Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors

In this study, data-driven deep learning methods were applied in order to model and predict the treatment of real municipal wastewater using anaerobic membrane bioreactors (AnMBRs). Based on the one-year operating data of two AnMBRs, six parameters related to the experimental conditions (temperature of reactor, temperature of environment, temperature of influent, influent pH, influent COD, and flux) and eight parameters for wastewater treatment evaluation (effluent pH, effluent COD, COD removal efficiency, biogas composition (CH4, N2, and CO2), biogas production rate, and oxidation-reduction potential) were selected to establish the data sets. Three deep learning network structures were proposed to analyze and reproduce the relationship between the input parameters and output evaluation parameters. The statistical analysis showed that deep learning closely agrees with the AnMBR experimental results. The prediction accuracy rate of the proposed densely connected convolutional network (DenseNet) can reach up to 97.44%, and the single calculation time can be reduced to within 1 s, suggesting the high performance of AnMBR treatment prediction with deep learning methods. [Display omitted] •Deep learning was applied to predict the treatment of municipal wastewater by AnMBRs.•Multiple deep learning network structures were proposed, optimized, and compared.•The prediction accuracy of the deep learning methods could reach up to 97.44%.•Multiple parameters for evaluating AnMBR treatment were collected and analyzed.•The deep learning approach agreed with the AnMBR experimental results.