Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations

Abstract

Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant Escherichia coli cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and E. coli cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by E. coli during the cultivation.

Keywords

• adaptive network-based fuzzy inference system
• dissolved oxygen
• fuzzy control
• recombinant <i>Escherichia coli</i>
• SUPERSYS_HCDC