Modeling of extracellular polymeric substances production at different carbon/nitrogen ratio and solid retention time by artificial neural network

Abstract

Aims: The ability of extracellular polymeric substances (EPS) production was observed in many species of heterotrophic microorganisms through the biological wastewater treatment systems. Materials and Methods: The batch experiments at different carbon/nitrogen and solid retention time (SRT) were carried out to investigate the effects of initial nitrogen concentration and SRT on EPS production and chemical oxygen demand (COD) removal efficiency. The artificial neural network (ANN) was developed to modeling of obtained data. Results: The results showed that: (i) with increasing SRT, the COD removal improves; (ii) initially, the amount of carbohydrate increases as SRT increases; however, with further increase of SRT, it declines; (iii) the protein/carbohydrate ratio improves as SRT decreases; (iv) the carbohydrate and protein concentration of soluble EPS increased with increasing initial nitrogen concentration from 0 to 10 mg/L; and (v) further increase of initial nitrogen concentration lead to depletion of carbohydrate production. Conclusion: The highest yield (Y) value was calculated at low sludge age and deficient initial nitrogen concentration, which may be due to the application of EPS production mechanism. The ANN model moderately predicted effluent COD concentration, carbohydrate, and protein production. © 2018 International Journal of Environmental Health Engineering | Published by Wolters Kluwer - Medknow