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Title: On-line, adaptive, optimal control of a high-density, fed-batch fermentation of Streptomyces C5

Thesis/Dissertation ·
OSTI ID:5533789

Traditional techniques of growing microorganisms in submerged culture have relied upon reproducing previously successful fermentations by applying pre-established, empirically-determined paths. However, inadequate instrumentation and such open loop control schemes have provided little ability for the system to adapt to process changes. This research addressed these problems through the application of an online, adaptive, optimal control technique that is based upon process information obtained from an extended Kalman filter. The experimental system consisted of an anthracycline-producing bacterium, Streptomyces C5, grown in a defined medium, in a 14-liter fermentor. The optimal controller manipulated the nutrient feed rate to control biomass production by employing a time-optimal policy. The controller's design incorporated physical constraints imposed by the nutrient feed pump and the glucose sensor. The optimal controller used estimates of 3 state variables and 5 parameters from an extended Kalman filter. Limited knowledge about the behavior of streptomycetes and the variable growth pattern of S. C5 in a defined medium required that the estimator be capable of performing under a range of conditions including: exponential or cube root growth laws; transition from trophophase to idiophase; different cell morphologies from dispersed flocs to pellets; and rapid changes in process parameters, such as the oxygen mass transfer coefficient due to antifoam addition. Early estimator and controller development was performed through simulation studies of high cell density conditions in which Contois kinetics and low dissolved oxygen concentration affected growth. Results of simulation studies are reported as is an experimental application in which the controller maintained conditions with +/- 4.5% of the optimum despite the microorganism exhibiting unexpected, large, rapid changes in growth rate.

Research Organization:
Ohio State Univ., Columbus (USA)
OSTI ID:
5533789
Resource Relation:
Other Information: Thesis (Ph. D.)
Country of Publication:
United States
Language:
English