Enhancing Sustainable and Energy-Efficient Anaerobic Digestion of Animal Manure: Effects of Intermittent Stirring and ANN-Based Methane Production Modeling

Abstract

Despite extensive research on anaerobic digestion (AD), the combined effects of intermittent stirring energy demand and substrate co-digestion on overall energy performance have remained insufficiently explored. This study evaluated the effects of intermittent stirring duration and the AD performance of cattle dung (CD), poultry droppings (PD), rabbit droppings (RD), and their mixtures on methane production and net energy recovery.  Experiments were conducted in 30 L laboratory-scale mechanically stirred batch anaerobic digesters operated under mesophilic conditions and intermittent stirring durations of 1, 2, and 3 h/day. Co-digestion clearly outperformed mono-digestion, achieving higher methane yields (up to 223 L/kg VS), improved biodegradability (>90%), methane-rich biogas, and shorter digestion times (18 days). Intermittent stirring duration significantly influenced performance; moderate stirring (2 h/day) was optimal for most binary mixtures, while higher stirring (3 h/day) enhanced methane yield in balanced ternary systems. Optimized co-digestion could reach up to 7.96 MJ/kg VS despite increased mixing energy use. The statistical analyses revealed strong interrelationships among process variables, identifying stirring duration and digestion time as key drivers of methane yield and net energy production. Artificial neural network (ANN) modeling successfully predicted methane production and net energy based on C/N ratio, stirring rate, and waste composition. The optimal ANN showed high accuracy (R=0.99).