ABSTRACT
In this study, the stability of the total nitrogen removal efficiency (TNRE) was modeled using an artificial neural network (ANN)-based binary classification model for the anaerobic ammonium oxidation (AMX) process under saline conditions. The TNRE was stabilized to 80.2 ± 11.4% at the final phase under the salinity of 1.0 ± 0.02%. The results of terminal restriction fragment length polymorphism (T-RFLP) analysis showed the predominance of Candidatus Jettenia genus. Real-time quantitative PCR analysis revealed the average abundance of Ca. Jettenia and Kuenenia spp. increased in 3.2 ± 5.4 × 108 and 2.0 ± 2.2 × 105 copies/mL, respectively. The prediction accuracy using operational parameters with data augmentation was 88.2%. However, integration with T-RFLP and real-time qPCR signals improved the prediction accuracy by 97.1%. This study revealed the feasible application of machine learning and biomolecular signals to the stability prediction of the AMX process under increased salinity.
