title: Improvement and prediction of OSA system performance in sludge reduction through integration with thermal and mechanical treatment.
abstract: The oxic-settling-anoxic (OSA) process is one of the sludge production reduction methods in the activated sludge process. In this method, sludge is stored in an anaerobic tank within the sludge return line before entrance into an aeration tank. Due to this method's flexibility in application to operating treatment plants and not being energy-consuming, its application is developing. In this research, the improvement of the OSA process is investigated via thermal and mechanical treatment in a sequencing batch reactor (SBR). A pilot-scale reactor and domestic wastewater are used. Sludge was subjected to high temperature in an anaerobic tank using a heat transformer and it was subjected to mechanical shear through mechanical mixing in the anaerobic tank. Different temperatures and voltages were tested. The OSA process reduced sludge production by 24% while the chemical oxygen demand (COD) removal rate decreased from 90% to 86%. Thermal treatment combined with the OSA process caused a maximum of 46% sludge production reduction. However temperatures above 90 °C are not recommended due to a high level of decrease in COD removal. Mechanical mixing in combination with the OSA process led to 34% sludge production reduction. The effluent quality is not affected by the OSA process itself but is slightly reduced by thermal treatment and mechanical mixing. Therefore, for reaching the maximum sludge reduction in OSA plus thermal and mechanical treatment it would be necessary to evaluate the effect of different sets of parameters on effluent quality beside the sludge reduction. For this purpose multi-layer perceptron artificial neural network models are developed to predict the effluent total suspended solids and COD removal efficiency as well as sludge production rate. The models perform well and would be useful tools in determining the optimal set of system operation parameters.
authors.affiliation: School of Civil Engineering, College of Engineering, University of Tehran, Tehran Iran E-mail: snazif@ut.ac.ir.
authors.forename: Sara
authors.initials: S
authors.lastname: Nazif
authors.affiliation: School of Environment, University of Tehran, Tehran Iran.
authors.forename: Naser
authors.initials: N
authors.lastname: Mehrdadi
authors.affiliation: School of Civil Engineering, College of Engineering, University of Tehran, Tehran Iran E-mail: snazif@ut.ac.ir.
authors.forename: Sajad
authors.initials: S
authors.lastname: Zare
authors.affiliation: School of Civil Engineering, College of Engineering, University of Tehran, Tehran Iran E-mail: snazif@ut.ac.ir.
authors.forename: Sarvenaz
authors.initials: S
authors.lastname: Mosavari
mesh_terms: D000693:Anaerobiosis; D057919:Biological Oxygen Demand Analysis; D019149:Bioreactors; D006358:Hot Temperature; D008962:Models, Theoretical; D012722:Sewage; D014865:Waste Disposal, Fluid / Q000379:methods*; D062065:Wastewater / Q000737:chemistry*; D014874:Water Pollutants, Chemical
chemical_list: D012722:Sewage; D062065:Wastewater; D014874:Water Pollutants, Chemical