Dynamic modeling of ammonia inhibition and microbial adaptation in the high-rate continuous anaerobic digestion of waste activated sludge

This study develops and validates a dynamic modeling framework to improve the prediction of ammonia inhibition and microbial adaptation dynamics in anaerobic digestion (AD) systems. A lab-scale continuous anaerobic digester was operated for 318 days, treating waste activated sludge (WAS) at organic loading rates (OLRs) of 1.54–8.13 g COD/L/d. Methane production declined as ammonia concentrations increased, prompting temporary suspension of feedstock injection to facilitate microbial adaptation. Despite sustained high ammonia levels, the digester recovered and maintained stable performance at higher OLRs. To capture the complex temporal behavior of methane production under ammonia stress, time-series machine learning models, including Long Short-Term Memory (LSTM) and Nonlinear AutoRegressive with eXogenous inputs (NARX), were applied. An adaptation factor (α) was incorporated into the NARX (NARX-α) model to explicitly quantify microbial resilience. Comparative performance evaluation showed that the proposed models outperformed the conventional ADM1, with NARX-α model achieving the highest predictive accuracy (R 2 = 0.70) and effectively capturing microbial adaptation dynamics. Microbial community analysis supported the modeling results, showing that recovery of methane production coincided with a shift toward ammonia-tolerant methanogens, particularly increased relative abundances of Methanosarcina and Methanoculleus . These findings demonstrate the potential of adaptive, data-driven modeling to enhance process stability and optimization in ammonia-stressed AD systems.