基于自联想神经网络的毕赤酵母发酵过程两阶段故障诊断

Two-Stage Fault Diagnosis of Pichia Pastoris Fermentation Based on an Auto-Associative Neural Network

DOI：10.3969/j.issn.1673-1689.2012.06.006

中文关键词: 毕赤酵母 神经网络 故障诊断 甲醇浓度

英文关键词: pichia pastoris neural network fault diagnosis methanol concentration

基金项目:国家973计划项目(2007CB714303)

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中文摘要:

在毕赤酵母表达人血清白蛋白-人白介素-2融合蛋白(IL-2-HSA)过程中,诱导期甲醇浓度和pH值直接影响了IL-2-HSA表达量的高低和发酵过程的稳定。为了准确有效的控制这两个参数,本论文基于毕赤酵母诱导期的生理学特性和过程参数特征,提出了基于自联想神经网络的毕赤酵母表达IL-2-HSA过程的诱导期两阶段故障诊断。研究结果表明该诊断系统能够在线快速准确地诊断出毕赤酵母诱导期的各种故障。当系统提示出现故障时,离线分析,对比最优的pH值和甲醇质量浓度变化曲线,确定故障类型,采取相应措施。

英文摘要:

For the IL-2-HSA expression with Pichia pastoris,methanol concentration and pH during induction phase are two important parameters affecting heterologous protein production and should be strictly controlled at adequate levels.In this study,based on the effective recognition of physiological status and characteristics of parameters,an auto-associative neural network (AANN) model was used for two-stage fault diagnosis in Pichia pastoris fermentation processes.The optimized AANN could provide on-line and accurate fault alarm for Pichia pastoris induction stage.It was potentially helpful in supplying useful information for removing fault and recovering abnormal fermentation.When detecting methanol over-feeding,glycerol limited feeding could improve the cell activity and release the toxicity of methanol.

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