Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling.,Biotechnology and Bioengineering

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Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling.
Biotechnology and Bioengineering ( IF 3.5 ) Pub Date : 2020-06-08 , DOI: 10.1002/bit.27454
Vincent Brunner ₁ , Manuel Siegl ₁ , Dominik Geier ₁ , Thomas Becker ₁

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A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data‐driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off‐gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.

中文翻译：

基于相位检测和混合建模的毕赤酵母分批补料过程的生物质软传感器。

使用酒精氧化酶 1 (AOX1) 启动子在毕赤酵母中生产重组蛋白的常见控制策略是将生物过程分为两个主要阶段：甘油生物质产生和通过甲醇诱导产生蛋白质。本研究报告建立了一种用于预测生物量浓度的软传感器，该传感器可自动适应这些不同的阶段。为此，使用了一种结合机械（碳平衡）和数据驱动建模（多元线性回归）的混合方法。使用多级相位检测算法根据当前过程阶段动态调整模型参数。该算法基于CO ₂的在线数据在废气（绝对值和一阶导数）和累积基础进料中。对模型的评估导致整个过程的平均相对预测误差为 5.52%，R ²为 0.96。由此产生的模型作为软传感器实现，用于在线监测毕赤酵母生物过程。软传感器可用于质量控制和过程控制系统的输入，例如甲醇控制。

更新日期：2020-08-14

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