Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network. (1st February 2021) - Yang, Shan-Shan; Yu, Xin-Lei; Ding, Meng-Qi; He, Lei; Cao, Guang-Li; Zhao, Lei; Tao, Yu; Pang, Ji-Wei; Bai, Shun-Wen; Ding, Jie; Ren, Nan-Qi
Highlights: Optimal ALK/ULS parameters were determined under different sludge concentration. Combined AO+ALK/ULS system was developed for treating wastewater at low C/N ratios. Efficient BNR and zero sludge production were achieved in the AO+ALK/ULS system. A multi-layered BPANN was developed and verified significant performance with high R 2 . RSLR of AO+ALK/ULS was real-time modeled in a feasible and quicker approach by BPANN. Abstract: In this study, a combined alkaline (ALK) and ultrasonication (ULS) sludge lysis-cryptic pretreatment and anoxic/oxic (AO) system (AO + ALK/ULS) was developed to enhance biological nitrogen removal (BNR) in domestic wastewater with a low carbon/nitrogen (C/N) ratio. A real-time control strategy for the AO + ALK/ULS system was designed to optimize the sludge lysate return ratio ( RSLR ) under variable sludge concentrations and variations in the influent C/N (⩽ 5). A multi-layered backpropagation artificial neural network (BPANN) model with network topology of 1 input layer, 3 hidden layers, and 1 output layer, using the Levenberg–Marquardt algorithm, was developed and validated. Experimental and predicted data showed significant concurrence, verified with a high regression coefficient ( R 2  = 0.9513) and accuracy of the BPANN. The BPANN model effectively captured the complex nonlinear relationships between the related input variables and effluent output in the combined lysis-cryptic + BNR system. The model could be used to support the real-time dynamic response and process optimization control to treat low C/N domestic wastewater. Graphical abstract: Image, graphical abstract