Extension of first principle elemental balancing soft-sensors by nonlinear reaction kinetics for increased robustness in bioprocess monitoring

Abstract

A first principle soft-sensor for biomass and substrate estimation in upstream bioprocessing based on the fusion of elemental balancing and nonlinear kinetics is presented. It aims to extend the validity range of well-established elemental balancing soft sensors to substrate saturated and overfeeding conditions that often occur in induced production phases. An experimental study with recombinant E. coli cultivations was conducted to illustrate the soft-sensor principle and to analyze the accuracy as well as generalizability of the approach. Under substrate limited growth the extended soft-sensor showed similar performance as classical elemental balancing. In induced production phases however, a decline in maximum substrate uptake capacity ( $q_{\mathrm{Smax}}$ ) of up to 80% was observed, where the extended soft-sensor showed up to 41 % better estimates for the biomass and up to 75 % better estimates for the substrate in terms of NRMSE. The paper discusses the possible benefits as well as the requirements for the implementation of the extended elemental balancing soft-sensor.

Keywords: Biomass estimation; Bioprocessing; Elemental balancing; Soft-sensor.