期刊
28TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING
卷 43, 期 -, 页码 1129-1134出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/B978-0-444-64235-6.50197-2
关键词
Fuzzy Logic; Dissolved Oxygen; Artificial Neural Network; recombinant Escherichia coli; SUPERSYS_HCDC
资金
- CNPq
- CAPES
- FAPESP
One very important bioprocess is the cultivation of recombinant E. coli for expression of heterologous protein. For this, High Cell Density Cultures is one of the most widely used technique. Therefore, researchers from the Chemical Engineering Department of Federal University of Sao Carlos (UFSCar) developed a very useful computer program (SUPERSYS_HCDC) that, among other functions, presents a hybrid system with a PID for agitation and a decision tree for air and oxygen flow rates that controls the percentage of dissolved oxygen in the cultivation (nowadays some commercial controllers also offers this cascade control). However, in particular, delays may occur in the device responsible for air and oxygen injection into the bioreactor, since the decision tree provides no smooth responses. The original system presented operates by introducing steps in the air and oxygen flow rates. Under the light of the above-mentioned facts, fuzzy reasoning was used to develop a fuzzy controller, aiming to improve dissolved oxygen control in recombinant E. coli cultivation for heterologous protein production. At first, fuzzy logic toolbox was used to generate a control algorithm implemented in a MATLAB code. Secondly, the membership function parameters were optimized using ANFIS tool. Finally, in order to perform tests using the fuzzy controller, it was coupled to a neural network model of the process. This was created using artificial neural network toolbox and E. coli cultivation data. Results for oxygen and air flow rates indicated that the trends of aeration required by E. coli cultivation were fulfilled. Using the fuzzy controller, it was possible to maintain the percentage of dissolved oxygen around the set point value of 30%. In general, the fuzzy controller responses were smoother than those provided by the decision tree, in a way that the dissolved oxygen peaks were softened.
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