Prediction of Biomass Concentration with Hybrid Neural Network

Zhang, Dapeng; Cheng, BaoHua; Wu, Ai‐Guo

doi:10.1007/978-3-642-31362-2_70

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, up to now it cannot be measured directly in an on-line process. Therefore, indirect measurements based upon soft sensing techniques such as artificial neural networks are applied [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

Birle

Hussein

Becker

2014

Engineering in Life Sciences

View full text Add to dashboard Cite

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real-time. This work presents a system for on-line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on-line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on-line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on-line biomass estimation. The root mean squared error of prediction is 4 × 10 6 cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy-based expert system allows to provide the desired yeast cell concentration of 100-120 × 10 6 cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.

show abstract