Robotic facilities that can perform advanced cultivations (e.g., fed‐batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation for the implementation of feedback control methods in miniaturized bioreactor systems, where observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma‐Point Kalman Filter in a high throughput platform with 24 parallel experiments at the mL‐scale to demonstrate its viability and added value in high throughput experiments. The filter exploits the information generated by the ammonia‐based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.

Revenues of anaerobic digestion (AD) plants can be increased by generating biogas and electricity on demand or through biogas upgrading. However, suitable control procedures for individual applications are required to guarantee optimal process conditions. Moreover, substrate characterization at full scale plants is often subject to large uncertainties. In this contribution, a multi-stage nonlinear model predictive controller (NMPC) was designed to optimize substrate feedings under uncertain substrate characterization. Simulations demonstrate that multi-stage NMPC provides biogas on demand to flexibly operate a combined heat and power unit, while safe gas storage filling limits are ensured (20 % safety margin) despite uncertain influent concentrations. Additionally, multi-stage NMPC successfully tracked changing setpoints of constant methane production for biogas upgrading within 12 h, and rejected disturbances posed by disturbing feedings of very high uncertainty. This study demonstrates that model-based control strategies like multi-stage NMPC can enhance the potential of anaerobic digestion plants by enabling demand-oriented operation despite substrate uncertainties and without fundamentally changing the existing hardware, which showcases more sustainable and profitable biogas production.

In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO₂ concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.