动态MPCA在发酵过程监测与故障诊断中的应用

针对发酵过程非线性和时变特点,提出了一种具有实时性的动态MPCA方法,采用多模型非线性结构代替传统MPCA单模型线性化结构,克服了后者不能处理非线性过程和实时性的问题,并避免了MPCA在线应用时预报未来测量值带来的误差,提高了发酵过程性能监测和故障诊断的准确性。对头孢菌素C发酵过程的拟在线仿真研究,验证了基于动态MPCA的统计过程监测的有效性。

Fermentation Process Monitoring and Fault Detection Based on Dynamic MPCA

A dynamic multiway principle component analysis for on-line batch process monitoring and fault detection was proposed. It integrates the time-lagged windows of process dynamic behavior with the multiway principle component analysis (MPCA). Using multi-model instead of single model, the dynamic MPCA approach emphasizes particularly on-line process performance monitoring and fault detecting. On-line process monitoring of cephalosporin C fermentation was studied, the results demonstrate that the dynamic MPCA method is able to efficiently monitor performance of the fermentation process and exactly detect faults which results in extraordinary behavior of processes.