Convolutional Neural Network for Methane Prediction in Anaerobic Reactors of Domestic Sewage

Background Sustainable operation of wastewater treatment plants (WWTPs) increasingly depends on the ability to recover energy from byproducts such as biogas. Methane (CH₄) production in anaerobic reactors is highly sensitive to influent characteristics, yet real-time monitoring and control remain limited due to environmental and economic constraints. This study explores the use of Convolutional Neural Networks (CNNs) to predict methane flow in domestic wastewater treatment systems, aiming to support proactive process control, reduce greenhouse gas emissions, and enhance the energy efficiency of sanitation infrastructure. Results A CNN model was developed and trained using high-resolution operational data from a full-scale WWTP employing UASB reactors in Curitiba, Brazil. Input variables included sewage flow rate, chemical oxygen demand (COD), total suspended solids (TSS), and volatile suspended solids (VSS). The optimal architecture—comprising a single hidden layer with eight neurons and a sigmoid activation function—achieved a mean coefficient of determination (R²) of 0.93 and a low mean squared error across multiple training runs. The model effectively captured temporal dependencies in the data, supporting its suitability for time-series forecasting. A web-based interface was also implemented, enabling plant operators to input real-time data and receive immediate methane flow predictions, facilitating on-site decision-making and process optimization. Conclusions The proposed CNN-based approach offers a robust, low-cost solution for forecasting methane production in anaerobic wastewater treatment, with significant implications for energy recovery and environmental management. By enabling predictive and preemptive operational adjustments, the model contributes to improving biogas quality and minimizing CH₄ losses through flaring. The integration of artificial intelligence into WWTP operations represents a key advancement toward digital and sustainable sanitation, aligning with circular economy principles and global climate goals. Future work may explore model transferability across diverse climatic regions and the integration of hybrid AI architectures to further enhance predictive performance.