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Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner. INTRODUCTIONThe modeling of atmospheric dispersion has been actively researched for many decades. However, because of the vast number of variables involved and the arbitrary variations in wind

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A denitrifying bacterial consortium obtained from the Pullman, Washington wastewater treatment facility was enriched under denitrifying conditions and its ability to reduce selenite and selenate was studied. Replicate experiments at two different experimental conditions were performed. All experiments were performed under electron-acceptor limiting conditions, with acetate as the carbon source and nitrate the electron acceptor. In the first set of experiments, selenite was present, whereas, in the second set, selenate was added. A significant lag period of approximately 150 h was necessary before selenite or selenate reduction was observed. During this lag period, nitrate and nitrite use was observed. Once selenite or selenate reduction had started, nitrate and nitrite reduction was concomitant with selenium species reduction. Trace amounts of selenite were detected during the selenate reduction study. Analysis of the data indicates that, once selenium species reduction was induced, the rate of reduction was proportional to the selenium species concentration and to the biomass concentration. Furthermore, at similar biomass and contaminant concentrations, selenite reduction is approximately four times faster than selenate reduction

A unique design of experiments is presented as an aid to study the destruction of dissolved organic matter in process wastewater. The chief dissolved components of the wastewater were formaldehyde, methanol, 2-propanol, ethanol, acetic acid, formic acid, and unknowns. The unknowns were assumed to be C5 alcohols or, specifically, pentanol. Taguchi methods were applied in the design of experiments for each different experimental variable to determine its percentage contribution to the destruction of the dissolved organic matter. This technique helped us gain an insight into the relative significance of the various experimental factors under actual operating levels. The utility of this approach is attractive particularly because AOP (advanced oxidation processes) is a developmental technology, and hence, there is scant documentation or experience on the destruction of a complex mixture of dissolved organic compounds. When there are a large number of experimental variables, and there exists indecision as to which can be manipulated in order to achieve higher removal efficiencies of the dissolved organics, then this technique can provide an extremely effective structured approach.A pilot plant study was conducted to evaluate three commercial catalysts for hydrotreating of light cycle oil to reduce its aromatic content and improve the cetane index. The operating parameters were varied between 325 and 400 "C, 1 and 3 h-l, and 4 and 10 MPa at 535 L/L. The data showed that, in general, the product density and aromatic content decreased as the temperature or pressure increased or space velocity decreased. The cetane index improvement ranged from 7.3 to 10.0 for the Ni-W/A1203 catalyst and from 6.1 to 10.1 for the Ni-Mo/A1,03 catalysts. The catalyst performance was evaluated in terms of hydrodesulfurization, hydrodenitrogenation, hydrogenation, aromatic saturation, and hydrogen consumption. This study confirms that light cycle oil can be hydrotreated to improve its cetane quality, thus increasing the extent of its blending ratio into the diesel pool.

Hexavalent chromium reduction kinetic parameters were estimated for several mixed cultures using molasses as the carbon source and nitrate as the terminal electron acceptor. Mixed cultures were enriched from diverse environmental sources, and kinetic parameter comparisons are made between the consortia and a pure culture grown under the same conditions. A statistical analysis of the results indicated that some kinetic parameters exhibited significant differences, whereas others did not. It should be noted that those parameters with statistically significant differences were nonetheless numerically similar. Conservative values, therefore, could be assumed, or site‐specific data obtained, for those parameters to design in situ bioremediation of chromium‐contaminated sites.