文章为考察种子培养过程中葡萄糖流加对多黏菌素E生物合成代谢的影响,对发酵过程中的DO、CER、OUR、RQ和活菌细胞量等重要生理参数实施了在线检测,并对黏菌素效价E2和E1进行了分析。研究显示,种子发酵过程中流加葡萄糖过多,与CER和E2/E...文章为考察种子培养过程中葡萄糖流加对多黏菌素E生物合成代谢的影响,对发酵过程中的DO、CER、OUR、RQ和活菌细胞量等重要生理参数实施了在线检测,并对黏菌素效价E2和E1进行了分析。研究显示,种子发酵过程中流加葡萄糖过多,与CER和E2/E1均呈正相关,碳阻遏效应显著;通过流加液体葡萄糖,可使糖点在0.03~0.06 g/100 m L,CER为44.1 mmol/(L·h);当菌体处于最佳产素期,且RQ在1.0~1.2时,移种进入发酵培养,效价涨幅较高,发酵培养黏菌素效价可提高到75万U/mL,提升7.14%。展开更多
Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased ...Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased inferences about causal relationships, thus hindering the accurate identification of root cause variables. This study proposed a shapelet-based state evolution graph for fault root cause diagnosis (SEG-RCD), which enables causal inference through the analysis of the important local features. First, the regularized autoencoder and fault contribution plot are used to identify the fault onset time and candidate root cause variables, respectively. Then, the most representative shapelets were extracted to construct a state evolution graph. Finally, the propagation path was extracted based on fault unit shapelets to pinpoint the fault root cause variable. The SEG-RCD can reduce the interference of noncausal information, enhancing the accuracy and interpretability of fault root cause diagnosis. The superiority of the proposed SEG-RCD was verified through experiments on a simulated penicillin fermentation process and an actual one.展开更多
文摘文章为考察种子培养过程中葡萄糖流加对多黏菌素E生物合成代谢的影响,对发酵过程中的DO、CER、OUR、RQ和活菌细胞量等重要生理参数实施了在线检测,并对黏菌素效价E2和E1进行了分析。研究显示,种子发酵过程中流加葡萄糖过多,与CER和E2/E1均呈正相关,碳阻遏效应显著;通过流加液体葡萄糖,可使糖点在0.03~0.06 g/100 m L,CER为44.1 mmol/(L·h);当菌体处于最佳产素期,且RQ在1.0~1.2时,移种进入发酵培养,效价涨幅较高,发酵培养黏菌素效价可提高到75万U/mL,提升7.14%。
基金support from the following foundations:the National Natural Science Foundation of China(62322309,62433004)Shanghai Science and Technology Innovation Action Plan(23S41900500)Shanghai Pilot Program for Basic Research(22TQ1400100-16).
文摘Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased inferences about causal relationships, thus hindering the accurate identification of root cause variables. This study proposed a shapelet-based state evolution graph for fault root cause diagnosis (SEG-RCD), which enables causal inference through the analysis of the important local features. First, the regularized autoencoder and fault contribution plot are used to identify the fault onset time and candidate root cause variables, respectively. Then, the most representative shapelets were extracted to construct a state evolution graph. Finally, the propagation path was extracted based on fault unit shapelets to pinpoint the fault root cause variable. The SEG-RCD can reduce the interference of noncausal information, enhancing the accuracy and interpretability of fault root cause diagnosis. The superiority of the proposed SEG-RCD was verified through experiments on a simulated penicillin fermentation process and an actual one.
