Process analytical technologies to monitor the liquid phase of anaerobic cultures

Bockisch, Anika; Kielhorn, Erich; Neubauer, Peter; Junne, Stefan

doi:10.1016/j.procbio.2018.10.005

Cited by 11 publications

(9 citation statements)

References 62 publications

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“…Early bioreactor control was dependent on human operation based on workers' knowledge and long-term practical experience. With the development of sensor technology, which enables on-line monitoring of various process parameters and the application of digital computers and actuators, process monitoring and control of bioreactors has stepped into the age of automation (Stanke and Hitzmann, 2013;Lemoine et al, 2017;Bockisch et al, 2019). Simultaneously, the HSCS for bioreactors emerged and evolved after decades of practical application (Rolf et al, 1982).…”

Section: Classic Hierarchical Structure Control Systemsmentioning

confidence: 99%

“…For some physical and chemical parameters like temperature, pH, rotation speed, DO, pressure, liquid level, OD, and viscosity, specific functional sensors have been developed, and control of these process parameters can be carried out via classic control strategies based on specific actuators (Rodríguez-Duran et al, 2001). For physiological and biochemical parameters like biomass, key nutrient and metabolite concentrations, and gas composition, more complex monitoring facilities, as well as advanced control strategies and algorithms, are usually adopted (Lemoine et al, 2017;Bockisch et al, 2019). The middle layer of a bioreactor HSCS consists of industrial field computers, such as the early PLC systems with a microprocessor module.…”

Section: Classic Hierarchical Structure Control Systemsmentioning

confidence: 99%

“…Although most of the physical and chemical process parameters can be directly monitored with specific functional sensors, there are still some process parameters, especially those related to the physiological and biochemical characteristics of cultures, that cannot be easily measured. Various novel sensors and cutting-edge PATs have been developed as the basis of smart sensors, as has been discussed in some excellent reviews (Randek and Mandenius, 2018;Bockisch et al, 2019). Among them, studies on optical sensors and spectrographic analytical techniques have attracted a great deal of attention.…”

Section: Application Of Smart Sensors and Actuators In Novel Bioreactmentioning

confidence: 99%

“…Ladner et al (2016) and Assawajaruwan et al (2017) reported the integrated application of fluorescence spectroscopy with the microtiter plate cultivation system and the development of multi-wavelength (2D) fluorescence spectroscopy techniques for bioprocess monitoring. In addition, many different optical sensors and analytical techniques based on visible light (e.g., RGB sensors), UV light, or NIR, Raman, or pulsed terahertz spectra have also been developed for monitoring the biochemical parameters of the fermentation broth (Classen et al, 2017;Bockisch et al, 2019). These novel sensors and PATs could be further developed into smart sensors for application in the newly developed next-generation bioreactor control systems.…”

Section: Application Of Smart Sensors and Actuators In Novel Bioreactmentioning

confidence: 99%

“…Multi-parameter sensors are another interesting direction of smart sensor development (Bockisch et al, 2014(Bockisch et al, , 2019Sardesai et al, 2015). In most cases, many process parameters need to be monitored simultaneously for process development, and the application of multi-parameter sensors could dramatically reduce the total number of sensors used.…”

Section: Multi-parameter Sensors For the Simultaneous Monitoring Of Mmentioning

confidence: 99%

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

Wang

Chen

et al. 2020

Front. Bioeng. Biotechnol.

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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.

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Section: Classic Hierarchical Structure Control Systemsmentioning

confidence: 99%

Section: Classic Hierarchical Structure Control Systemsmentioning

confidence: 99%

Section: Application Of Smart Sensors and Actuators In Novel Bioreactmentioning

confidence: 99%

Section: Application Of Smart Sensors and Actuators In Novel Bioreactmentioning

confidence: 99%

Section: Multi-parameter Sensors For the Simultaneous Monitoring Of Mmentioning

confidence: 99%

See 3 more Smart Citations

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

Wang

Chen

et al. 2020

Front. Bioeng. Biotechnol.

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Bioprocess in‐line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

Müller

Flake

Brands

et al. 2023

Biotech & Bioengineering

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To increase the process productivity and product quality of bioprocesses, the in‐line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in‐situ and non‐invasively. However, evaluating bioprocess Raman spectra with a robust state‐of‐the‐art statistical model requires effortful model calibration. In the present study, we in‐line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics‐based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.

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

Müller,

Börger,

Thien

et al. 2024

Biotech & Bioengineering

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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.

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Process analytical technologies to monitor the liquid phase of anaerobic cultures

Cited by 11 publications

References 62 publications

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

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

Bioprocess in‐line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

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

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