Extension of a Particle Filter for Bioprocess State Estimation using Invasive and Non-Invasive IR Measurements

Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty

In this study the use of a particle filter algorithm to monitor Corynebacterium glutamicum fed-batch bioprocesses with uncertain raw material input composition is shown. The designed monitoring system consists of a dynamic model describing biomass growth on spent sulfite liquor. Based on particle filtering, model simulations are aligned with continuously and noninvasively measured carbon evolution and oxygen uptake rates, giving an estimate of the most probable culture state. Applied on two validation experiments, culture states were accurately estimated during batch and fed-batch operations with root mean square errors below 1.1 g L^-1 for biomass, 0.6 g L^-1 for multiple substrate concentrations and 0.01 g g^-1 h^-1 for biomass specific substrate uptake rates. Additionally, upon fed-batch start uncertain feedstock concentrations were corrected by the estimator without the need of any additional measurements. This provides a solid basis towards a more robust operation of bioprocesses utilizing lignocellulosic side streams.