Artificial Neural Network‐Based Optimization of Anaerobic Digestion in Municipal Wastewater Treatment

Improving methane production is essential for enhancing the self‐sufficiency of wastewater treatment plants (WWTPs), with anaerobic digestion (AD) acting as the key sustainability driver by serving as an energy recovery mechanism. Artificial intelligence (AI) offers an effective way to manage the AD process without requiring a detailed understanding of its complex nature. This study explores the use of artificial neural networks (ANNs) to model digesters in the Konya WWTP (KWWTP) in Türkiye, aiming to optimize methane output and hydrogen sulfide levels. The ANN model utilizes 11 input parameters, while daily methane production and hydrogen sulfide concentration are designated as outputs. By employing sensitivity analysis, the study investigates how input variables influence the outputs. Additionally, a genetic algorithm (GA) is applied to find optimal input values that maximize methane production and minimize hydrogen sulfide levels. The model’s performance is assessed using mean‐square error (MSE) and correlation coefficient ( R ) metrics for both training and test datasets, achieving MSE values of 0.0048 and 0.0077, and correlation coefficients of 0.96 and 0.94, respectively. Sensitivity analysis reveals that the pH of thickened sludge is the most influential factor for both outputs, while sludge flow rate has the least effect on methane production, and volatile fatty acids in the digester minimally affect hydrogen sulfide levels. Furthermore, multiple optimal input configurations can achieve higher methane production, which could be increased by up to 27%, with a corresponding hydrogen sulfide concentration of 350 ppm.