Study of Methane Production Kinetics in Anaerobic Digesters Using the Monod Model and Neural Networks

This study, conducted in the Ecuadorian Andes, evaluated the anaerobic co-digestion of local crop residues (amaranth and quinoa) with llama, vicuña, and pig manure to analyze methane production kinetics. The raw materials were characterized by proximate, elemental, and structural analyses, and biogas volume and the CH4 fraction were monitored daily. The Amaranth-vicuña and Amaranth-llama treatments reached 77.29 ± 5.63 and 64.62 ± 3.62 mL biogas/g VS and 36.20 ± 7.29 and 31.78 ± 3.62 mL CH4/g VS, respectively; in contrast, Quinoa-vicuña and Quinoa-llama produced only 1.04 ± 0.25 and 0.24 ± 0.03 mL CH4/g VS. Monod-model parameters were estimated using an apparent formulation based on the methane production rate, and the kinetic behavior was compared with first-order, modified Gompertz, and modified logistic models. In addition, artificial neural networks (ANNs) were evaluated to predict the methane production curve from substrate characterization. Network 44, with a 13-15-10-1 architecture, yielded an overall R2 = 0.998, validation R2 = 0.997, and validation MSE = 0.415. Ten-times repeated five-fold cross-validation of the same architecture yielded R2 = 0.985 ± 0.007 and RMSE = 1.21 ± 0.28 mL CH4/g VS, supporting its interpolation capability within the experimental domain, although this does not demonstrate extrapolation to new substrate combinations. Overall, the proposed approach combines interpretable kinetic parameters with ANN-based prediction, but external validation with independent datasets is still required. The reported yields correspond to the specific production achieved in a low-cost batch system operated at room temperature and should not be interpreted as standardized biochemical methane potential (BMP) values.