NEURAL NETWORK MODEL FOR DISSOLVED OXYGEN CONTROL IN A BATCH FERMENTER

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate. Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

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

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-09586-x ◽

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

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Research on Dissolve Oxygen Modeling Based on Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.287-290.2640 ◽

2011 ◽

Vol 287-290 ◽

pp. 2640-2643

Author(s):

Guo Dong Gao ◽

Wen Xiao Zhang ◽

Gong Zhi Yu ◽

Jiang Hua Sui

Keyword(s):

Neural Network ◽

Dissolved Oxygen ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Structure Learning ◽

Learning Algorithm ◽

Impact Factors ◽

Function Learning ◽

Bp Neural Network Model

The structure, characteristics and principles of BP neural network model are described in this paper. First, three impact factors of the dissolved oxygen are selected as the sample input of network, and then the parameters of BP neural network are selected, such as network structure, learning algorithm, output layer transfer function, learning rate and so on. Finally, the BP neural network model is established and trained, in order to approach compensate the effects of improves non-linearity. The simulation results show that BP neural network is practical and dependable in the field of dissolved oxygen modeling and has nice applied prospect.

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Adaptive controller based on flexi-structure neural network for dissolved oxygen control

2010 International Conference on Intelligent Control and Information Processing ◽

10.1109/icicip.2010.5564271 ◽

2010 ◽

Author(s):

Zhaozhao Zhang ◽

Wei Guo

Keyword(s):

Neural Network ◽

Dissolved Oxygen ◽

Adaptive Controller ◽

Oxygen Control ◽

Dissolved Oxygen Control

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A pretreatment method of wastewater based on artificial intelligence and fuzzy neural network system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179945 ◽

2020 ◽

Vol 39 (2) ◽

pp. 1711-1720

Author(s):

He Chan ◽

Yan Nai-He

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Network Model ◽

Neural Network Model ◽

Fuzzy Neural Network ◽

Saline Wastewater ◽

Pretreatment Method ◽

Fuzzy Function ◽

Dissolved Oxygen Control ◽

Fuzzy Neural

A pretreatment method of industrial saline wastewater based on Artificial Intelligence based fuzzy neural network analysis was proposed to improve the pretreatment accuracy of industrial saline wastewater. This method uses a four-layer AI fuzzy neural network model and proposes a graded fuzzy neural network model for pretreatment method of industrial saline wastewater, it includes input layer, fuzzification layer, fuzzy logical layer and output layer, and designs the framework and calculation mode of the fuzzy function block and the neural network module. Finally, the dynamic simulation experiments of dissolved oxygen control in the fifth zone and nitrate nitrogen control in the second zone are carried out based on the simulation benchmark model (BSM1) platform. The experimental results show that this approach can effectively raise the adaptive control accuracy of the system compared with PID, feed forward neural network and conventional recurrent neural network.

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