[show abstract][hide abstract] ABSTRACT: This paper analyzes five years hourly wind data from twenty-nine weather stations to identify the potential location for wind energy applications in Oman. Different criteria including theoretical wind power output, vertical profile, turbulence and peak demand fitness were considered to identify the potential locations. Air density and roughness length, which play an important role in the calculation of the wind power density potential, were derived for each station site. Due to the seasonal power demand, a seasonal approach was also introduced to identify the wind potential on different seasons. Finally, a scoring approach was introduced in order to classify the potential sites based on the different factors mentioned above. It is concluded that Qayroon Hyriti, Thumrait, Masirah and Rah Alhad have high wind power potential and that Qayroon Hyriti is the most suitable site for wind power generation.
Renewable and Sustainable Energy Reviews 01/2010; 14(5):1428-1436. · 5.63 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper describes an artificial neural networking (ANN) model developed to predict the behaviour of semi-rigid composite
joints at elevated temperature. Three different semi-rigid composite joints were selected, two flexible end-plates and one
flush end-plate. Seventeen different parameters were selected as input parameters representing the geometrical and mechanical
properties of the joints as well as the joint’s temperature and the applied loading, and used to model the rotational capacity
of the joints with increasing temperatures. Data from experimental fire tests were used for training and testing the ANN model.
Results from nine experimental fire tests were evaluated with a total of 280 experimental cases. The results showed that the
R2 value for the training and testing sets were 0.998 and 0.97, respectively. This indicates that results from the ANN model
compared well with the experimental results demonstrating the capability of the ANN simulation techniques in predicting the
behaviour of semi-rigid composite joints in fire. The described model can be modified to study other important parameters
that can have considerable effect on the behaviour of joints at elevated temperatures such as temperature gradient, axial
Keywordsflush end-plate–flexible end-plate–composite joints–artificial neural network–fire–rotation
International journal of steel structures 01/2010; 10(4):337-347. · 0.30 Impact Factor
[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: Contaminants are deposited on the outdoor insulator surface due to environmental conditions. Conductivity or Equivalent Salt Deposit Density (ESDD) normally expresses this contamination on the insulator surface. In the laboratory, NaCl, tap water and distilled water are used for measuring conductivity and ESDD. In addition, different sizes of glass plates are used as an insulating medium. A conductivity-measuring instrument (Cond 300i) is used to measure the conductivity of the salt-solution. Based on the experimental data, the relationship between the different variables (temperature, salinity, salt, type of water, plate size and sigma) and the ESDD are modeled using artificial neural networks. The developed model showed a good predictive success with R2 value above 0.98. This value indicates high accuracy for both model development and the model generalization capability. The meteorological variables (temperature, salinity, salt, type of water, plate size, etc.) with the greatest influence on ESDD are also identified using the weight partitioning method. It is found that glass plate size is the variable that has the greatest effect on the prediction of the ESDD since it has a contribution of 47%. The volume conductivity at different degrees had a contribution between 12.38% and 12.87%, while the type of water, the salt quantity, the salinity, and the temperature used had a contribution percentage of 7.92, 7.92, 7.43, and 4.46, respectively. The investigation indicated that the ANN models are well-suited for predicting the contamination level to prevent flashover on the insulator surface and for analyzing the contribution of the different factors affecting this contamination level that are represented either by the ESDD or the conductivity. Additionally, the ANN models can be extended for other applications in which nonlinear relationships are observed.
[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: This paper discusses the development of a predictive artificial neural network (ANN)-based prototype controller for the optimum operation of an integrated hybrid renewable energy-based water and power supply system (IRWPSS). The integrated system, which has been assembled, consists of photovoltaic modules, diesel generator, battery bank for energy storage and a reverse osmosis desalination unit. The electrical load consists of typical households and the desalination plant. The proposed Artificial Neural Networking controller is designed to be implemented to take decision on diesel generators ON/OFF status and maintain a minimum loading level on the generator under light load and high solar radiation levels and maintain high efficiency of the generators and switch off diesel generator when not required based on predictive information. The key objectives are to reduce fuel dependency, engine wear and tear due to incomplete combustion and cut down on greenhouse gas emissions. The statistical analysis of the results indicates that the R2 value for the testing set of 186 cases tested was 0.979. This indicates that ANN-based model developed in this work can predict the power usage and generator status at any point of time with high accuracy.
[show abstract][hide abstract] ABSTRACT: This paper presents a technique based on the development of an artificial neural network (ANN) model for modeling and predicting the relationship between the grounding resistance and length of an electrode buried in the soil based on experimental data. The results indicate the strong agreement between the model prediction and experimental values. The statistical analysis shows that the R2 values were 0.995 and 0.925 for the training and testing sets, respectively.
[show abstract][hide abstract] ABSTRACT: Purpose – Presents a technique based on the development of an artificial neural network (ANN) model for predicting the electromagnetic inference effects on gas pipelines shared right-of-way (ROW) with high voltage transmission lines. Design/methodology/approach – Examines the induced pipeline voltage under different soil resistivity, fault current and separation distance. Findings – The results indicate strong agreement between model prediction and observed values. Originality/value – Demonstrates that the ANN-based model developed can predict the induced voltage with high accuracy. The accuracy of the predicted induced voltage is very important for designing mitigation systems that will increase overall pipeline integrity and make the pipeline and appurtenances safe for operating personnel.
COMPEL International Journal of Computations and Mathematics in Electrical 02/2005; 24(1):69-80. · 0.28 Impact Factor
[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 deals specifically with the use of a neural network for ozone modelling in the lower atmosphere. The development of a neural network model is presented to predict the tropospheric (surface or ground) ozone concentrations as a function of meteorological conditions and various air quality parameters. The development of the model was based on the realization that the prediction of ozone from a theoretical basis (i.e. detailed atmospheric diffusion model) is difficult. In contrast, neural networks are useful for modelling because of their ability to be trained using historical data and because of their capability for modelling highly non-linear relationships. The network was trained using summer meteorological and air quality data when the ozone concentrations are the highest. The data were collected from an urban atmosphere. The site was selected to represent a typical residential area with high traffic influences. Three neural network models were developed. The main emphasis of the first model has been placed on studying the factors that control the ozone concentrations during a 24-hour period (daylight and night hours were included). The second model was developed to study the factors that regulate the ozone concentrations during daylight hours at which higher concentrations of ozone were recorded. The third model was developed to predict daily maximum ozone levels. The predictions of the models were found to be consistent with observations. A partitioning method of the connection weights of the network was used to study the relative percent contribution of each of the input variables. The contribution of meteorology on the ozone concentration variation was found to fall within the range 33.15–40.64%. It was also found that nitrogen oxide, sulfur dioxide, relative humidity, non-methane hydrocarbon and nitrogen dioxide have the most effect on the predicted ozone concentrations. In addition, temperature played an important role while solar radiation had a lower effect than expected. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modelling.
[show abstract][hide abstract] ABSTRACT: Air quality modelling is an essential tool for most air pollution studies and the introduction of SO2 standards creates a need for modelling the dispersion of SO2. This work deals specifically with the use of the Industrial Source Complex Short Term (ISCST) model at a refinery. The study is performed over a period of 21 days. The first objective of this study was to measure the atmospheric levels of SO2 and then to compare their values with the international standard limits. The second objective was to evaluate the ISCST model by comparing the calculated and measured concentrations. The third objective was to demonstrate the effect of wind regimes on the dispersion of SO2 and to determine the spatial distribution of SO2 over the modelled area. The results showed that the levels of SO2 were well below the ambient air quality standard. Based on isopleths for SO2 distribution in the study area (as output from the ISCST model), it can be stated that no health risk is present in areas adjacent to the refinery.
[show abstract][hide abstract] ABSTRACT: This paper presents an artificial neural network (ANN)-based technique for tuning a damping controller for a static var compensator (SVC) to improve the damping of power systems over a wide range of typical load models. A proportional–integral (PI) controller is considered for the damping controller and its parameters are determined using the pole-placement technique. The types and characteristics of loads vary seasonally, and in some cases change over a day; consequently, a damping controller designed with fixed parameters can be adequate for some loads but, contrarily, can contribute to system instability with loads having other extreme characteristics. The developed ANN-based technique uses pre-determined system load characteristics combined with other measured system conditions as continuous inputs. Based on such information, the ANN technique adjusts the SVC damping controller parameters to assure good damping and system stability for the prevailing conditions. The proposed ANN technique has been applied to tune the parameters of the SVC damping controller for two power systems. The results show that the load model parameters have a considerable effect on the tuned parameters of the damping controller. Computer simulations performed on the two power systems show that the tuned parameters of the SVC damping controller using the ANN technique can provide better damping than the fixed parameters damping controller.
International Journal of Electrical Power & Energy Systems. 01/2000;
[show abstract][hide abstract] ABSTRACT: A systematic and general formulation of a Propagation Simulation Program (PSP) is developed for the coherent field of microwave and millimeter wave carrier signals traversing intermediate layered precipitation media taking into account the random behavior of particle size, orientation, shape and concentration distributions. Based on a rigorous solution of the volumetric multiple-scattering integral equations, the formalism offers the capability of treating the potential transmission impairments on satellite earth links and radar remote sensing generated by composite atmospheric layers of precipitation in conjunction with the finite polarization isolation of dual-polarized transmitting and receiving antennas. A multi-layered formulation is employed which encompasses an ensemble of discrete particles comprising an arbitrary mixture of ice crystals, melting snow and raindrops that may exist simultaneously along satellite-earth communication paths.
[show abstract][hide abstract] ABSTRACT: In this work, a novel approach using an artificial neural network was used to develop a model for analyzing the relationship between the Global Radiation (GR) and climatological variables, and to predict GR for locations not covered by the model's training data. The predicted global radiation values for the different locations (for different months) were then compared with the actual values. Results indicate that the model predicted the Global Radiation values with a good accuracy of approximately 93% and a mean absolute percentage error of 7.30. In addition, the model was also tested to predict GR values for the Seeb location over a 12 month period. The monthly predicted values of the ANN model compared to the actual GR values for Seeb produced an accuracy of 95% and a mean absolute percentage error of 5.43. Data for these locations were not included as part of ANN training data. Hence, these results demonstrate the generalization capability of this novel approach over unseen data and its ability to produce accurate estimates. Finally, this ANN-based model was also used to predict the global radiation values for Majees, a new location in north Oman.
[show abstract][hide abstract] ABSTRACT: Water sorption isotherms at 15, 25 and 45 °C were determined for two date varieties. Water sorption modeling was carried out using the five-parameter Guggenheim-Anderson-de Boer (GAB) equation, a modified-GAB equation and a novel artificial neural network (ANN) approach. Modeling using the GAB equations used physical data as input, while the ANN approach used both physical and chemical compositional data. The five-parameter GAB equation had a lower mean relative error (approximately 7%) than the modified-GAB equation (approximately 16%), in predicting equilibrium moisture content (EMC). The effects of temperature on the water sorption isotherms were not evident with the five-parameter GAB equation. Although the temperature effects on water sorption isotherms were evident with the modified GAB equation, the overall error was very high. Neither GAB equation could predict water sorption isotherm crossing, an effect observed in the experimental data. An ANN model, optimized by trial and error, was superior to both GAB equations. It could predict EMC with a mean relative error of 4.31% and standard error of moisture content of 1.36 g -05/ -05kg. The correlation coefficients (r2) of the relationships between the actual and predicted values of equilibrium moisture content and date varieties obtained by the ANN were 0.9978 and 0.9999 respectively. The ANN model was able to capture water sorption isotherm crossing due to temperature effects. Water activity and chemical compositional data, however, had more impact upon the water sorption isotherms than temperature.
[show abstract][hide abstract] ABSTRACT: In part I (see ibid., vol.10, no.3, p.139-43, 1996) of this two-part article, the importance of electricity demand forecasting, factors influencing the various ranges of demand forecasting, and their correlation and contribution to demand were discussed. Various models which can be used to identify the demand pattern and underlying growth to predict future demand were presented. A step-by-step method of building a forecasting model was also provided. In this concluding tutorial, the techniques discussed earlier are applied to generate load and energy forecasts for a rapidly growing utility for both the short and medium term.