Abstract
The expansion of concentrated dairy operations has resulted in the production of nutrient-dense effluent, specifically anaerobically digested liquid dairy manure (ADLDM), which poses a significant environmental threat if not properly managed. Increased levels of nitrogen, phosphorus, and organic matter in ADLDM can lead to eutrophication and groundwater pollution. This dissertation investigates development, optimization, and modeling of advanced biological treatment systems employing sequencing batch reactors (SBRs) in order to promote the efficient and simultaneous removal of nitrogen (N), phosphorus (P), and chemical oxygen demand (COD) from ADLDM. The primary objective of this work is to provide sustainable and scalable treatment options for the dairy industry. To improve the simultaneous removal of TP, OP, NH₃-N, TN, and COD, a two-step fed SBR system was designed and optimized in the first phase. Three key operational parameters—anaerobic to aerobic time ratio, dissolved oxygen concentration, and hydraulic retention time (HRT)—were adjusted by the Taguchi approach and gray relational analysis. The system attained removal efficiencies over 88% for all targeted contaminants under optimal conditions (90:90 min anaerobic: aerobic cycle, DO of 0.4:2.4 mg L⁻¹, and 3-day HRT). Statistical study validated the significant effect of dissolved oxygen on total phosphorus and chemical oxygen demand removal, as well as the critical role of hydraulic retention time in nitrogen removal performance.
A unique intermittently-aerated extended-idle sequencing batch reactor (IA-EI SBR) technology was developed based on these results to mitigate low carbon-to-nutrient conditions frequently encountered in practical manure management. Process parameters such as cycle time, aeration strategy, feeding phases, and idle time were optimized by central composite design and response surface approach. The IA-EI SBR system achieved removal efficiencies of 95.82% for total phosphorus (TP), 82.64% for orthophosphate (OP), 92.92% for ammonia nitrogen (NH₃-N), 73.84% for total nitrogen (TN), and 90.94% for chemical oxygen demand (COD).
The final part of the research included predictive modeling that simulated the performance of the IA-EI SBR system. Four kinetic models—First-Order, Monod, Modified Stover-Kincannon, and Grau Second Order—were applied for describing substrate removal kinetics and biomass growth. Model parameters have been determined from experimental data and verified via simulation. The results showed a significant correlation between predicted and observed values, which confirmed the efficiency of these models in explaining system behavior and supporting future scale-up. Overall, This dissertation improves the conceptual basis of biological nutrient removal and provides practical insights to be utilized in real-world scenarios. The research combines experimental optimization with modeling methods to advance cost-effective, energy-efficient, and environmentally sustainable technologies for dairy wastewater treatment, thereby promoting sustainable nutrient management and circular resource utilization within the industry.