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Model structures for batch and fed-batch ethanol fermentations

机译:分批和补料分批乙醇发酵的模型结构

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摘要

Parameter estimation is one of the most challenging steps while modelling a biological process, due to the inherent complexity of this type of processes and the difficulty in obtaining reliable experimental data. A common modelling practice in this area is to use a batch culture to estimate model parameters and then use these values to simulate and optimize a fed-batch culture. Most of the times this approach is inadequate, resulting in unreliable model predictions. In this study, we analyzed and reparameterized batch and fed-batch kinetic models taken from the literature. A sensitivity analysis applied to the original models showed that most estimated parameters were unidentifiable, unsensitive and not significant for the data of different temperatures. Applying HIPPO, an automatized iterative reparameterization procedure coded in MATLAB, we searched for model structures, for both batch and fed-batch cultures, with identifiable, sensitive and significant estimated parameters. After the analysis of the models, we found a single model structure, suitable for both type of cultures, with two estimable parameters that showed a good fit at all temperatures. Nevertheless, a residual analysis showed that the parameters are positively correlated, indicating that apparently the model needs another degree of freedom.
机译:由于这种过程固有的复杂性以及难以获得可靠的实验数据,因此参数估计是对生物过程进行建模时最具挑战性的步骤之一。在该领域中,常见的建模实践是使用批处理培养来估计模型参数,然后使用这些值来模拟和优化补料分批培养。在大多数情况下,这种方法是不合适的,从而导致模型预测不可靠。在这项研究中,我们分析并重新参数化了从文献中获得的批次和进料批次动力学模型。对原始模型进行的敏感性分析表明,大多数估计的参数对于不同温度的数据都是无法识别,不敏感且不重要的。应用HIPPO(一种用MATLAB编码的自动迭代重新参数化程序),我们搜索了具有可识别,敏感和重要的估计参数的批处理和补料分批培养的模型结构。在对模型进行分析之后,我们发现了一个适用于两种培养物的单一模型结构,其中两个可估计参数在所有温度下均显示出良好的拟合度。但是,残差分析表明参数是正相关的,这表明该模型显然需要另一个自由度。

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