Hybrid selective noncatalytic reduction (SNCR)/selective catalytic reduction (SCR) for NOx removal using low-temperature SCR with Mn-V2O5/TiO2 catalyst.

UNLABELLED: A hybrid selective noncatalytic reduction/selective catalytic reduction (SNCR/SCR) system that uses two types of technology, low-temperature SCR process and SNCR process, was designed to develop nitrogen oxide (NOx) reduction technology. SCR was conducted with space velocity (SV)=2400 hr(-1) and hybrid SNCR/SCR with SV=6000 hr(-1), since the study focused on reducing the amount of catalyst and both achieved 98% NOx reduction efficiency. Characteristics of NOx reduction by NH3 were studied for low-temperature SCR system at 150 °C using Mn-V2O5/TiO2 catalyst. Mn-added V2O5/TiO2 catalyst was produced, and selective catalyst reduction of NOx by NH3 was experimented. NOx reduction rate according to added Mn content in Mn-V2O5/TiO2 catalyst was studied with varying conditions of reaction temperature, normalized stoichiometric ratio (NSR), SV, and O2 concentration. In the catalyst experiment according to V2O5 concentration, 1 wt.% V2O5 catalyst showed the highest NOx reduction rate: 98% reduction at temperature window of 200~250 °C. As a promoter of the V2O5 catalyst, 5 wt.% Mn was added, and the catalyst showed 47~90% higher efficiency even with low temperatures, 100~200 °C. Mn-V2O5/TiO2 catalyst, prepared by adding 5 wt.% Mn in V2O5/TiO2 catalyst, showed increments of catalyst activation at 150 °C as well as NOx reduction. Mn-V2O5/TiO2 catalyst showed 8% higher rate for NOx reduction compared with V2O5/TiO2 catalyst in 150 °C SCR. Thus, (5 wt.%)Mn-(1 wt.%)V2O5/TiO2 catalyst was applied in SCR of hybrid SNCR/SCR system of low temperature at 150 °C. Low-temperature SCR hybrid SNCR/SCR (150 °C) system and hybrid SNCR/SCR (350 °C) showed 91~95% total reduction rate with conditions of SV=2400~6000 hr(-1) SCR and 850~1050 °C SNCR, NSR=1.5~2.0, and 5% O2. Hybrid SNCR/SCR (150 °C) system proved to be more effective than the hybrid SNCR/SCR (350 °C) system at low temperature. IMPLICATIONS: NOx control is very important, since they are the part of greenhouse gases as well as the cause of acid rain and ozone hole. A technology, so-called hybrid SNCR/SCR process, was tested using Mn-V2O5/TiO2 monolithic catalyst for NOx reduction, and the method is promising. The results of this study would provide some ideas to parties such as policy makers, environmental engineers, and so on.

Design of jetty piles using artificial neural networks.

To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area.

The study used existing indicator bacterial data and a number of physicochemical parameters that can be measured instantaneously to determine if a decision tree approach, especially classification and regression tree, could be used to predict bacterial concentrations in timely manner for beach closure management. Each indicator bacteria showed different tree structures and each had its own significant variables; Dissolved oxygen played an important role for both total coliform and fecal coliform and turbidity was the most important factor to predict concentrations of enterococci for decision tree approaches. Root mean squared error stayed between 5 and 6.5% of the average values of observations; RMSEs from each simulation, 0.25 for total coliform, 0.31 for fecal coliform, and 0.29 for enterococci. Estimations from tree structures would be regarded as a good representation of the actual data. In addition to results of the objective function, RMSE, 77.5% of actual value fell into the 95% of confidence interval of estimations for total coliform concentrations, 60% for fecal coliform concentrations, and 62.5% for enterococci concentrations. The approach showed reliable estimations for the majority of the data processed, although the method did not portray low concentrations of bacteria as well.

Identification and application of physical and chemical parameters to predict indicator bacterial concentration in a small Californian creek.

This study of Aliso Creek in California aimed to identify physical and chemical parameters that could be measured instantly to be used in a model to serve as surrogates for indicator bacterial concentrations during dry season flow. In this study, a new data smoothing technique and ranking/categorizing analysis was used to reduce variation to allow better delineation of the relationships between adopted variables and concentrations of indicator bacteria. The ranking/categorizing approach clarified overall trends between physico-chemical data and the indicators and suggested sources of the bacteria. This study also applied a principle component regression model to the data. Although the model was promising for predicting concentrations of total and fecal coliforms, it was somewhat weaker in predicting enteroccocci.