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Tacoma, WA, United States

Mullai P.,Annamalai University | Rene E.R.,Recovery Water | Sridevi K.,Annamalai University
BioMed Research International | Year: 2013

Mangrove sediments host rich assemblages of microorganisms, predominantly mixed bacterial cultures, which can be efficiently used for biohydrogen production through anaerobic dark fermentation. The influence of process parameters such as effect of initial glucose concentration, initial medium pH, and trace metal (Fe2+) concentration was investigated in this study. A maximum hydrogen yield of 2.34, 2.3, and 2.6 mol H2 mol -1 glucose, respectively, was obtained under the following set of optimal conditions: initial substrate concentration - 10,000 mg L-1, initial pH - 6.0, and ferrous sulphate concentration - 100 mg L-1, respectively. The addition of trace metal to the medium (100 mg L-1 FeSO4·7H2O) enhanced the biohydrogen yield from 2.3 mol H2 mol-1 glucose to 2.6 mol H2 mol -1 glucose. Furthermore, the experimental data was subjected to kinetic analysis and the kinetic constants were estimated with the help of well-known kinetic models available in the literature, namely, Monod model, logistic model and Luedeking-Piret model. The model fitting was found to be in good agreement with the experimental observations, for all the models, with regression coefficient values >0.92. © 2013 P. Mullai et al. Source

Behera S.K.,Institute of Chemical Technology | Behera S.K.,The Clean Tech Center | Rene E.R.,Recovery Water | Kim M.C.,Yoo Sung Co. | Park H.-S.,The Clean Tech Center
International Journal of Energy Research | Year: 2014

SUMMARY: In order to provide adequate engineering assistance and to improve the energy efficiency in process industries, it is crucial to evaluate the operational performance of a boiler in terms of its practical requirements, viz. temperature, pressure, and mass flow rate of steam. This study was aimed at assessing and optimizing the performance of a refuse plastic fuel-fired boiler using artificial neural networks. A feed-forward back propagation neural network model was developed and trained using existing plant data (5months), to predict temperature, pressure, and mass flow rate of steam, using the following input parameters: feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature. The predictive capability of the model was evaluated in terms of mean absolute percentage error between the model fitted and actual plant data, while sensitivity analysis was performed on the input parameters by determining the absolute average sensitivity values. The higher absolute average sensitivity value of the incinerator exit temperature in comparison to that of feed water pressure, feed water temperature and conveyor speed suggested that the change of incineration exit temperature has a significant influence on the selected outputs (steam properties). Overall, the good results observed from this work demonstrate the fact that artificial neural networks can efficiently predict the data on steam properties and could serve as a good tool to monitor boiler behavior under real-time conditions. © 2013 John Wiley & Sons, Ltd. Source

Recovery Water | Date: 2014-06-20

A system, method, and apparatus for desalinating water, such as seawater. The system, method, and/or apparatus includes an electrodialysis cell that can separate monovalent ionic species from multivalent ionic species, so they may be separately treated. Each separate treatment may include precipitation of salt via the use of an organic solvent, followed by processing of precipitated salts and membrane treatment of water to remove solvent and remaining salts.

Recovery Water | Date: 2013-12-06

A system, method, and apparatus for precipitating a water soluble salt or water soluble salts from water, including adding a water-miscible solvent to a water solution including an inorganic salt. The system, method and apparatus also allow for the separation of the precipitated salt, and for separation of the solvent from the water. In doing so, reclamation of water is provided.

Recovery Water | Date: 2013-12-06

Described herein are methods of separating phase separated, neutrally buoyant materials from liquids and apparatuses for carrying out the methods. The methods and apparatuses employ nanobubbles. In certain embodiments, the nanobubbles may be formed in solutions of hydrophobically modified water soluble polymers. The methods result in removal of 90% by weight or more of neutrally buoyant materials from liquids. The methods are useful in certain embodiments for separating neutrally buoyant oily mixtures from water produced by mining operations.

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