摘要
Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.
差错监视 bioprocess 是重要的保证一个反应堆的安全并且维持产品的高质量。为 bioprocess 造一个精确机械学的模型是困难的，因此差错基于富有的历史或联机的数据库监视是一个有效方法。一组数据基于自举方法能随机地是采样，改善归纳能力当模特儿。在这份报纸，在网上指责与结合的概括添加剂模型(鲸鱼群) 监视自举为 glutamate 被建议发酵过程。鲸鱼群并且自举首先被用来从 glutamate 发酵实验基于联机、离线的正常取样的数据决定信心间隔。然后，鲸鱼群被用来在网上指责在时间，溶解的氧，氧举起率，和二氧化碳进化监视率。方法能在网上提供精确差错警报并且是有用的为在发酵移开差错和反常现象提供有用信息。
基金
Supported by the National Natural Science Foundation of China （61273131）
111 Project （B12018）
the Innovation Project of Graduate in Jiangsu Province （CXZZ12_0741）
the Fundamental Research Funds for the Central Universities （JUDCF12034）
