Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors.

The immediate response to the state disturbances of anaerobic digestion is essential to prevent anaerobic digestion failure. However, frequent monitoring of the state and performance of anaerobic digestion is challenging. Thus, deep learning models were investigated to predict the state and performance variables from online sensor data. The online sensor data, including pH, electric conductivity, and oxidation-reduction potential, were used as the input features to build deep learning models. The state and performance data measured offline were used as the labels. The model performance was compared for several deep learning models of convolutional neural network (CNN), long short-term memory (LSTM), dense layer, and their combinations. The combined model of CNN and bidirectional LSTM was robust and well-generalized in predicting the state and performance variables (R2 = 0.978, root mean square error = 0.031). The combined model is an excellent soft sensor for monitoring the state and performance of anaerobic digestion from electrochemical sensors.

Contribution analysis of different electron transfer pathways to methane production in anaerobic digestion coupled with bioelectrochemical system.

The contribution analysis of different electron transfer pathways to CH4 production was investigated in bioelectrochemical anaerobic digestion (BEAD). It demonstrates that the indirect interspecies electron transfer (IIET) pathway and the direct interspecies electron transfer (DIET) pathways contributed to 41.7 % and 58.3 % of the CH4 production in the BEAD reactor, respectively. The DIET pathway was further divided into DIET via electrode (eDIET) and biological DIET (bDIET) in the bulk solution, and contributed 11.1 % and 47.2 % of CH4 production, respectively. This indicates that the dominant electron transfer pathway for CH4 production is from the bulk solution, rather than on the polarized electrode. The electroactive microorganisms were well enriched in the bulk solution by the electric field generated between anode and cathode. The enriched electroactive microorganisms significantly improved the CH4 production in the bulk solution through the bDIET pathway.