Abstract Soft sensors have been extensively used to predict the difficult-to-measure key quality variables. The robust soft sensors should be able to sufficiently extract the local dynamic and nonlinear features of process data for accurate prediction. Convolutional neural network (CNN) has shown powerful performance in local feature representation that is suitable for soft sensor modeling. However, the process variables that have a distant topological structure usually cannot be covered within the same convolution kernel when applying CNN to process data, which results in the fact that local correlations of those distant process variables are not captured. Therefore, a new multichannel CNN (MCNN) is proposed for various local dynamic feature representation. As a key step, a multichannel 3-D tensor is augmented for each sample as the input to the MCNN model. For the 3-D tensor, each channel has specific local correlations of certain variables, while the variables have different neighborhood relationships for different channels, which refer the various local correlations of different combination variables. Combining with the time axis of each channel, the various local dynamic correlations of different variable combinations can be learnt using MCNN regardless of their distance. The feasibility and effectiveness of MCNN-based soft sensor are demonstrated on the industrial debutanizer column and hydrocracking process.

中文翻译：

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基于多通道卷积神经网络的动态过程软传感器模型

摘要 软传感器已被广泛用于预测难以测量的关键质量变量。稳健的软传感器应该能够充分提取过程数据的局部动态和非线性特征，以进行准确预测。卷积神经网络 (CNN) 在适合软传感器建模的局部特征表示方面表现出强大的性能。然而，在应用CNN处理数据时，具有远距离拓扑结构的过程变量通常无法覆盖在同一个卷积核内，这导致这些远距离过程变量的局部相关性没有被捕获。因此，针对各种局部动态特征表示提出了一种新的多通道 CNN（MCNN）。作为关键步骤，为每个样本增加一个多通道 3-D 张量作为 MCNN 模型的输入。对于3维张量，每个通道都有特定变量的特定局部相关性，而不同通道的变量具有不同的邻域关系，指的是不同组合变量的各种局部相关性。结合每个通道的时间轴，无论距离如何，都可以使用MCNN学习不同变量组合的各种局部动态相关性。在工业脱丁烷塔和加氢裂化过程中证明了基于 MCNN 的软传感器的可行性和有效性。它指的是不同组合变量的各种局部相关性。结合每个通道的时间轴，无论距离如何，都可以使用MCNN学习不同变量组合的各种局部动态相关性。在工业脱丁烷塔和加氢裂化过程中证明了基于 MCNN 的软传感器的可行性和有效性。它指的是不同组合变量的各种局部相关性。结合每个通道的时间轴，无论距离如何，都可以使用MCNN学习不同变量组合的各种局部动态相关性。在工业脱丁烷塔和加氢裂化过程中证明了基于 MCNN 的软传感器的可行性和有效性。