@misc{10245, author = {{Wefing, Patrick and Schneider, Jan}}, booktitle = {{Brauwelt international : journal for the brewing and beverage industry }}, issn = {{0934-9340}}, pages = {{224--227}}, publisher = {{Hans Carl Verlag}}, title = {{{FAN measurement during mashing}}}, volume = {{IV}}, year = {{2023}}, } @misc{9697, abstract = {{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 = {{Wefing, Patrick and Trilling, Marc and Gossen, Arthur and Neubauer, Peter and Schneider, Jan}}, booktitle = {{Journal of The Institute of Brewing}}, keywords = {{ontinuous mashing, continuous stirred tank reactor, mean residence time, fermentable sugar}}, number = {{1}}, pages = {{1--23}}, publisher = {{Wiley}}, title = {{{A continuous mashing plant controlled by mean residence time}}}, doi = {{10.58430/jib.v129i1.7}}, volume = {{129}}, year = {{2023}}, } @misc{7443, author = {{Wefing, Patrick and Schneider, Jan}}, booktitle = {{Brauwelt}}, issn = {{1866-5195 }}, number = {{22}}, pages = {{304--308}}, publisher = {{Fachverlag Hans Carl GmbH}}, title = {{{FAN-Messung während des Maischens}}}, volume = {{12-13}}, year = {{2022}}, } @misc{7444, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, booktitle = {{38th Congress of the European Brewery Convention (EBC 2022) : held 30 May - 1 June 2022, Madrid, Spain}}, isbn = {{978-1-7138-7038-8}}, location = {{Madrid}}, publisher = {{Curran Associates, Inc.}}, title = {{{Machine learning aided free amino nitrogen determination in beer mash with an inline NIR transflectance }}}, year = {{2022}}, } @misc{8404, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, location = {{Berlin }}, title = {{{Machine learning aided free amino nitrogen determination in beer mash with an inline NIR transflectance }}}, year = {{2022}}, } @misc{8424, author = {{Katsch, Linda and Conradi, Florian and Wefing, Patrick and Schneider, Jan}}, booktitle = {{38th Congress of the European Brewery Convention (EBC 2022) : held 30 May - 1 June 2022, Madrid, Spain }}, isbn = {{978-1-7138-7038-8 }}, location = {{Madrid}}, publisher = {{Curran Associates, Inc.}}, title = {{{Determination and prediction of the final attenuation and quality parameters in beer with near-infrared spectroscopy}}}, year = {{2022}}, } @article{6689, abstract = {{Free amino nitrogen (FAN) concentrations in beer mash can be determined with machine learning algorithms from near-infrared (NIR) spectra. NIR spectroscopy is an alternative to a classical chemical analysis and allows for the application of inline process quality control. This study investigates the capabilities of different machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision Tree Regressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR), K-nearest neighbours (KNN) regression as well as Support Vector Regression (SVR) to predict the FAN content in beer mash from NIR spectra. Various pre-processing strategies such as principal component analysis (PCA) and data standardization were used to process NIR data that were used to train the machine learning algorithms. Algorithm training was conducted with NIR data obtained from 16 beer mashes with varying FAN concentrations. The trained models were then validated with 4 beer mashes that were not used for model training. Machine learning algorithms based on linear regression showed the highest prediction accuracy on unpre-processed data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L (R2 = 0.96) and a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The FAN concentration range of the investigated samples was between approx. 180 and 220 mg/L. Machine learning based NIR spectra analysis is an alternative to classical chemical FAN level determination methods and can also be used as inline sensor system.}}, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, issn = {{0723-1520}}, journal = {{Brewing science }}, keywords = {{mashing, NIR, machine learning, FAN}}, number = {{9/10}}, pages = {{107 -- 121}}, publisher = {{Carl}}, title = {{{Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms}}}, doi = {{https://doi.org/10.23763/BrSc21-10wefing}}, volume = {{74}}, year = {{2021}}, } @misc{8405, author = {{Wefing, Patrick and Conradi, Florian and Beckhoff-Bumbke, Steffen}}, location = {{Bielefeld}}, title = {{{Closed Loop Systems Engineering}}}, year = {{2020}}, } @article{5419, abstract = {{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.}}, author = {{Wefing, Patrick and Conradi, Florian and Trilling-Haasler, Marc and Neubauer, Peter and Schneider, Jan}}, journal = {{Biochemical Engineering Journal }}, keywords = {{Continuous mashing, Residence time distribution, Beer, Enzyme bioreactor, Maltose rest}}, 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}}}, doi = {{10.1016/j.bej.2020.107765}}, volume = {{164}}, year = {{2020}}, } @inproceedings{5427, author = {{Wefing, Patrick and Conradi, C. and Beckhoff, St.-Bumke}}, title = {{{Auf dem Weg zur Smart FOODFACTORY, Prototyp einer kontinuierlichen Maisch Anlage}}}, year = {{2020}}, } @article{5429, author = {{Wefing, Patrick and Conradi, Florian and Trilling-Haasler, Marc and Schuster, Rudolf and Gossen, Arthur and Schneider, Jan}}, journal = {{Brauwelt}}, number = {{15 - 16}}, pages = {{413 -- 416}}, publisher = {{Carl Hanser Verlag}}, title = {{{Maischen 4.0 – kontinuierliche Maischanlage}}}, year = {{2020}}, } @inproceedings{5443, author = {{Zhang, Fan and Pinkal, K. and Conradi, Florian and Wefing, Patrick and Schneider, Jan and Niggemann, Oliver}}, location = {{Melbourne, Australia}}, title = {{{Quality Control of Continuous Wort Production through Production Data Analysis in Latent Space}}}, year = {{2019}}, } @inproceedings{5444, author = {{Conradi, Florian and Wefing, Patrick and Schneider, Jan}}, location = {{Antwerpen}}, title = {{{Near infrared spectroscopy and mashing – a promising approach for real time inline quality control?}}}, year = {{2019}}, } @inproceedings{5445, author = {{Wefing, Patrick and Conradi, Florian and Schneider, Jan}}, location = {{Antwerpen}}, title = {{{Laboratory plant for a Continuous Closed Loop controlled Mashing aided by digital technologies}}}, year = {{2019}}, } @inproceedings{5457, author = {{Schneider, Jan and Wefing, Patrick and Conradi, Florian}}, location = {{Rust}}, title = {{{ Industry 4.0 and Continuous Mashing. - Design of a „closed loop controlled mashing“ pilot plant }}}, year = {{2019}}, } @inproceedings{5459, author = {{Wefing, Patrick and Conradi, Florian and Schneider, Jan}}, location = {{Braunschweig}}, title = {{{Industrie 4.0 in der Lebensmittelindustrie – Entwicklung einer „Closed Loop Controlled“ Maischeanlage}}}, year = {{2019}}, } @inproceedings{5466, author = {{Zimmer, Manuel and Conradi, Florian and Wefing, Patrick and Schneider, Jan}}, location = {{Antwerpen}}, title = {{{Non-invasive on-line monitoring of the secondary bottle fermentation process using near infrared spectroscopy}}}, year = {{2019}}, } @inproceedings{5471, author = {{Conradi, Florian and Wefing, Patrick and Zhang, Fan and Schneider, Jan}}, location = {{Dresden}}, number = {{S1}}, pages = {{S103--S103}}, publisher = {{WILEY‐VCH}}, title = {{{Echtzeitqualitätssicherung enzymkatalysierter technologischer Prozesse am Beispiel des Maischens im Rahmen der Bierherstellung}}}, doi = {{10.1002/lemi.201951103}}, volume = {{73}}, year = {{2019}}, } @inproceedings{5474, author = {{Schneider, Jan and Regtmeier, J. and Conradi, Florian and Wefing, Patrick}}, location = {{Bielefeld}}, title = {{{Das Labor in der Leitung – Smarte Qualitätskontrolle von Lebensmitteln durch innovative Sensortechnik}}}, year = {{2019}}, } @inproceedings{5475, author = {{Schneider, Jan and Conradi, Florian and Wefing, Patrick and Weishaupt, Imke and Zimmer, Manuel and Schwarzer, Knut}}, location = {{Lemgo}}, title = {{{Muss man die Aufheizzonen einer KZE in die PE-Berechnung einbeziehen?}}}, year = {{2019}}, } @inproceedings{5630, author = {{Conradi, F. P. and Wefing, Patrick and Pinkal, K. and Zhang, Fan and Niggemann, Oliver and Schneider, Jan}}, location = {{Gent}}, title = {{{Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe}}}, year = {{2018}}, } @inproceedings{5631, author = {{Wefing, Patrick and Conradi, F. P. and Fuchs, Lara and Schoppmeier, J. W. and Pinkal, K. and Niggemann, Oliver and Schneider, Jan}}, location = {{Gent}}, title = {{{Laboratory plant for a closed loop-controlled continuous (CLCC) mashing}}}, year = {{2018}}, } @inproceedings{5633, author = {{Conradi, F. P. and Wefing, Patrick and Pinkal, K. and Zhang, Fan and Niggemann, Oliver and Schneider, Jan}}, location = {{Berlin}}, title = {{{Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe}}}, year = {{2018}}, } @inproceedings{5634, author = {{Wefing, Patrick and Conradi, Florian}}, location = {{Berlin}}, title = {{{Industrie 4.0 und Maischen – Aufbau einer Pilotanlage zum closed loop controlled mashing}}}, year = {{2018}}, } @inproceedings{5642, author = {{Wefing, Patrick and Conradi, F. P.}}, location = {{Berlin}}, title = {{{Industrie 4.0 und Maischen – Aufbau einer Pilotanlage zum closed loop controlled mashing}}}, year = {{2018}}, } @inproceedings{5581, author = {{Conradi, Florian and Wefing, Patrick}}, location = {{Lemgo}}, title = {{{Innovation in der Brauerei - Konzept einer kontinuierlichen Maischeanalge durch intelligente Vernetzung mit moderner Sensortechnik}}}, year = {{2017}}, }