Process analytical technologies to monitor the liquid phase of anaerobic cultures

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM)

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

Raman spectroscopy applied to online monitoring of a bioreactor: Tackling the limit of detection

An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L-1 for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L-1. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.

Phase Separation in Anaerobic Digestion: A Potential for Easier Process Combination?

The flexibilization of bioenergy production has the potential to counteract partly other fluctuating renewable energy sources (such as wind and solar power). As a weather-independent energy source, anaerobic digestion (AD) can offer on-demand energy supply through biogas production. Separation of the stages in anaerobic digestion represents a promising strategy for the flexibilization of the fermentative part of biogas production. Segregation in two reactor systems facilitates monitoring and control of the provision of educts to the second methanogenic stage, thus controlling biogas production. Two-stage operation has proven to reach similar or even higher methane yields and biogas purities than single-stage operation in many different fields of application. It furthermore allows methanation of green hydrogen and an easier combination of material and energy use of many biogenic raw and residual biomass sources. A lot of research has been conducted in recent years regarding the process phase separation in multi-stage AD operation, which includes more than two stages. Reliable monitoring tools, coupled with effluent recirculation, bioaugmentation and simulation have the potential to overcome the current drawbacks of a sophisticated and unstable operation. This review aims to summarize recent developments, new perspectives for coupling processes for energy and material use and a system integration of AD for power-to-gas applications. Thereby, cell physiological and engineering aspects as well as the basic economic feasibility are discussed. As conclusion, monitoring and control concepts as well as suitable separation technologies and finally the data basis for techno-economic and ecologic assessments have to be improved.

Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators

Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.