Data science-based modeling of the lysine fermentation process.

Mathematical modeling of the fermentation process is useful for understanding the influence of operating parameters on target production and control performance, depending on the situation, to stabilize the target production at a high-level. However, the previous approaches using physical modeling methods and traditional knowledge-based methods are difficult to apply on working fermentors at a commercial plant scale because they have unknown and unmeasured parameters involved in target production. This study focused on developing an ensemble learning model that can predict the amino acid fermentation process behavior based on observation values, which can be obtained from fermentation tanks and future control input. The results revealed the influence of each control input on lysine production during the culturing period. Furthermore, high-order stability, which achieved the target trajectory for lysine production, was realized using dynamic fermentation controls. Additionally, this study demonstrates that the fermentation behavior on a commercial plant scale is reproduced using the ensemble device. The ensemble learning model will provide novel control system with data-science based model of Industry 4.0 in the field of biotechnological processes.