Fault monitoring of fermentation process based on extended kernel entropy load matrix
GAO Xue-jin
YANG Yan-xia
WANG Pu
LI Xiao-li
CHANG Peng
QI Yong-sheng
Abstract：Hard classification for multistage fermentation process and cause of the defects of false alarm and alarm failure, in order to effectively reduce the omission and the rate of false positives, this paper proposes a strategy based on extended nuclear entropy load matrix. First, the three-mention training data array of fermentation process is unfolded in batch ways, resulting in two-dimension forms. Then, kernel entropy component analysis (KECA) was done for each time slice matrix to obtain its load matrix. After that, time slice matrix was added to the nuclear load matrix of entropy, and the change of the nuclear load matrix of entropy was utilized to describe the changes of batch processes.The KECA monitoring model was established at each stage of the division after the stage of nuclear load matrix of entropy was determined by FCM algorithm. At last, the effectiveness and utility of the proposed method were validated through the simulation of fed-batch penicillin and E. coli production of interleukin-2. Results showed, the proposed method could not only divide the stage and reduce the false alarm precisely, but also detect the production difficulty more advance.
Keywords：process monitoringprincipal component analysismultistagefermentation process
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 813-821 )