Use of a two-step process to denitrification of synthetic brines: electroreduction in a dual-chamber cell and catalytic reduction.

Membrane separation processes are being currently applied to produce drinking water from water contaminated with nitrate. The overall process generates a brine with high nitrate/nitrite concentration that is usually send back to a conventional wastewater treatment plant. Catalytic processes to nitrate reduction are being studied, but the main goal of achieving a high selectivity to nitrogen production is still a matter of research. In this work, a two-step process was evaluated, aiming to verify the best combination of operational parameters to efficiently reduce nitrate to nitrogen. In the first step, the nitrate was reduced to nitrite by electroreduction, applying a copper electrode and different cell potentials. A second step of the process was carried out by reducing the generated nitrite with a catalytic process by hydrogenation. The results showed that the highest nitrate reduction (89%) occurred when a cell potential of 11 V was applied. In this condition, the nitrite ion was generated with all experimental conditions evaluated. Then, to reduce the nitrite ion formed by catalytic reduction, activated carbon fibers (ACF) and powder γ-alumina (γ-Al2O3) were tested as supports for palladium (Pd). With both catalysts, the total nitrite conversion was obtained, being the selectivity to gaseous compounds 94% and 97% for Pd/Al2O3 and Pd/ACF, respectively. Considering the results obtained, a two-stage treatment setup to brine denitrification may be proposed. With electrochemistry, an operating condition was achieved in which ammonium production can be controlled to very low values, but the reduction is predominant to nitrite. With the second step, all nitrite is converted to nitrogen gas and just 3% of ammonium is produced with the most selective catalyst. The main novelty of this work is associated to the use of a two-stage process enabling 89% of nitrate reduction and 100% of nitrite reduction.

Effect of operational parameters and Pd/In catalyst in the reduction of nitrate using copper electrode.

Water with high concentration of nitrate may cause damage to health and to the environment. This study investigated how concentration, current density, flow, pH, the use of Pd/In catalyst and operating mode (constant current density and constant cell potential) have an influence in the electrochemical reduction of nitrate and in the formation of gaseous compounds using copper electrode. Experiments were performed in two-compartment electrolytic cells separated by a cationic membrane with nitrate model solutions prepared as a surrogate of concentrated brines from membrane desalination plants. The results show that the electroreduction process has potential for reduction of nitrate and that it is influenced by the operational conditions. The best conditions found for the treatment - with satisfactory reduction of nitrate, formation of gaseous compounds and reproducibility - were at nitrate concentrations of 600 and 1000 mg L-1, current density of 1.1 mA cm-2 and without pH control, since in these conditions the production of gaseous compounds is higher than the production of nitrite. When Pd/In catalyst was used, the nitrate reduction was 50% after 6 h of experiment and the predominant product were gaseous compounds. When compared to the experiment without the catalyst, the arrangement with Pd/In was the most efficient one.

Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources--canola, cotton, corn, palm, sunflower and soybeans--were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.