How is kla determined for bioreactors?
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The volumetric oxygen transfer coefficient (kLa) in bioreactors can be determined using various methods. One method is the dynamic method, which involves measuring the oxygen concentration response over time in a stirred tank reactor . Another method is the gassing-out technique, which is widely used and simple to apply. However, this method is sensitive to probe dynamics and can lead to inaccuracies in kLa determination, especially in airlift bioreactors. To overcome this, a new method has been proposed that simultaneously determines kLa and electrode sensitivity without the need for additional assays . Additionally, an automated algorithm has been developed for estimating kLa in conventional bioreactors, which yields reliable results in a fraction of the manual processing time . Simulation studies have also shown that KLa values for O2 and CO2 can be determined from measurements taken in the gas phase, taking into account the gas phase dynamics to avoid errors .
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| The paper discusses a method for determining KLa values for O2 and CO2 in fermenters using the dynamic method measuring step responses in the gas phase. | |
4 Citations | The paper describes an automated algorithm to estimate the volumetric oxygen transfer coefficient (kLa) in bioreactors. The algorithm involves data smoothing, selecting a high oxygen variation zone, selecting a constant time period, and estimating kLa. |
11 Citations | The paper proposes a new method for determining kLa in bioreactors using the gassing-out technique. The method overcomes the effects of probe delay and allows for the simultaneous determination of kLa and electrode sensitivity (ke). |
| The paper mentions four methods to estimate the overall oxygen mass transfer coefficient (KLa) for bioreactors: the dynamic method, the stationary method based on a previous determination of the oxygen uptake rate (QO2X), the gaseous oxygen balance, and the carbon dioxide balance. | |
| The paper describes the measurement of the volumetric mass transfer coefficient (kLa) in a stirred tank reactor using a dynamic method based on unsteady state. |
Related Questions
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Can artificial intelligence be used to improve the performance of bioreactors?4 answersArtificial intelligence (AI) can be used to improve the performance of bioreactors. AI-based models have been developed to predict the performance of membrane bioreactors (MBRs) in treating wastewater, allowing for the recovery of clean water from polluted sources. In the field of alcoholic fermentation, AI has been used to design multilayer neural networks that can accurately predict the state variables of the process, reducing the need for extensive measurements and increasing sensor lifecycles. Additionally, computational methods combining mechanistic models and flux balance analysis have been used to predict cell culture performance in bioreactors, allowing for better control and optimization of the process. AI techniques have also been applied to biomass-to-biofuels conversion processes, such as transesterification, alcoholic fermentation, anaerobic digestion, and dark fermentation, to predict biofuel production and optimize process parameters. Overall, AI has shown promise in improving the efficiency and effectiveness of bioreactors in various applications.
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What is the energy consumption of membrane bioreactors?5 answersMembrane bioreactors (MBRs) have been studied for their energy consumption. One study found that a novel MBR employing reciprocating motion had a specific energy demand (SED) ranging from 0.003 to 0.015 kWh/m3, resulting in up to an 85% reduction in energy consumption compared to conventional MBRs. Another study developed a low energy consumption vortex wave flow MBR, which had an average energy consumption of 1.90 ± 0.55 kWh/m3, only two-thirds of conventional MBR energy consumption. A comparison between MBRs and activated sludge systems showed that MBRs can operate at similar or lower energy consumption levels, around 3 kWh/m3, while producing treated water suitable for unrestricted crop irrigation. Additionally, a study on side-stream MBRs found that the specific energy consumption varied depending on the mixed liquor suspended solids concentration and the amount of backwashes. Overall, the energy consumption of MBRs can vary depending on the specific design and operational parameters.
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