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Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on DSTHELM

Kitchen waste is both a pollutant and an available resource. The anaerobic digestion (AD) technology can effectively treat waste and generate biogas. The concentration of volatile fatty acids (VFA) is one of the essential quality monitoring indicators in the anaerobic digestion process of kitchen waste and is difficult to measure online. In this paper, the hierarchical extreme learning machine (HELM) is applied to the soft sensor for the concentration of VFA considering the shortcomings of traditional soft sensor. The combined feature information is extracted by the multi-layer extreme learning machine-autoencoder (ELM-AE) structure in the HELM. Besides, the last layer is the combined manifold regularization extreme learning machine (ELM) and used to predict and mine the data structure of labeled data and unlabeled data. Considering that the characteristics of system operating conditions are prone to change in the actual production process of anaerobic digestion, the domain space transfer hierarchical extreme learning machine (DSTHELM) is used to solve the data drift problem caused by different initial conditions in the actual data; this is different from the traditional hierarchical extreme learning machine. This method increases the accuracy by 14.31% and 9.67% in training set and test set compared with HELM, indicating that the model possesses higher prediction accuracy and better generalization performance.