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Adaptive steady‐state optimization of biomass productivity in continuous fermentors
Author(s) -
Harmon Jeff,
Svoronos Spyros A.,
Lyberatos Gerasimos
Publication year - 1987
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.260300302
Subject(s) - chemostat , biomass (ecology) , steady state (chemistry) , bioreactor , productivity , dilution , process (computing) , stability (learning theory) , control theory (sociology) , production (economics) , biological system , computer science , process engineering , mathematical optimization , biochemical engineering , mathematics , engineering , chemistry , biology , ecology , genetics , physics , control (management) , macroeconomics , organic chemistry , machine learning , artificial intelligence , bacteria , economics , operating system , thermodynamics
Abstract An adaptive steady‐state optimization algorithm is presented and applied to the problem of optimizing the production of biomass in continuous fermentation processes. The algorithm requires no modeling information but is based on an on‐line identified linear model, locates the optimum dilution rate, and maintains the chemostat at its optimum operating condition at all times. The behavior of the algorithm is tested against a dynamic model of a chemostat that incorporates metabolic time delay, and it is shown that large disturbances in the subtrate feed concentration and the specific growth rate, causing a shift in the optimum, are handled well. The developed algorithm is also used to drive a methylotroph single‐cell production process to its optimum.