[show abstract][hide abstract] ABSTRACT: It has been clearly established that the reattachment length for laminar flow depends on two non-dimensional parameters, the Reynolds number and the expansion ratio, therefore in this work, an ANN model that predict reattachment positions for the expansion ratios of 2, 3 and 5 based on the above two parameters has been developed. The R2 values of the testing set output Xr1, Xr2, Xr3, and Xr4 were 0.9383, 0.8577, 0.997 and 0.999 respectively. These results indicate that the network model produced reattachment positions that were in close agreement with the actual values. When considering the reattachment length of plane sudden-expansions the judicious combination of CFD calculated solutions with ANN will result in a considerable saving in computing and turnaround time. Thus CFD can be used in the first instance to obtain reattachment lengths for a limited choice of Reynolds numbers and ANN will be used subsequently to predict the reattachment lengths for other intermediate Reynolds number values. The CFD calculations concern unsteady laminar flow through a plane sudden expansion and are performed using a commercial CFD code STAR-CD while the training process of the corresponding ANN model was performed using the NeuroShellTM simulator.
[show abstract][hide abstract] ABSTRACT: This work encompasses ozone modeling in the lower atmosphere. Data on seven environmental pollutant concentrations (CH4, NMHC, CO, CO2, NO, NO2, and SO2) and five meteorological variables (wind speed, wind direction, air temperature, relative humidity, and solar radiation) were used to develop models to predict the concentration of ozone in Kuwait's lower atmosphere. The models were developed by using summer air quality and meteorological data from a typical urban site when ozone concentration levels were the highest. The site was selected to represent a typical residential area with high traffic influences. The combined method, which is based on using both multiple regression combined with principal component analysis (PCR) and artificial neural network (ANN) modeling, was used to predict ozone concentration levels in the lower atmosphere. This combined approach was used to improve the prediction accuracy of ozone. The predictions of the models were found to be consistent with observed values. The R2 values were 0.965, 0.986, and 0.995 for PCR, ANN, and the combined model prediction, respectively. It was found that combining the predictions from the PCR and ANN models reduced the root mean square errors (RMSE) of ozone concentrations. It is clear that combining predictions generated by different methods could improve the accuracy and provide a prediction that is superior to a single model prediction.
[show abstract][hide abstract] ABSTRACT: In this investigation, two Artificial Neural Network (ANN) models were applied for predicting ground-level sulfur dioxide (SO2) in the Sultanate of Oman in order to provide an early warning advisory for the protection of public health. The objective of the first model (Model I) was to use ANN to predict sulfur dioxide (SO2) levels at certain receptors from the Mina Al-Fahal refinery in Oman. The artificial neural network was also used for predicting the first 3 maximum SO2 concentrations and their corresponding locations with respect to the refinery (Model II). The models were used to determine meteorological conditions that most affect SO2 concentrations. In assessing this aspect, five meteorological parameters that are expected to affect the SO2 concentrations were explored. They include wind speed, atmospheric stability class, wind direction, mixing height, and ambient temperature. The developed models showed good predictive success with, R-squared values above 0.96 indicating high accuracy for both the models development and generalization capability. The meteorological variables with the greatest influence on SO2 concentrations were also identified. It was found that wind direction was the variable most important to Model I while wind direction, stability, and wind speed were the highest contributing variables in Model II. The investigation indicated that the ANN models were well-suited for modelling SO2 levels. Additionally, the ANN models can be extended for other applications in which non-linear relationships are observed.
American Journal of Environmental Sciences. 01/2008;
[show abstract][hide abstract] ABSTRACT: Data on the concentrations of seven environmental pollutants (CH4, NMHC, CO, CO2, NO, NO2 and SO2) and meteorological variables (wind speed and direction, air temperature, relative humidity and solar radiation) were employed to predict the concentration of ozone in the atmosphere using both multiple linear and principal component regression methods. Separate analyses were carried out for day light and night time periods. For both periods the pollutants were highly correlated, but were all negatively correlated with ozone. Multiple regression analysis was used to fit the ozone data using the pollutant and meteorological variables as predictors. A variable selection method based on high loadings of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the regression model of the logarithm of the ozone data. It was found that while high temperature and high solar energy tended to increase the day time ozone concentrations, the pollutants NO and SO2 being emitted to the atmosphere were being depleted. Night time ozone concentrations were influenced predominantly by the nitrogen oxides (NO+NO2), with the meteorological variables playing no significant role. However, the model did not predict the night time ozone concentrations as accurately as it did for the day time. This could be due to other factors that were not explicitly considered in this study.
[show abstract][hide abstract] ABSTRACT: This work is dealing essentially with atmospheric corrosion to assess the degrading effects of air pollutions on various metals that are mostly used in the engineering systems. The exposure study was conducted in Oman. The common materials like aluminum, brass, copper, epoxy, galvanized, mild steel and stainless steel were used for investigation. The sites of exposure were chosen at five locations where the metals are likely to be used. Additive models using median polish were used to investigate the patterns of corrosion by metal type and location. Regression analysis was also used to develop a number of predictor models for corrosion, based on metal type, location, number of months of exposure, and number of degrading pollutants in the air. The results of the additive models showed that copper and mild steel were the most corrosive metals while stainless steel and epoxy were the least corrosive. Of the locations, Sohar came out as the site with the worst corrosion record. Carbonates were the main component of corrosion, followed by chlorides and sulphates. The site at Al-Rusail had the highest level of carbonates corrosion, while the Airport and Al-Fahl showed the highest level of chlorides and sulphates corrosion, respectively.
Anti-corrosion Methods and Materials - ANTI-CORROS METHOD MATER. 01/1968; 15(10).