Animal wastewater treatment with an improved combined Anaerobic-Aerobic System: Towards energy Self-Sufficiency

A combined anaerobic–aerobic (CAO) system is a feasible animal wastewater treatment approach, and one of the prerequisites for its sustainable application is to realise energy self-sufficiency. Here, we built an energy balance model to calculate the probability of achieving energy self-sufficiency in three CAO systems with multiple uncertainties through Monte Carlo simulation. Upon comparison with the conventional combined anaerobic–aerobic (C-CAO) system and partial raw wastewater bypassed combined anaerobic–aerobic (PRB-CAO) system, the matters pre-captured combined anaerobic–aerobic (MPC-CAO) system displayed the highest probability (45.5%) and stability (with 10.9% coefficient of variation) in achieving energy self-sufficiency with the uniformly distributed input parameters. The uncontrollable factor of ambient temperature mainly contributed to the CAO system’s net energy production among all input parameters. However, operating with the optimal control parameters of anaerobic digestion temperature (approximately 20 °C for C-CAO and PRB-CAO systems, 25–35 °C for MPC-CAO system) and organic loading rate (OLR) (50% of simulated maximum OLR) reduced the ambient temperature effect, thereby increasing the probability of energy self-sufficiency. And increasing the organic capture efficiency further reduced the effect of ambient temperature on net energy production of the MPC-CAO system. In addition, the probability of achieving energy self-sufficiency in the MPC-CAO system was more than 80%, with 30–50% of nitrogen captured simultaneously with organic matter and the direct use of anaerobic digestate (e.g., as fertilizer). These findings indicate that capturing more matter and increasing the capture efficiency is highly conducive for achieving energy self-sufficiency in CAO systems. Meanwhile, Monte Carlo simulation efficiently locates the key factors determining the CAO system’s energy production performance from the different levels of uncertainty of multi-parameters, which is demonstrated as a time saving method for further system evaluation, design and upgrade. However, additional factors such as capital investment, social and environmental impacts should also be further considered for increasing the robustness of energy balance simulation model and providing a comprehensive assessment of CAO system for animal wastewater treatment.