TECHNOLOGICAL JUSTIFICATION OF OPERATING PARAMETERS OF A SEQUENCING BATCH REACTOR WITH EXPANDED POLYSTYRENE FLOATING CARRIERS FOR DAIRY WASTEWATER TREATMENT

Abstract

The paper presents the results of a numerical investigation of the design parameters of a sequencing batch reactor (SBR) with expanded polystyrene (ePS) floating carriers for dairy wastewater treatment. Dairy-plant wastewater is a highly-concentrated, carbon-rich flow (ratio of chemical oxygen demand to total nitrogen COD/N_tot ≈ 90), so denitrification in it is not carbon-limited; the bottleneck of the classical spatially-separated denitrification–nitrification scheme is its energy intensity — the need for a separate anoxic reactor, nitrate recirculation and a high dissolved-oxygen level. The goal of the work is to substantiate the operating ranges of the design parameters and to assess the economic feasibility of the proposed solution. The methodology is based on the ASM3_2N mathematical model with a lumped biofilm description (Wanner et al.), implemented in Python (NumPy, SciPy) and calibrated on laboratory data. Series of numerical experiments were carried out to evaluate the influence of the dissolved-oxygen concentration (DO), the volumetric fraction of the floating carrier (F) and the S_S/NH₄⁺-N ratio on the total-nitrogen removal efficiency. At the reference conditions (DO = 1.0 mg O₂/L; F = 30 % v/v) TN removal reaches 86.5 % for the carrier system against 76.0 % for the control reactor; the dependence on DO is non-monotonic, with an optimum at 1.0 mg/L (operating window 0.8–1.2). A two-dimensional operating window (DO × F) was constructed with efficiency levels of 70, 80 and 85 %. Design recommendations are formulated: operating DO 0.8–1.2 mg O₂/L; F = 25–35 %; minimum required S_S/NH₄⁺-N ≥ 5. A techno-economic assessment for a 300 m³/day treatment facility, using DP «Ruzhin-Moloko» (Zhytomyr region) as an example, shows a reduction of capital costs of about 15 %, of operating costs of about 32 %, and of the specific aeration energy of 37.5 %. The obtained estimates are the result of model-based prediction consistent with a laboratory experiment and require further industrial verification.