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The study presents the development of classification models for sedimentation of activated sludge using the artificial neural networks (ANN), logistic-regression (RL), and linear discrimination model (LDM). The input consisted of indicators of wastewater quantity and quality (biochemical oxygen demand, chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to the wastewater treatment plant) and operational characteristic of the bioreactor (pH, temperature of activated sludge, mixed liquor suspended solids, concentration of oxygen in the nitrification chamber, amount of PIX dosing).The prediction quality of the developed models was measured with: sensitivity, specificity, and computed errors. The calculations of the sedimentation were performed for sludge volume index (SVI). The results indicate that successful predictions were obtained using ANN, RL and LDM methods, which is supported by the fit of computations to measurement results. The study shows that for the wastewater treatment plant of concern, sedimentation properties can be obtained using only the loads of organic compounds, mixed liquor suspended solids, temperature, pH of activated sludge, concentration of oxygen in the nitrification chamber and amount of PIX dozing. Other analysed variables appear to be statistically insignificant for the sludge volume index

Application of the selected classification models to the analysis of the settling capacity of the activated sludge – case study