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Dynamic Soft Sensor for Anaerobic Digestion of Kitchen Waste Based on SGSTGAT

机译：基于SGSTGAT的厨房垃圾厌氧消化动态软传感器

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摘要

Anaerobic digestion technology is an effective way to solve the problem of urban kitchen waste. Volatile fatty acid (VFA) is an essential intermediate product in the anaerobic digestion process. Real-time monitoring of the concentration of VFA cannot only directly reflect the anaerobic digestion process and improve resource conversion efficiency but also effectively avoid the reactor operation failure caused by acidification. Traditional soft sensors of VFA cannot adapt to the dynamic characteristics of industrial process. In this paper, a dynamic soft sensor based on spatiotemporal graph convolution network is developed. The spatial and temporal information of the anaerobic digestion process is extracted through graph convolutional network (GCN) and gated recurrent unit (GRU) respectively. With the purpose of further improving the prediction accuracy and generalization ability of the model, the adaptive adjacency matrix and graph attention mechanism are introduced into the model to solve the over-smoothing problem of GCN, and the semi-supervised learning mechanism based on manifold regularization is introduced to fully mine data information of unlabeled samples. Then, the gated unit is used to realize the information fusion of different dimensional features and the selection of model depth. At last, a dynamic soft sensor based on the semi-supervised gated spatiotemporal graph attention network is established to estimate the concentration of VFA in real time. Compared with baseline models such as GCN and GRU, the root mean square error of this model is reduced by 17.34% and 15.54%, respectively.
机译：Anaerobic Digestion Technology是解决城市厨房垃圾问题的有效途径。挥发性脂肪酸（VFA）是厌氧消化过程中的必需中间产物。实时监测VFA的浓度不能直接反映厌氧消化过程，提高资源转换效率，而且还有效地避免酸化引起的反应器操作失败。 VFA的传统软传感器不能适应工业过程的动态特性。本文开发了一种基于时空图卷积网络的动态软传感器。厌氧消化过程的空间和时间信息分别通过图形卷积网络（GCN）和门控复发单元（GRU）提取。为了进一步提高模型的预测准确性和泛化能力，将自适应邻接矩阵和图注意机制引入模型中以解决GCN的过平滑问题，以及基于歧管正则化的半监控学习机制被引入到完全挖掘未标记样本的数据信息。然后，所属的单元用于实现不同尺寸特征的信息融合和模型深度的选择。最后，建立了一种基于半监督门控时滞的动态传感器注意网络，以实时估计VFA的浓度。与基线模型（如GCN和GRU）相比，该模型的根均方误差分别降低了17.34％和15.54％。

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