Seed Data Analysis for Production Fermenter Performance Estimation

Ignova, M.; Glassey, J.; Ward, A.C.; Montague, G.A.; Irvine, T.S.

doi:10.1016/b978-0-08-042377-7.50013-1

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Thus p corresponds to the total sampling time of the fermentation. The dimension of the mapping space, m , is set to 2 because the total variance explained by the first and second principal components was higher than 95% (Ignova et al, 1995; Shimizu et al, 1997).…”

Section: Methodsmentioning

confidence: 99%

Bioprocess Fault Detection by Nonlinear Multivariate Analysis: Application of an Artificial Autoassociative Neural Network and Wavelet Filter Bank

et al. 1998

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A nonlinear multivariate analysis, artificial autoassociative neural network (AANN), was applied to bioprocess fault detection. In an optimal production process of a recombinant yeast with a temperature controllable expression system, faults in test cases with faulty temperature sensors and plasmid instability of recombinant cells could be detected by the AANN. Since the raw data of measured variables included high-frequency noise, a wavelet filter bank (WFB) was applied to noise elimination before training of the AANN. The filtering performance of the WFB was compared with those of some classical first-order digital filters. The filtered signals at several resolution scales by the WFB were employed as the training data of the AANN. The computing time and summation of square of errors in training were compared, and the appropriate degree of the noise filtering and the density of the training data of the AANN were discussed. The performance of the feature capturing by the AANN was compared with that by a linear multivariate analysis, principal component analysis. A J index defined in this paper, using inputs and outputs of the AANN, was used for fault detection successfully. The output of the first unit of the trained AANN functioned effectively for the discrimination of the data in the abnormal cases from the data in the normal cases.

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