On‐line‐Messung und Regelung wichtiger Produktparameter bei einer industriellen Fermentation

Peters, Udo; Hege, U.; Enders, T.; Becker, Thomas; Delgado, Antonio; Denk, V.

doi:10.1002/cite.330690914

Cited by 1 publication

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“…The modeling of the fermentation process results from the adaptation of all weights of the neural network with experimental data. For the adaptation of the process model experimental data from over 50 fermentations, measured with an automated pilot fermenter, were used [16,17].…”

Section: Modelingmentioning

confidence: 99%

Dynamic neural networks as a tool for the online optimization of industrial fermentation

Becker

Enders

Delgado

2002

Bioprocess and Biosystems Engineering

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A system for online optimization of industrial fermentation based on a model with dynamic neural networks is described. The developed dynamic neural network, consisting of adapted neurons to consider the process dynamics, can model the complex, non-linear fermentation of beer in order to predict the future process. The predicted trajectories of gravity, pH, and diacetyl are in agreement with the experimental data measured at an automated pilot fermenter. It was possible to predict the future course of the batch fermentation as soon as 12 h of process data were available. In combination with the variational principle, the process model was used to optimize productivity. The temperature trajectory is optimized using a cost functional, including technical and technological conditions of the brewery in order to reduce the process time by steady product quality. The results show a reduction of the process time of up to 20%, which leads to an increase in utilization capacity.

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Section: Modelingmentioning

confidence: 99%