Control of dissolved oxygen concentration using neural network in a batch bioreactor

2010 ◽  
Vol 20 (4) ◽  
pp. 619-628 ◽  
Author(s):  
Tufan Mete ◽  
Gulay Ozkan ◽  
Hale Hapoglu ◽  
Mustafa Alpbaz
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Dissolved Oxygen Concentration ◽  
Batch Bioreactor

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

2021 ◽  
Vol 193 (12) ◽  
Author(s):  
Salar Valizadeh Moghadam ◽  
Ahmad Sharafati ◽  
Hajar Feizi ◽  
Seyed Mohammad Saeid Marjaie ◽  
Seyed Babak Haji Seyed Asadollah ◽  
...  
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Dissolved Oxygen Concentration ◽  
Efficient Strategy ◽  
Deep Recurrent Neural Network

Self-tuning control of dissolved oxygen concentration in a batch bioreactor

2009 ◽  
Vol 87 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Suna Ertunc ◽  
Bulent Akay ◽  
Havva Boyacioglu ◽  
Hale Hapoglu
Keyword(s):  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Dissolved Oxygen Concentration ◽  
Batch Bioreactor ◽  
Self Tuning

APPLICATION OF ANN-INVERSION SOFT-SENSING METHOD IN BIOCHEMICAL FERMENTATION

2004 ◽  
Vol 01 (04) ◽  
pp. 371-379 ◽  
Author(s):  
XIANZHONG DAI ◽  
DONGCHUAN YU ◽  
YUHAN DING ◽  
WANCHENG WANG
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Soft Sensor ◽  
Experimental Results ◽  
Dissolved Oxygen Concentration ◽  
Soft Sensing ◽  
Biochemical Variables ◽  
Neural Network Inversion

Based on Artificial Neural Network inversion (ANN-inversion), a novel soft-sensing method is presented to estimate some direct-unmeasurable biochemical variables, such as mycelia concentration, sugar concentration and chemical potency from other direct-measurable variables such as dissolved oxygen concentration, pH, and volume in erythromycin fermentation. The proposed ANN-inversion soft-sensor is composed of a static ANN and several differentiators. Experimental results show that the soft-sensing values are almost identical with the actual ones, or the designed ANN-inversion soft-sensor possesses good approximate ability to the direct-unmeasurable variables.

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