–121.
chicago: 'Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer, and
Jan Schneider. “Determination of Free Amino Nitrogen in Beer Mash with an Inline
NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms.”
Brewing Science 74, no. 9/10 (2021): 107–21. https://doi.org/10.23763/BrSc21-10wefing.'
chicago-de: 'Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer
und Jan Schneider. 2021. Determination of free amino nitrogen in beer mash with
an inline NIR transflectance probe and data evaluation by machine learning algorithms.
Brewing science 74, Nr. 9/10: 107–121. doi:https://doi.org/10.23763/BrSc21-10wefing,
.'
din1505-2-1: 'Wefing, Patrick ; Conradi, Florian ; Rämisch,
Johannes ; Neubauer, Peter
; Schneider, Jan: Determination
of free amino nitrogen in beer mash with an inline NIR transflectance probe and
data evaluation by machine learning algorithms. In: Brewing science Bd.
74, Carl (2021), Nr. 9/10, S. 107–121'
havard: P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Determination
of free amino nitrogen in beer mash with an inline NIR transflectance probe and
data evaluation by machine learning algorithms, Brewing Science . 74 (2021) 107–121.
ieee: 'P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, and J. Schneider, “Determination
of free amino nitrogen in beer mash with an inline NIR transflectance probe and
data evaluation by machine learning algorithms,” Brewing science , vol.
74, no. 9/10, pp. 107–121, 2021, doi: https://doi.org/10.23763/BrSc21-10wefing.'
mla: Wefing, Patrick, et al. “Determination of Free Amino Nitrogen in Beer Mash
with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning
Algorithms.” Brewing Science , vol. 74, no. 9/10, 2021, pp. 107–21, https://doi.org/10.23763/BrSc21-10wefing.
short: P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Brewing Science 74
(2021) 107–121.
ufg: 'Wefing, Patrick u. a.: Determination of free amino nitrogen in beer
mash with an inline NIR transflectance probe and data evaluation by machine learning
algorithms, in: Brewing science 74 (2021), H. 9/10, S. 107–121.'
van: Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free
amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation
by machine learning algorithms. Brewing science . 2021;74(9/10):107–21.
date_created: 2021-11-02T10:06:04Z
date_updated: 2025-01-30T15:43:53Z
department:
- _id: DEP1308
- _id: DEP4028
doi: https://doi.org/10.23763/BrSc21-10wefing
intvolume: ' 74'
issue: 9/10
keyword:
- mashing
- NIR
- machine learning
- FAN
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.researchgate.net/publication/355735532_Determination_of_free_amino_nitrogen_in_beer_mash_with_an_inline_NIR_transflectance_probe_and_data_evaluation_by_machine_learning_algorithms
oa: '1'
page: 107 - 121
publication: 'Brewing science '
publication_identifier:
eissn:
- 0723-1520
issn:
- 1866-5195
publication_status: published
publisher: Carl
quality_controlled: '1'
status: public
title: Determination of free amino nitrogen in beer mash with an inline NIR transflectance
probe and data evaluation by machine learning algorithms
type: journal_article
user_id: '83781'
volume: 74
year: '2021'
...
---
_id: '5419'
abstract:
- lang: eng
text: Continuous mashing provides advantages compared to conventional batch-wise
mashing in terms of space time yield. The majority of fermentable sugars are generated
during the so-called “β-amylase rest” (62–64 ◦C). These low molecular sugars are
fermented later in the brewing process by yeasts and therefore determine the beer
attenuation degree. Biological malt variations complicate the application of a
continuous system in industrial scale particularly concerning targeted quality
parameters. The aim is the prediction of sugar formation from process parameters
for a real time control system. Therefore, a semi-empirical model for sugar formation
in a continuous stirred tank reactor (CSTR) system was developed under incorporation
of the residence time distri- bution (RTD). The here presented model, which focuses
on the “β-amylase rest”, is able to predict fermentable sugar concentrations in
the continuous “β-amylase rest” with sufficient accuracy, in contrast to models
that only use the flow rate and the reactor volume to determine the reaction time.
However, the precision and trueness depend on the quality of the empirical data
acquired previously in laboratory experiments for the selected temperature and
raw material quality.
article_number: '107765'
article_type: original
author:
- first_name: Patrick
full_name: Wefing, Patrick
id: '68976'
last_name: Wefing
- first_name: Florian
full_name: Conradi, Florian
id: '68967'
last_name: Conradi
- first_name: Marc
full_name: Trilling-Haasler, Marc
id: '81622'
last_name: Trilling-Haasler
orcid: 0000-0002-3685-6383
- first_name: Peter
full_name: Neubauer, Peter
last_name: Neubauer
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Wefing P, Conradi F, Trilling-Haasler M, Neubauer P, Schneider J. Approach
for modelling the extract formation in continuous conducted “beta-amylase rest”
as part of the production of beer mash with targeted sugar content. Biochemical
Engineering Journal . 2020;164. doi:10.1016/j.bej.2020.107765
apa: Wefing, P., Conradi, F., Trilling-Haasler, M., Neubauer, P., & Schneider,
J. (2020). Approach for modelling the extract formation in continuous conducted
“beta-amylase rest” as part of the production of beer mash with targeted sugar
content. Biochemical Engineering Journal , 164, Article 107765.
https://doi.org/10.1016/j.bej.2020.107765
bjps: Wefing P et al. (2020) Approach for Modelling the Extract Formation
in Continuous Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer
Mash with Targeted Sugar Content. Biochemical Engineering Journal 164.
chicago: Wefing, Patrick, Florian Conradi, Marc Trilling-Haasler, Peter Neubauer,
and Jan Schneider. “Approach for Modelling the Extract Formation in Continuous
Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted
Sugar Content.” Biochemical Engineering Journal 164 (2020). https://doi.org/10.1016/j.bej.2020.107765.
chicago-de: Wefing, Patrick, Florian Conradi, Marc Trilling-Haasler, Peter Neubauer
und Jan Schneider. 2020. Approach for modelling the extract formation in continuous
conducted „beta-amylase rest“ as part of the production of beer mash with targeted
sugar content. Biochemical Engineering Journal 164. doi:10.1016/j.bej.2020.107765,
.
din1505-2-1: 'Wefing, Patrick ; Conradi, Florian ; Trilling-Haasler,
Marc ; Neubauer, Peter ;
Schneider, Jan: Approach for modelling
the extract formation in continuous conducted „beta-amylase rest“ as part of the
production of beer mash with targeted sugar content. In: Biochemical Engineering
Journal Bd. 164 (2020)'
havard: P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, J. Schneider, Approach
for modelling the extract formation in continuous conducted “beta-amylase rest”
as part of the production of beer mash with targeted sugar content, Biochemical
Engineering Journal . 164 (2020).
ieee: 'P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, and J. Schneider,
“Approach for modelling the extract formation in continuous conducted ‘beta-amylase
rest’ as part of the production of beer mash with targeted sugar content,” Biochemical
Engineering Journal , vol. 164, Art. no. 107765, 2020, doi: 10.1016/j.bej.2020.107765.'
mla: Wefing, Patrick, et al. “Approach for Modelling the Extract Formation in Continuous
Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted
Sugar Content.” Biochemical Engineering Journal , vol. 164, 107765, 2020,
https://doi.org/10.1016/j.bej.2020.107765.
short: P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, J. Schneider, Biochemical
Engineering Journal 164 (2020).
ufg: 'Wefing, Patrick u. a.: Approach for modelling the extract formation
in continuous conducted „beta-amylase rest“ as part of the production of beer
mash with targeted sugar content, in: Biochemical Engineering Journal
164 (2020).'
van: Wefing P, Conradi F, Trilling-Haasler M, Neubauer P, Schneider J. Approach
for modelling the extract formation in continuous conducted “beta-amylase rest”
as part of the production of beer mash with targeted sugar content. Biochemical
Engineering Journal . 2020;164.
date_created: 2021-04-08T05:59:08Z
date_updated: 2024-07-03T07:08:55Z
department:
- _id: DEP4023
- _id: DEP1308
- _id: DEP4018
doi: 10.1016/j.bej.2020.107765
intvolume: ' 164'
keyword:
- Continuous mashing
- Residence time distribution
- Beer
- Enzyme bioreactor
- Maltose rest
language:
- iso: eng
publication: 'Biochemical Engineering Journal '
publication_status: published
quality_controlled: '1'
status: public
title: Approach for modelling the extract formation in continuous conducted "beta-amylase
rest" as part of the production of beer mash with targeted sugar content
type: journal_article
user_id: '83780'
volume: 164
year: '2020'
...