CARBON DIOXIDE GENERATION, TRANSPORT AND RELEASE DURING THE FERMENTATION OF BARLEY MALT
Notice bibliographique
Résumé
Carbon Dioxide (CO2) is a major fermentation product generated during the production of beer, the subsequent release of this gas within the fermentor results in agitation that is necessary for sustained industrial fermentation. CO2 is sometimes monitored allowing brewers to stoichiometrically relate CO2 released to other products. In this manner the rate of gas release from the fermentor may be used to assess, control and predict other aspects of fermentation. The dynamics of CO2 generation, transport and release are explored throughout this thesis over several studies. The tools used to examine CO2 production were scrutinized including a miniature assay using various modeling techniques. \nA miniature scale fermentation assay included in the methods of the American Society of Brewing Chemists was compared to industrial scale fermentations. It was found that discrepancies were possibly due (at least in part) to fermentor geometry. Following this study, a literature review of CO2 solubility in aqueous sugar, and ethanol solutions was conducted. This study exposed previously undescribed inaccuracies in literature, i.e., it was found that several gas solubility tables were empirical derived and are therefore unlikely to accurately reflect all styles of beer. The next study scrutinized the consumption of sugars during barley fermentation and found that these fermentations often exhibit asymmetric sigmoidal attenuation. A five parameter logistic model was introduced to model this sugar consumption more accurately than previously described techniques. Using methods refined during the aforementioned studies, a fermentation was conducted where a mass balance was used to track all major fermentation parameters (the consumption of individual sugars, and the production of ethanol, carbon dioxide, yeast biomass and glycerol). This allowed an assessment of Balling’s theorem as compared to modern theory. It was shown that while accurate in predicting original extract, Balling’s theorem incorrectly quantified other fermentation parameters. This has large ramifications for both industry and research as the estimation of fermentation parameters (such as ethanol and fermentation time) is now better understood.\nFrom these studies, the production of beer becomes less of a “black box” operation, and CO2 saturation, transport and release can be better explained. Of the many fermentation aspects monitored during these studies, most were predicted by theory, however, there were notable exceptions. For instance, it was found that both the inhibition of maltose consumption and yeast sugar consumption dynamics (which remained relatively constant throughout the fermentation at ~ 50 pg·h-1 for cells with an average mass of ~ 40 pg). were found to deviate from previously described reports. These, and other findings improve our understanding of brewing fermentations allowing for additional applications of theory and recommendations in industrial operations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».