Published: CABEQ 30 (1) (2016) 47-60
Paper type: Original Scientific Paper
O. Grigs, V. Galvanauskas, K. Dubencovs, J. Vanags, A. Suleiko, T. Berzins and L. Kunga
A developed solution for fed-batch process modeling and model predictive control (MPC), facilitating good manufacturing practice (GMP) based on process elaboration, control, and validation, is presented in the paper. The step-by-step evolution of the socalled “golden batch” optimal biomass growth profile and its control during the process is demonstrated. The case study of an advanced fed-batch control was performed on the recombinant E. coli BL21 lab-scale (5.4 L) biomass production process using the conventional stirred tank glass reactor. Additionally, a test experiment for control reproducibility and applicability assessment of the proposed approach was carried out in a single-use stirred tank reactor (5.7 L). Four sequentially performed experiments are demonstrated as an example for desirable feeding profile evolution for E. coli BL21 biomass production in a glucose-limited fed-batch process. Under different initial biomass and glucose conditions, as well as for different reference feeding profiles selected in the explorative experiments, good tracking quality of preset reference trajectories by the MPC system has been demonstrated. Estimated and experimentally measured biomass mean deviations from the preset reference value at the end of the processes were 4.6 and 3.8 %, respectively. Biomass concentration of 93.6 g L–1 (at 24 h) was reached in the most productive run. Better process controllability and safer process run, in terms of avoiding culture overfeeding but still maintaining a sufficiently high growth rate, was suggested for the process with biomass yield of 79.8 g L–1 (at 24 h). Practical recommendations on the approach application and adaptation for fed-batch cultures of interest are provided.
This work is licensed under a Creative Commons Attribution 4.0 International License
fed-batch, process reproducibility, model predictive control, model adaptation, bioreactors, Matlab