×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerSafwan-Zubair area is regarded as one of the important agricultural areas in Basrah province, South of Iraq. The aim of this study is to predict groundwater level in this area using ANNs model. The data required for building the ANNs model are generated using MODFLOW model (V.5.3). MODFLOW model was calibrated based on field measurements of groundwater level in 13 monitoring wells during a period of one year (Nov./2013 to Oct/2014). The neural network toolbox available in MATLAB version 7.1 (2010B) was used to develop the ANN models. Three layers feed-forward network with Log- sigmoid transfer function was used. The networks were trained using Levenberg-Marquradt back-propagation algorithm. The ANN modes are divided into two groups, each of four models. The input data of the first group include hydraulic heads, while, the input data of the second group include hydraulic heads and recharge rates. Based on results of this study it was found that; the best ANN model for predicting groundwater levels in the study area is obtained when the input data includes hydraulic heads and recharge rates of two successive months preceding the target month, the best structure of ANN model is of three layers feed-forward network type composes of two hidden layers, each of ten nodes, and the including of recharge rates as input data, beside the hydraulic heads has improved slightly the results.
Impact energy prediction of thermal aged cast stainless steel from impact test was studied using artificial neural network (ANN) modeling. Impact energy data for specimens from eleven cast stainless steel alloys at different aging times and temperatures, were used to evaluate possible artificial neural network architecture for prediction impact energy. These data are taken from Argonne National Laboratories (ANL) in USA that involved impact test results of cast stainless steel after aging between 200 and 400oC for up to 30000 hour. The ANN model exhibited excellent comparison with experimental results of ANL i.e. correlation coefficient (R=0.9451) and mean square error (MSE=1.2*10-5). Since a large number of variables were used during training the ANN model, a reliable and useful predictor for impact energy in thermal aged cast stainless steel was provided.
The dieless drawing process is an innovative method emanated and appeared in coincidence with development of the concept of metal superplasticity. It is utilized from the local heating of a wire or tube to a specified temperature and followed by a local cooling, so an additional deformation is inhibited. In this study, a special dieless drawing machine was designed to carry out an experimental program on SUS304-stainless steel wire having diameter of (1.6-2) mm to investigate the main process parameters such as speeds, heat quantity, heating coil width and heating-cooling separation distance. Also, a numerical model based on thermo-mechanical analysis was developed and validated with experimental program. Furthermore, an artificial neural network ANN model based on current experimental data was prepared to predict the dieless drawing behavior. A maximum area reduction of 40.7% was obtained in single pass. A 3.12mm/s feeding velocity and 4.97mm/s drawing velocity were realized through the experimental tests. The results showed that both drawing force and wire profile were effected by increasing of feeding speed, heating coil width and separation distance. Also, it is confirmed that strain rate was reduced by increasing the heating coil width and the reduction ratio was promoted. A maximum error of 21% was recorded between ANN model and experimental results. The results showed a good agreement among experimental, numerical and ANN models.
In this research, the effect of seawater environments and surface roughness on uniform corrosion rate of carbon steel (A516 grade 65) was studied depending on the experimental work and artificial neural network modeling. The experimental work involves chemical composition, samples machining, roughness measurements (for carbon steel specimens), conductivity and salinity measurements (for seawater), and uniform corrosion test. Weight loss technique was employed in determining the uniform corrosion rate in carbon steel material. Also, artificial neural network (ANN) model was built to predict the values of uniform corrosion rate (mpy) at different values of conductivity, salinity for seawater and roughness factor for carbon steel depending on the experimental results which were used train and test the ANN. The results obtained of uniform corrosion rate by ANN predictions are shown to be agreed well against experimental values. i.e. correlation coefficient, R=0.9974
Artificial Neural Networks (ANN) have been applied to structural engineering in recent years. Most of the researches are based on backpropagation neural networks due to its well-studied theory. A backpropagation neural network has been used to predict the ultimate torsional strength of reinforced concrete rectangular beams. The effects of the parameters, such as the number of nodes in the input, output and hidden layers and the pre-process of the training patterns, on the behaviour of the neural network have been investigated. The algorithm called 'resilient propagation algorithm' has been used to the performance of the neural network. After training, the generalization of the neural network was tested by the patterns not included in the training patterns. Once the neural network has been trained, the ultimate torsional strength of reinforced concrete is obtained very easily and efficiently. Based on the ANN results, a parametric analysis was carried out to study the influence of parameters affecting the ultimate torsional strength of reinforced concrete beams and these results are compared with the equations of ACI-code.
This paper introduces a radial distribution feeder protection scheme based on certain features extraction from current signals measurement at the substation. The features are captured using the discrete wavelet transform (DWT). Two digital signals processing methods are used to introduce those features to the 1) fault detection 2) identification and 3) localization schemes; the first one is the energy method and the second one is the root mean square method. For the purpose of fault type identification, two systems are tested and compared, a Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Fault location scheme is then built based on ANNs. An effort is made to reduce the computational burden and the speed of detection provided by the fault detection and identification schemes. Since the short circuit faults are the most likely types of faults that can occur in power systems, the ten types of these faults taking into account different fault resistances are simulated in MATLAB environment and the protection scheme is built based on the idea of over current. The power quality disturbances such as switching transient events on the feeder is also taken into account in order to build a reliable and secure protection scheme.
Castor Oil is a natural raw material, used to prepare Brominated Castor Oil (BCO) and quaternary ammonium salt based castor oil (TEt-CO). The two products were tested as demulsifiers and compared with a commercial demulsifier (Chimec2439) by using bottle test method. BCO showed a high ability on water separation efficiency 90% with a dose of 150µl at 120min time settling while TEt- CO showed a low water separation efficiency reached to 10%. The effect of the demulsifier BCO was tested by varying different variables which have an obvious effect on water separation efficiency such as: dose, temperature, time of mixing emulsion, pH and salinity of aqueous phase of emulsion, and water ratio. The effect of some additives (i.e. methanol, ethanol, xylene and toluene) on the efficiency of the BCO was tested for the purpose of enhancing its effectiveness to break the crude oil emulsion. The experimental data obtained by using BCO were formulated as a model using the Artificial Neural Networks (ANNs) to evaluating the water separation efficiency. Multi-layer perceptron artificial neural network was developed based on the collected data of this study. The results showed that the training algorithm of back propagation (BP) is sufficient enough in predicting BCO efficiency under different operation conditions. It was found that the correlation coefficient values are 0.9995 and 0.9999 for the testing and training data, respectively and the mean square error (MSE) was 6.18*10^-5 at 200 epochs.