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An approximate probability graphical modelling method for complex industrial fermentation processes

机译:复杂工业发酵过程的近似概率图形建模方法

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Fed-batch fermentation process is an effective method for production. Due to the various feeding streams and operational conditions in different fed-batches, usually it is difficult to formulate a kinetics-based ordinary differential equations model for industrial fed-batch fermentation process. On the other hand, there are plenty of historical data collected during the fermentation process. In this paper, we firstly applied the graphical modeling method to model the fed-batch fermentation process. In this proposed method, the missing data within records are imputed, and then, the correlations between variables are determined by the low order conditional independence method, after that, the parameters of these related variables are learned by the multivariate auto regressive method. The calculation of L-lysine fed-batch fermentation process demonstrates the effectiveness of the proposed approximate model method.
机译:FED分批发酵过程是一种有效的生产方法。由于各种饲养的流和不同喂养批次的操作条件,通常难以制定用于工业喂养批量发酵过程的基于动力学的常微分方程模型。另一方面,在发酵过程中存在大量的历史数据。在本文中,我们首先应用了图形建模方法来模拟美联储批量发酵过程。在这种提出的方​​法中,记录中的缺失数据被避逸,然后,变量之间的相关性由低阶条件独立方法确定,之后,通过多变量自动回归方法学习这些相关变量的参数。 L-赖氨酸补料批量发酵方法的计算证明了所提出的近似模型方法的有效性。

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