Zheng Jiaqi, Ma Lianwei, Wu Yi, Ye Lingjian, Shen Feifan
College of Science & Technology, Ningbo University, Ningbo 315300, People's Republic of China.
School of Information Science and Engineering, NingboTech University, Ningbo 315100, People's Republic of China.
ACS Omega. 2022 May 2;7(19):16653-16664. doi: 10.1021/acsomega.2c01108. eCollection 2022 May 17.
A soft sensor is a key component when a real-time measurement is unavailable for industrial processes. Recently, soft sensor models based on deep-learning techniques have been successfully applied to complex industrial processes with nonlinear and dynamic characteristics. However, the conventional deep-learning-based methods cannot guarantee that the quality-relevant features are included in the hidden states when the modeling samples are limited. To address this issue, a supervised hybrid network based on a dynamic convolutional neural network (CNN) and a long short-term memory (LSTM) network is designed by constructing multilayer dynamic CNN-LSTM with improved structures. In each time instant, data augmentation is implemented by dynamic expansion of the original samples. Moreover, multiple supervised hidden units are trained by adding quality variables as part of the layer input to acquire a better quality-related feature learning performance. The effectiveness of the proposed soft senor development is validated through two industrial applications, including a penicillin fermentation process and a debutanizer column.
当工业过程无法进行实时测量时，软传感器是关键组件。近年来，基于深度学习技术的软传感器模型已成功应用于具有非线性和动态特性的复杂工业过程。然而，当建模样本有限时，传统的基于深度学习的方法无法保证与质量相关的特征包含在隐藏状态中。为了解决这个问题，通过构建具有改进结构的多层动态卷积神经网络（CNN）和长短期记忆（LSTM）网络，设计了一种基于动态卷积神经网络和长短期记忆网络的有监督混合网络。在每个时刻，通过对原始样本进行动态扩展来实现数据增强。此外，通过添加质量变量作为层输入的一部分来训练多个有监督隐藏单元，以获得更好的与质量相关的特征学习性能。通过青霉素发酵过程和脱丁烷塔这两个工业应用验证了所提出的软传感器开发方法的有效性。
