A review of control strategies for manipulating the feed rate in fed-batch fermentation processes

Towards optimal substrate feeding for heterologous protein production in Pichia pastoris (Komagataella spp) fed‐batch processes under PAOX1 control: a modeling aided approach

“…This alternative thus allows a simple implementation of an optimized μ profile with S as the controlled variable thanks to the conjunction with the S‐μ‐q P model. That would minimize costs in an industrial application, and at the same time, avoid an open‐loop control that would cause the process to be hampered by system perturbations …”

“…That would minimize costs in an industrial application, and at the same time, avoid an open-loop control that would cause the process to be hampered by system perturbations. 51 In addition, a comparison with the most suitable alternatives previously reported with P. pastoris expressing ROL under the control of P AOX1 has also been carried out, and the data are presented in Table 3. Table 3 gathers the final performance indexes and mean specific rates obtained for each case.…”

Comparative study of μ‐stat methanol feeding control in fed‐batch fermentation of Pichia pastoris producing HBsAg: an open‐loop control versus recurrent artificial neural network‐based feedback control

“…ANN has already demonstrated to be a promising soft sensor for rapid and accurate monitoring and control of bioprocess systems for various recombinant host cells and proteins . However, no single study has been done for controlling the feed rate of recombinant P. pastoris Mut + producing intracellular HBsAg based on RNN.…”

“…have developed an accurate model based on biochemical reaction engineering principles by constructing it with structured conservation equations. In addition to the suggested parametric continuous feed stream design, online monitoring of specific growth rate can offer clear advantages, since it gives the possibility of integration of the operation unit with a feed controlling system to manipulate and adjust this key factor online in the case of any small or unexpected deviation (closed‐loop control) . Since specific growth rate cannot be measured directly, it should be calculated indirectly by biomass changes which can be monitored online by hardware sensors or software sensors .…”

“…This is mainly because, in addition to the general complexity of metabolic mechanisms of microorganisms, the fed‐batch fermentation process, owing to high variation with respect to time, is a highly dynamic and nonlinear process, which makes the mathematical description very complicated . Under these conditions, artificial neural network (ANN) can be a useful soft sensor tool for predicting and controlling critical but hardly accessible parameters by finding their nonlinear relationship with accessible key parameters …”