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Articles by A. El-Shafie
Total Records ( 3 ) for A. El-Shafie
  A. El-Shafie , A.E. Noureldin , M.R. Taha and H. Basri
  Developing river inflow forecast is an essential requirement for reservoir operation. Accurate forecasting results in better control of water availability, more refined operation of reservoirs and improved hydropower generation. Artificial Neural Networks (ANN) models have been determined useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using mathematical models. The ANN forecasting model is established considering the utilization of the inflow pattern of the previous three months. In this study, real inflow data collected over the last 130 years at Lake Nasser upstream Aswan High Dam (AHD) on Nile River, Egypt was used to develop and examine the performance of the proposed method. The results showed that the proposed ANN model was capable of providing monthly inflow forecasting with Relative Error (RE) less than 20%, which is considerably more accurate if compared with the pre-developed regression model. The main merit of this model is to provide accurate source of information for inflow forecasting for better reservoir operation and appropriate long-term water resources management and planning.
  Mustafa M. Abed , A. El-Shafie and Siti Aminah Bt. Osman
  Problem statement: When the loads are applied to a brickwork structure, visco-elastic behavior upon their stress-strain relationships is exhibited, where the response can be classified into two separate parts: an instantaneous elastic strains and time-dependent creep strains. The creep strain represents the non- instantaneous strain that happens with time when the stress is sustained. Through the previous century, along with the alter in brickwork construction, A chain of creep tests on brickwork has shown that creep in brickwork be able to result in deformation that rise gradually with the way of time. Brickwork has considerable creep strain that is complicated to predict because of its reliance on several unrestrained parameters (e.g., relative humidity, time of load application, stress level). Dependable and precise prediction models for the long term, time-dependent creep deformation of brickwork structures are required. Artificial Neural Network (ANN) models have been determined useful and efficient especially in such problems for which the characteristics of the processes are difficult to describe using numerical models. Approach: This study introduces a creep prediction model based Focused Time-Delay Neural Network (FTDNN) which could detect and consider within its architecture the time dependency which is major factor in creep deformation in brickwork structure. Results: Performance of the proposed FTDNN model was examined with experimental creep data from brickwork assemblages collected over the last 15 years. Results showed that the FTDNN model has a relatively small prediction error compared to the other models with the error less than 15%. Conclusion: The results showed that the FTDNN model outperformed the existing ANN models and significantly enhance the accuracy of creep prediction.
  W.M. Alalayah , M.S. Kalil , A.A.H. Kadhum , J. Jahim , A. Zaharim , N.M. Alauj and A. El-Shafie
  Box-Wilson Design (BWD) model was applied to determine the optimum values of influencing parameters in anaerobic fermentation to produce hydrogen using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564). The main focus of the study was to find the optimal relationship between the hydrogen yield and three variables including initial substrate concentration, initial medium pH and reaction temperature. Microbial growth kinetic parameters for hydrogen production under anaerobic conditions were determined using the Monod model with incorporation of a substrate inhibition term. The values of μmax (maximum specific growth rate) and Ks (saturation constant) were 0.398 h-1 and 5.509 g L-1, respectively, using glucose as the substrate. The experimental substrate and biomass-concentration profiles were in good agreement with those obtained by the kinetic-model predictions. By varying the conditions of the initial substrate concentration (1-40 g L-1), reaction temperature (25-40°C) and initial medium pH (4-8), the model predicted a maximum hydrogen yield of 3.24 mol H2 (mol glucose)-1. The experimental data collected utilising this design was successfully fitted to a second-order polynomial model. An optimum operating condition of 10 g L-1 initial substrate concentration, 37°C reaction temperature and 6.0±0.2 initial medium pH gave 80% of the predicted maximum yield of hydrogen where as the experimental yield obtained in this study was 77.75% exhibiting a close accuracy between estimated and experimental values. This is the first report to predict bio-hydrogen yield by applying Box-Wilson Design in anaerobic fermentation while optimizing the effects of environmental factors prevailing there by investigating the effects of environmental factors.
 
 
 
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