Artificial Neural Networks for Predicting Methane Content in Biogas from Livestock Waste

Introduction: Anaerobic digestion of livestock waste represents a sustainable alternative for energy generation, with methane serving as the main energy carrier. However, the experimental quantification of the biochemical methane potential (BMP) is time-consuming and costly. Objective: This study aimed to develop an Artificial Neural Network (ANN) model to estimate the methane content in biogas from livestock residues, using the substrates' elemental composition (C, H, and N) as input variables. Theoretical Framework: ANNs are applied to model nonlinear relations between livestock waste composition and methane yield, overcoming the limits of costly BMP assays. Method: Three ANN architectures were tested, with the 3-7-7-1 structure showing the best performance. The models were evaluated using statistical metrics (R² and RMSE) and a physical plausibility filter to exclude meaningless predictions. Results and Discussion: The network accurately predicted methane content (error <10%) in 66.67% of validated samples, with no valid prediction exceeding 17% error. Dataset limitations and waste heterogeneity caused some instability, although all three ANN configurations showed similar trends. Research Implications: The application of ANN proves promising for estimating methane content, delivering satisfactory performance despite experimental limitations. When combined with physical filters and critical analysis, ANNs can complement laboratory methods and provide a rapid tool for biogas assessment. Originality/Value: This study pioneers methane content prediction with ANN and a plausibility filter, offering a rapid alternative to laboratory methods.