文献类型：期刊文献

英文题名：Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals

作者：Sun, Yifei[1];Yan, Xuefeng[1];Jiang, Qingchao[1];Wang, Guan[2];Zhuang, Yingping[2];Wang, Xueting[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：46

期号：11

起止页码：1677

外文期刊名：BIOPROCESS AND BIOSYSTEMS ENGINEERING

收录：;EI(收录号：20234314966928);WOS:【SCI-EXPANDED(收录号:WOS:001088869800002)】；

基金：This work was supported by National key research and development program of China (2021YFC2101100), and National Natural Science Foundation of China (21878081).

语种：英文

外文关键词：Multiple feature fusion; Unequal sampling intervals; Time series prediction; Transformer; Penicillin fermentation process

摘要：The quality prediction of batch processes is an important task in the field of biological fermentation. However, dynamic nonlinearity, unequal sampling intervals, uneven duration, and multiple features of a batch process make this task challenging. Thus, the multiple-feature fusion transformer (MFFT) model is proposed for the time series quality prediction of a batch process. First, the application of sequence-to-sequence architecture enables MFFT to perform a wide range of sequence prediction tasks. Second, the transformer parallel operation model imposes no rigid requirement for the order of sequence input, allowing the model to deal with problems of unequal interval sampling and utilize the sequence information. Third, MFFT integrates a pretrained ResNet50 as a mycelium status classifier for fusing image information into the features. Moreover, a multiple-feature encoding structure is proposed to integrate sampling time and mycelium status. Finally, multiple tasks in penicillin fermentation have shown that MFFT significantly outperforms existing methods for time series prediction.