Nitrogen removal in a sequencing batch biofilm reactor with liquid circulation: Effect of feed strategy and carbon source in the denitrifying step

Ammonium nitrogen removal from a synthetic wastewater by nitrification and denitrification processes were performed in a sequencing batch biofilm reactor containing immobilized biomass on polyurethane foam with circulation of the liquid-phase. It was analyzed the effect of four external carbon sources (ethanol, acetate, carbon synthetic medium and methanol) acting as electron donors in the denitrifying process. The experiments were conducted with intermittent aeration and operated at 30+/-1 degrees C in 8-h cycles. The synthetic wastewater (100 mgCOD/L and 50 mgNH(4)(+)-N/L) was added batch-wise, while the external carbon sources were added fed-batch-wise during the periods where aeration was suspended. Ammonium nitrogen removal efficiencies obtained were 95.7, 94.3 and 97.5% for ethanol, acetate and carbon synthetic medium, respectively. As to nitrite, nitrate and ammonium nitrogen effluent concentrations, the results obtained were, respectively: 0.1, 5.7 and 1.4 mg/L for ethanol; 0.2, 4.1 and 1.8 mg/L for acetate and 0.2, 6.7 and 0.8 for carbon synthetic medium. On the other hand using methanol, even at low concentrations (50% of the stoichiometric value calculated for complete denitrification), resulted in increasing accumulation of nitrate and ammonium nitrogen in the effluent over time.

HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections

Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.