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Conventional biological nitrogen removal (BNR) processes for mainstream municipal wastewater (MMW) treatment have high energy and chemical costs. Partial nitritation/anammox (PN/A) has the potential to reduce the carbon footprint of BNR; however, its implementation for MMW treatment has been limited by the low ammonium and high organic matter concentrations in MMW, which prevent suppression nitrite oxidizing bacteria (NOB) and heterotrophic denitrifiers. In this study, after organic carbon diversion, ammonium was separated from MMW in a novel bench-scale sequencing batch biofilm reactor (SBBR) containing chabazite, a natural zeolite mineral with a high ammonium ion exchange (IX) capacity. After breakthrough, chabazite was bioregenerated by PN/A biofilms. Recirculation was applied from the bottom to the top of the column to create an aerobic zone (top) for ammonia-oxidizing microorganisms (AOM) and an anoxic zone (bottom) for anammox bacteria. Rapid IX-PN/A SBBR startup was observed after inoculation with PN/A enrichments. The time required for bioregeneration decreased with increasing recirculation rate, with high total inorganic nitrogen (TIN) removal efficiency (81 %) and ammonium removal rate (0.11 g N/L/day) achieved at recirculation velocity of 1.43 m/h. The core microbiome of the IX-PN/A SBBR contained a high abundance of bacteria of the phylum Pseudomonadota (15.27–20.62 %), Patescibacteria (12.38–20.05 %), Chloroflexota (9.36–14.23 %), and Planctomycetota (7.55–12.82 %), while quantitative PCR showed the highest ammonia monooxygenase (amoA, 2.0 × 102) and anammox copy numbers (amx, 1.0 × 104) in the top layers. The single-stage IX-PN/A SBBR achieved stable BNR for >two years without chemical inputs, media replacement or brine waste production. 

Deep learning (DL) system research is often impeded by the limited availability and expensive costs of GPUs. In this paper, we introduce GPEmu, a GPU emulator for faster and cheaper prototyping and evaluation of deep learning system research without using real GPUs. GPEmu comes with four novel features: time emulation, memory emulation, distributed system support, and sharing support. We support over 30 DL models and 6 GPU models, the largest scale to date. We demonstrate the power of GPEmu by successfully reproducing the main results of nine recent publications and easily prototyping three new micro-optimizations.