Model predictive control with supervisory substrate targeting for multi-setpoint biomass control in continuous bioprocesses

Abstract

Precise biomass control is critical in microbial fermentation processes, particularly under continuous and cascade production conditions where physiological stability is essential. This work presents an experimental implementation of a supervisory layer to a nonlinear model predictive control (NMPC) for real-time regulation of biomass in Corynebacterium glutamicum fermentations. Two NMPC strategies were developed: a single-inflow control (SIC) system that feeds a concentrated substrate and a multi-inflow control (MIC) system that also adds a sugar-free medium for dynamic dilution. An Extended Kalman Filter (EKF) was employed, providing real-time estimates of biomass, substrate, and CO${}_2$ concentrations to enhance predictive control accuracy. Using a proportional substrate setpoint cost-shaping layer, the controller successfully tracked three biomass setpoints ($7.0,13.0,$ and $15.7g{L}^{-1}$) within a single fermentation run. Both NMPC strategies delivered tight setpoint tracking while sustaining exponential growth and preventing substrate limitation stress. MIC reduced overshoot by up to 78.0 % and the integral of absolute error by up to 41.1 % relative to SIC. These findings demonstrate the feasibility and effectiveness of NMPC for robust biomass regulation and provide a foundation for future applications in adaptive, multi-phase fermentation processes.