Korean Journal of Chemical Engineering, Vol.35, No.1, 195-203, 2018
Zeaxanthin production by Paracoccus zeaxanthinifaciens ATCC 21588 in a lab-scale bubble column reactor: Artificial intelligence modelling for determination of optimal operational parameters and energy requirements
The operational optimization of zeaxanthin production by Paracoccus zeaxanthinifaciens ATCC 21588 in a bubble column reactor was performed by coupling genetic algorithm (GA) to an artificial neural network (ANN) model developed using experimental one-variable-at-a-time (OVAT) results. The effects of varying air flow rate (2- 5 vvm) and inoculum size (4 and 8%) for different incubation time (30-80 h) were evaluated. Volumetric power input (P/VL) and energy input (E) to the bubble column were then correlated with the ANN-GA optimized conditions. A maximum zeaxanthin production of 13.76±0.14mg/L was observed at 4 vvm using an inoculum size of 4% (v/v) after 60h of incubation in OVAT experiments with corresponding P/VL value of 231.57 W/m3 reflecting an energy consumption of 50.02 kJ during the fermentation period. The ANN based GA optimization predicted a maximum zeaxanthin production of 14.79mg/L at 3.507 vvm, 4% inoculum size and 55.83 h against the experimental production of 15.09±0.51mg/L corresponding to a P/VL value of 202.03 W/m3 reflecting to a significantly reduced energy input (40.01 kJ). The proposed OVAT based ANN-GA optimization approach can be used to simulate similar studies involving microbial fermentation in bioreactors.
Cheng YT, Yang CF, J. Taiwan Inst. Chem. Eng., 61, 270, 2016
Berry A, Janssens D, Humbelin M, Jore JP, Hoste B, Cleenwerck I, Vancanneyt M, Bretzel W, Mayer AF, Lopez-Ulibarri R, Shanmugam B, Int. J. Syst. Evol. Microbiol., 53, 231, 2003
Schocher AJ, Wiss O, US Patent, 3,891,504 (1975).
Doran PM, Bioprocess engineering principles, Academic Press, London (1995).