Derivation of Optimal Operation Factors of Anaerobic Digesters through Artificial Neural Network Technology

The anaerobic digestion of sewage sludge in South Korean wastewater treatment plants is affected by seasonal factors and other influences, resulting lower efficiency gas production, which cannot reach optimal yields. aim this study was to improve the production a plant (WWTP) using data mining techniques adjust operational parameters. Through experimental obtained from WWTP Daegu City, Korea, an artificial neural network (ANN) technology used range organic loading rate (OLR) hydraulic retention (HRT) methane digestion. Data sources were normalized, analysis including Pearson correlation analysis, multiple regression for results. results showed predicted 0.5% increase 1.3% at loads 1.26–1.46 kg/m3 day HRT 26–30 days. This shows that ANN model we established feasible can be