For predicting the value of the quality variable in fermentation processes, traditional data-driven methods do not use information in large amounts of unlabelled data. To solve this data-rich but information-poor (DRIP) problem, a teacher student stacked sparse recurrent autoencoder (TS-SSRAE) model is proposed. Compared with traditional data-driven methods, the proposed method has three main advantages. First, an autoencoder is an unsupervised method which can effectively extract rich information in unlabelled data. The proposed stacked recurrent autoencoder (SRAE) with long short-term memory (LSTM) recurrent neural unit is superior to traditional autoencoders when extracting the dynamic correlation information in the fermentation process. Second, sparse constraints can make it much easier for hidden neurons to obtain useful information in a single moment. Finally, the LSTM recurrent neural unit is complex and the inputs of a SRAE must be a sequence, which increases the complexity of the model to a certain extent. So, the knowledge distillation is employed to simplify the model and reduce the computing time. In order to demonstrate its effectiveness, the proposed method is applied to the penicillin fermentation process for a simulation experiment and Escherichia coli production of interleukin-2. The results show that the proposed method based on TS-SSRAE can have better performance than conventional methods.

中文翻译：

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使用教师学生堆叠稀疏循环自编码器的发酵过程质量预测

为了预测发酵过程中质量变量的值，传统的数据驱动方法不使用大量未标记数据中的信息。为了解决这个数据丰富但信息贫乏（DRIP）的问题，提出了一种教师学生堆叠稀疏循环自动编码器（TS-SSRAE）模型。与传统的数据驱动方法相比，该方法具有三个主要优点。首先，自动编码器是一种无监督方法，可以有效地提取未标记数据中的丰富信息。在提取发酵过程中的动态相关信息时，所提出的具有长短期记忆（LSTM）循环神经单元的堆叠循环自动编码器（SRAE）优于传统的自动编码器。第二，稀疏约束可以使隐藏的神经元更容易在单个时刻获得有用的信息。最后，LSTM 循环神经单元比较复杂，一个 SRAE 的输入必须是一个序列，这在一定程度上增加了模型的复杂度。因此，采用知识蒸馏来简化模型并减少计算时间。为了证明其有效性，将所提出的方法应用于青霉素发酵过程进行模拟实验和大肠杆菌产生白细胞介素-2。结果表明，所提出的基于TS-SSRAE的方法比传统方法具有更好的性能。