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首页> 外文期刊>Bioprocess and Biosystems Engineering >Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations
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Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations

机译:强大的人工智能工具,可自动启动重组大肠杆菌培养物中的补充培养基补料

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摘要

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO_2 molar fraction in the exhaust gas.
机译:分批补料发酵中最重要的事件之一是开始补料的时间的定义。本文提出了一种基于神经网络委员会(NNC)合理选择人工智能(AI)系统架构的方法,该方法可识别批处理阶段的结束。 AI系统已成功用于重组大肠杆菌的高细胞密度培养。 AI算法针对不同的系统进行了验证,表达了三种用于人类和动物疫苗的抗原:肺炎链球菌(PspA),进化枝1和3以及红斑丹毒菌(SpaA)的表面蛋白片段。具有单个隐藏层的标准前馈神经网络(NN)是NNC的基础。具有最佳性能的NN体系结构具有以下输入:搅拌器速度,进气和氧气流速,二氧化碳释放速率以及废气中的CO_2摩尔分数。

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