Abstract
A novel on-line adaptive optimization algorithm is developed and applied to continuous biological reactors. The algorithm makes use of a simple nonlinear estimation model that relates either the cell-mass productivity or the cell-mass concentration to the dilution rate. On-line estimation is used to recursively identify the parameters in the nonlinear process model and to periodically calculate and steer the bioreactor to the dilution rate that yields optimum cell-mass productivity. Thus, the algorithm does not require an accurate process model, locates the optimum dilution rate online, and maintains the bioreactors at this optimum condition at all times. The features of the proposed new algorithm are compared with those of other adaptive optimization techniques presented in the literature. A detailed simulation study using three different microbial system models was conducted to illustrate the performance of the optimization algorithms. (orig.).
Sauvaire, P;
Mellichamp, D A;
Agrawal, P
[1]
- California Univ., Santa Barbara, CA (United States). Dept. of Chemical and Nuclear Engineering
Citation Formats
Sauvaire, P, Mellichamp, D A, and Agrawal, P.
Nonlinear adaptive optimization of biomass productivity in continuous bioreactors.
Germany: N. p.,
1991.
Web.
doi:10.1007/BF00369421.
Sauvaire, P, Mellichamp, D A, & Agrawal, P.
Nonlinear adaptive optimization of biomass productivity in continuous bioreactors.
Germany.
https://doi.org/10.1007/BF00369421
Sauvaire, P, Mellichamp, D A, and Agrawal, P.
1991.
"Nonlinear adaptive optimization of biomass productivity in continuous bioreactors."
Germany.
https://doi.org/10.1007/BF00369421.
@misc{etde_6034643,
title = {Nonlinear adaptive optimization of biomass productivity in continuous bioreactors}
author = {Sauvaire, P, Mellichamp, D A, and Agrawal, P}
abstractNote = {A novel on-line adaptive optimization algorithm is developed and applied to continuous biological reactors. The algorithm makes use of a simple nonlinear estimation model that relates either the cell-mass productivity or the cell-mass concentration to the dilution rate. On-line estimation is used to recursively identify the parameters in the nonlinear process model and to periodically calculate and steer the bioreactor to the dilution rate that yields optimum cell-mass productivity. Thus, the algorithm does not require an accurate process model, locates the optimum dilution rate online, and maintains the bioreactors at this optimum condition at all times. The features of the proposed new algorithm are compared with those of other adaptive optimization techniques presented in the literature. A detailed simulation study using three different microbial system models was conducted to illustrate the performance of the optimization algorithms. (orig.).}
doi = {10.1007/BF00369421}
journal = []
volume = {7:3}
journal type = {AC}
place = {Germany}
year = {1991}
month = {Nov}
}
title = {Nonlinear adaptive optimization of biomass productivity in continuous bioreactors}
author = {Sauvaire, P, Mellichamp, D A, and Agrawal, P}
abstractNote = {A novel on-line adaptive optimization algorithm is developed and applied to continuous biological reactors. The algorithm makes use of a simple nonlinear estimation model that relates either the cell-mass productivity or the cell-mass concentration to the dilution rate. On-line estimation is used to recursively identify the parameters in the nonlinear process model and to periodically calculate and steer the bioreactor to the dilution rate that yields optimum cell-mass productivity. Thus, the algorithm does not require an accurate process model, locates the optimum dilution rate online, and maintains the bioreactors at this optimum condition at all times. The features of the proposed new algorithm are compared with those of other adaptive optimization techniques presented in the literature. A detailed simulation study using three different microbial system models was conducted to illustrate the performance of the optimization algorithms. (orig.).}
doi = {10.1007/BF00369421}
journal = []
volume = {7:3}
journal type = {AC}
place = {Germany}
year = {1991}
month = {Nov}
}