In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troubleso...In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.展开更多
In the process of ultrafine particle classification,the separation curve,which reflects the characteristics of separating process,is frequently influenced by the characteristics of separation flow field and operating ...In the process of ultrafine particle classification,the separation curve,which reflects the characteristics of separating process,is frequently influenced by the characteristics of separation flow field and operating parameters,etc.This paper introduces the concept of system deviation and deduces the calculating method of the separation curves.Meanwhile,it analyses the influences of classification flow field's specific properties and some operating parameters on the separation curves.The results show that,in the process of ultrafine particle classification,the local vortex in the separation field improves the separation efficiency to a certain degree,but the accuracy will decrease;the coacervation action of particles will seriously influence the classification accuracy.展开更多
文摘In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.
文摘In the process of ultrafine particle classification,the separation curve,which reflects the characteristics of separating process,is frequently influenced by the characteristics of separation flow field and operating parameters,etc.This paper introduces the concept of system deviation and deduces the calculating method of the separation curves.Meanwhile,it analyses the influences of classification flow field's specific properties and some operating parameters on the separation curves.The results show that,in the process of ultrafine particle classification,the local vortex in the separation field improves the separation efficiency to a certain degree,but the accuracy will decrease;the coacervation action of particles will seriously influence the classification accuracy.
