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Kinetics and reactor modeling of L-lysine fermentation.

机译:L-赖氨酸发酵的动力学和反应器模型。

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In order to model and control a bioprocess it is crucial to understand its dynamics. None of the past studies included complete kinetics of the L-lysine fermentation. The goal of this research was to investigate effects of process variables, such as dissolved oxygen, agitation, aeration, influent substrate concentration and rate, on the kinetics of L-lysine fermentation and to develop a fundamental model to optimize the bioreactor operation. Steady state CSTR data indicated that the specific rates of growth, substrate consumption, product formation and oxygen uptake were functions of the cell, substrate, product concentrations, and dissolved oxygen. The unstructured model developed incorporated the oxygen transfer and was able to predict both transient and steady state behavior of continuous culture as well as apparent effects of influent substrate concentration. The results are remarkable, because it is extremely difficult to simulate unbalanced growth by unstructured models developed from balanced growth data. The model was further adapted to batch culture with controlled dissolved oxygen and proved to simulate batch experiments at different initial substrate concentrations well. As expected, continuous culture gave the highest productivity. While many fundamental models were developed in the past for microbial cultures, little is known for the cell cultures. Therefore, the methodology developed in this research can be very useful for the cell cultures.
机译:为了建模和控制生物过程,了解其动力学至关重要。过去的研究都没有包括L-赖氨酸发酵的完整动力学。这项研究的目的是研究工艺变量(如溶解氧,搅拌,曝气,进水底物浓度和速率)对L-赖氨酸发酵动力学的影响,并开发一个优化生物反应器操作的基本模型。稳态CSTR数据表明,特定的生长速率,底物消耗,产物形成和氧吸收是细胞,底物,产物浓度和溶解氧的函数。开发的非结构化模型结合了氧的转移,能够预测连续培养的瞬时和稳态行为,以及进水底物浓度的明显影响。结果是惊人的,因为通过平衡增长数据开发的非结构化模型很难模拟不平衡增长。该模型进一步适用于在受控的溶解氧条件下进行分批培养,并证明可以很好地模拟不同初始底物浓度下的分批实验。不出所料,连续培养获得了最高的生产率。尽管过去针对微生物培养开发了许多基本模型,但对于细胞培养知之甚少。因此,这项研究中开发的方法对于细胞培养可能非常有用。

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