Anaerobic digestion is associated with various crucial variables, such as biogas yield, chemical oxygen demand, and volatile fatty acid concentration. Real-time monitoring of these variables can not only reflect the process of anaerobic digestion directly but also accelerate the efficiency of resource conversion and improve the stability of the reaction process. However, the current real-time monitoring equipment on the market cannot be widely used in the industrial production process due to its defects such as expensive equipment, low accuracy, and lagging analysis. Therefore, it is essential to conduct soft sensor modeling for unmeasurable variables and use auxiliary variables to realize real-time monitoring, optimization, and control of the an-aerobic digestion process. In this paper, the basic principle and process flow of anaerobic digestion are first briefly introduced. Subsequently, the development history of the traditional soft sensor is systematically reviewed, the latest development of soft sensors was detailed, and the obstacles of the soft sensor in the industrial production process are discussed. Finally, the future development trend of deep learning in soft sensors is deeply discussed, and future research directions are provided.

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 can not 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.