Effect of platinum on the photocatalytic degradation of chlorinated organic compound

TiO2 nanoparticles, doped with di erent Pt contents, were prepared by a modified photodeposition method using Degussa P-25 TiO2, H2PtCl6 6H2O and methanol as the solvents. The physicochemical properties of Pt/TiO2 were investigated by the nitrogen adsorption and desorption isotherm measurement technique, X-ray di raction analysis and photoluminescence spectra, respectively. Reaction rates from photocatalytic removal of dichloromethane over Degussa P-25 TiO2 and Pt/TiO2 were evaluated. The average diameter and BET surface area of the TiO2 catalyst particles were 300 nm and 50 m2/g, respectively. The degradation e ciency was 99.0%, 82.7%, 55.2%, and 57.9% with TiO2 at inlet concentrations of 50, 100, 200, and 300 ppm, respectively. And the degradation e ciency was 99.3%, 79.7%, 76.5%, and 73.4% with a 0.005 wt.% Pt/TiO2 at inlet concentrations of 50, 100, 200, and 300 ppm, respectively. In addition, we found that the photoluminescence emission peak intensities decreased with increases in the doping amount of Pt, which indicates that the irradiative recombination was weakened. Furthermore, the results showed that the UV/0.005 wt.% Pt/TiO2 process was capable of e ciently decomposing gaseous DCM in air.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

Developing and evaluating techniques for localizing pollutant emission sources with open-path Fourier transform infrared measurements and wind data

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OP-FTIR) spectroscopy. The first approach combined the plume's peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume's peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR's monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25- to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m.     

Study on the indoor volatile organic compound treatment and performance assessment with TiO2/MCM-41 and TiO2/quartz photoreactor under ultraviolet irradiation

Volatile organic compounds (VOCs) are the cause of indoor air pollution and are readily emitted from furniture and cleaning agents. In Taiwan, the concentrations of indoor VOCs range roughly from 1 to 10 ppm. It is important to effectively reduce indoor VOC emissions and establish the implementation of long-term, low-cost, controlled techniques such as those found in the ultraviolet/titanium dioxide (UV/TiO2) control systems. This study evaluates the performance of a photoreactor activated by visible irradiation and packed with TiO2/quartz or TiO2/mobile catalytic material number 41 (MCM-41). The photocatalysts tested include commercial TiO2 (Degussa P-25) and synthesized TiO2 with a modified sol-gel process. The UV light had a wavelength of 365 nm and contained an 8-W, low-pressure mercury lamp. Reactants and products were analyzed quantitatively by using gas chromatography with a flame-ionization detector. It is important to understand the influence of such operational parameters, such as concentration of pollutant, temperature, and retention time of processing. The indoor concentrations of VOCs varied from 2 to 10 ppm. Additionally, the temperatures ranged from 15 to 35 degrees C and the retention time tested from 2 to 8.2 sec. The results show that quartz with TiO2 had a better photoreductive efficiency than quartz with MCM-41. The toluene degradation efficiency of 77.4% with UV/TiO2/quartz was larger than that of 54.4% with the UV/TiO2/MCM-41 system under 10-min reaction time. The degradation efficiency of the UV/TiO2 system decreased with the increasing concentrations of indoor VOCs. The toluene degradation efficiency at 2 ppm was approximately 5 times greater than that at 10 ppm. The photoreduction rate of the VOCs was also evaluated with the Langmuir-Hinshewood model and was shown to be pseudo-first-order kinetics.     

Mineralization of Reactive Black 5 in aqueous solution by basic oxygen furnace slag in the presence of hydrogen peroxide

Basic oxygen furnace slag (BOF slag) is a solid waste arisen from the steel making process. FeO is one of the major components of BOF slag. The FeO-containing property of BOF slag makes it possible to catalyze the Fenton reaction. Reactive Black 5 (RB5) dye is chosen as the target compound in this study. This study has investigated the catalytic performance of BOF slag on the Fenton reaction to decompose RB5 in aqueous solution. A first-order kinetic model with respect to TOC was adopted to explain the mineralization of RB5 by the H(2)O(2)/BOF slag process. The experimental results in this study suggested that dosage with 1.49 x 10(-4)M min(-1) H(2)O(2) and 12.5 g l(-1) BOF slag in the solution at pH 2 provided the optimal operation conditions for the mineralization of RB5 yielding a 51.2% treatment efficiency at 100 min reaction time, and complete decoloration can be achieved within 30 min reaction time. The H(2)O(2)/Fe(2+) ratio was then determined to be 6.06:1.     

Using pollution roses to assess sulfur dioxide impacts in a township downwind of a petrochemical complex

This study used pollution roses to assess sulfur dioxide (SO₂) pollution in a township downwind of a large petrochemical complex based on data collected from a single air quality monitoring station. The pollution roses summarized hourly SO₂ concentrations at the Taishi air quality monitoring station, located approximately 7.8–13.0 km south of the No. 6 Naphtha Cracking Complex in Taiwan, according to 36 sectors of wind direction during the preoperational period (1995–1999) and two postoperational periods (2000–2004 and 2005–2009). The 99th percentile of hourly SO₂ concentrations 350? downwind from the complex increased from 28.9 ppb in the preoperational period to 86.2–324.2 ppb in the two postoperational periods. Downwind SO₂ concentrations were particularly high during 2005–2009 at wind speeds of 6–8 m/sec. Hourly SO₂ levels exceeded the U.S. Environmental Protection Agency (EPA) health-based standard of 75 ppb only in the postoperational periods, with 65 exceedances from 0–10? and 330–350? downwind directions during 2001–2009. This study concluded that pollution roses based on a single monitoring station can be used to investigate source contributions to air pollution surrounding industrial complexes, and that it is useful to combine such directional methods with analyses of how pollution varies between different wind speeds, times of day, and periods of industrial development.

Implications: The pollution roses summarize SO₂ concentrations by wind direction and to investigate source contribution to air quality. Percentile statistics can catch pollution episodes occurring in a very short time at specific wind directions and speeds. The downwind areas have already exceeded regulated 1-hr SO₂ standard since the operation of the complex.     

GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING1

ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real‐time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall‐runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real‐time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.     

GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING1

ABSTRACT: Genetic Programming (GP) is a domain‐independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then utilized as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km² is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the trained GP. Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.