MINIMIZATION OF PHENOL AND AMMONIACAL NITROGEN IN REFINERY WASTEWATER EMPLOYING BIOLOGICAL TREATMENT

Wastewater from refineries is usually characterized by presence of phenol and ammoniacal nitrogen. The aim of this work was to search for a refinery wastewater minimization employing a biological technology to reduce these pollutants. Experimental data was obtained by treating a synthetic wastewater in a sequencing batch reactor (SBR) operating on different operational schemes for batch scheduling. COD, nitrate, ammonia, phosphate and phenol were monitored as well as dissolved oxygen, pH, level and redox potential through a supervisory control software. Reductions of 95 % were achieved for different concentrations of NH4 + and phenol, providing an effluent acceptable by Brazilian environmental legislation

Preconcentration Procedure Using Cloud Point Extraction In The Presence Of Electrolyte For Cadmium Determination By Flame Atomic Absorption Spectrometry

This paper describes a micelle-mediated phase separation in the presence of electrolyte as a preconcentration method for cadmium determination by flame atomic absorption spectrometry (FAAS). Cadmium was complexed with ammonium O,O-diethyldithiophosphate (DDTP) in an acidic medium (0.32 mol l- 1 HCl) using Triton X-114 as surfactant and quantitatively extracted into a small volume (about 20 μl) of the surfactant-rich phase after centrifugation. The chemical variables that affect the cloud point extraction, such as complexing time (0-20 min), Triton X114 concentration (0.043-0.87% w/v) and complexing agent concentration (0.01-0.1 mol l- 1), were investigated. The cloud point is formed in the presence of NaCl at room temperature (25 °C), and the electrolyte concentration (0.5-5% w/v) was also investigated. Under optimized conditions, only 8 ml of sample was used in the presence of 0.043% w/v Triton X-114 and 1% (w/v) NaCl. This method permitted limits of detection and quantification of 0.9 μg l- 1 and 2.9 μg l- 1 Cd, respectively, and a linear calibration range from 3 to 400 μg l- 1 Cd. The proposed method was applied to Cd determination in physiological solutions (containing 0.9% (w/v) of NaCl), mineral water, lake water and cigarette samples (tobacco). © 2005 Elsevier B.V. All rights reserved.605743748Carabias-Martínez, R., Rodríguez-Gonzalo, E., Moreno-Cordero, B., Pérez-Pavón, J.L., Garcia-Pinto, C., Laespada, E.F., Surfactant cloud point extraction and preconcentration of organic compounds prior to chromatography and capillary electrophoresis (2000) J. Chromatogr. a, 902, pp. 251-265Alfassi, Z.B., Wai, C.M., (2000) Preconcentration Techniques for Trace Elements, , CRC Press Boca RatonCave, M.R., Butler, O., Cook, J.M., Cresser, M.S., Garden, L.M., Holden, A.J., Miles, D.L., Environmental analysis-atomic spectrometry update (1999) J. Anal. At. 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Formation of metal diethyldithiophophates and extraction from mineral acid solutions (1962) Fresenius Z. Anal. Chem., 185, pp. 179-201Silva, M.A.M., Frescura, V.L.A., Curtius, A.J., Determination of trace elements in water samples by ultrasonic nebulization inductively coupled plasma mass spectrometry after cloud point extraction (2000) Spectrochim. Acta, Part B: Atom. Spectrosc., 55, pp. 803-813Chen, J., Teo, K.C., Determination of cadmium, copper, lead and zinc in water samples by flame atomic absorption spectrometry after cloud point extraction (2001) Anal. Chim. Acta, 450, pp. 215-222Pinto, C.G., Pavón, J.L.P., Cordero, B.M., Cloud point preconcentration and flame atomic absorption spectrometry: Application to the determination of cadmium (1996) J. Anal. At. Spectrom., 11, pp. 37-41Tani, H., Kamidate, T., Watanabe, T., Micelle-mediated extraction (1997) J. 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Soft sensors with white- and black-box approaches for a wastewater treatment process

The increasing degradation of water resources makes it necessary to monitor and control process variables that may disturb the environment, but which may be very difficult to measure directly, either because there are no physical sensors available, or because these are too expensive. In this work, two soft sensors are proposed for monitoring concentrations of nitrate (NO) and ammonium (NH) ions, and of carbonaceous matter (CM) during nitrification of wastewater. One of them is based on reintegration of a process model to estimate NO and NH and on a feedforward neural network to estimate CM. The other estimator is based on Stacked Neural Networks (SNN), an approach that provides the predictor with robustness. After simulation, both soft sensors were implemented in an experimental unit using FIX MMI (Intellution, Inc) automation software as an interface between the process and MATLAB 5.1 (The Mathworks Inc.) software