Mohamed El-Sayed El-Mahdy

This study was carried out to assess the performance of the activated sludge process (ASP) for the treatment of oil refinery wastewater. Seasonal variation for temperature and the total dissolved solids (TDS) of wastewater were the key parameters examined. The treatment system is a batch-laboratory column that is continuously fed with oil refinery wastewater after physical separation of the surface oil layer in the refinery (API Separator). The determination of optimum operating conditions was performed for the treatment system at different temperatures ranging from 20 °C to 35 °C. Two main groups were examined: fresh wastewater (Group A) had an average TDS of less than 3 g/L, and saline wastewater (Group B) had an average TDS of 10–15 g/L. Results indicated that removal percentages in fresh wastewater (Group A) for chemical oxygen demand (COD), biological oxygen demand (BOD), oil and grease (O&G), phenols, and total suspended solids (TSS) were (76%–83%), (80.9%–92%), (83.5%–100%), and (94%–100%), respectively, while removal percentages in saline wastewater (Group B) for COD, BOD, O&G, phenols, and TSS were (76%–81%), (85.3%–95.8%), (87.5%–90%), (100%), and (93%–92%), respectively. The highest removal efficiency for pollution parameters was obtained at an average temperature of 25–35 °C. In conclusion, the overall treatment efficiency of fresh wastewater is better than that of saline wastewater. The quality of treated effluents achieved complies with the permissible limits of Egyptian regulations. Finally, ASP is efficient for the oxidation of organic matter applied to oil refinery wastewater with similar characteristics.

This study used Artificial Intelligence (AI) techniques as a modeling tool to estimate the risk of Nile flooding in the cities of southern Sudan. Climatic records, and precipitation, from stations along the area were used between 2010 and 2019. To test how well the models worked, the forecast was done using a variety of stations. To determine the flood rate in southern Sudan with the highest degree of accuracy, various artificial neural network techniques were investigated. Six artificial neural network (ANN) models were created and compared to show flood prediction to reach the maximum level of accuracy and to improve the results (NN, GRNN, RNN, CFNN, PNN, FFNN). The artificial neural network (FFNN) produced the best results in the first test, reaching a 95 % accuracy rate. Three further strategies were evaluated by increasing the neural network's hidden layer count to ten. Tests with 15 and 25 hidden layers also showed that the accuracy changes with the increase of hidden layers. Also, six other algorithms were applied to reach the highest value expected from Using one of the artificial intelligence techniques (AI), in predicting the flood by machine learning methods (ML). The highest expected value of flooding was reached through the (Gradient Boosting) model, where it was Classification Accuracy (CA) 0.937, followed by (AdaBoost), (CA 0.916).

To protect the dam body and downstream area, spillways should be built. Because the difference in water level upstream and downstream spillways is usually very big, energy dissipation facilities should be introduced. Stepped spillways were discovered to immensely enhance energy dissipation. V-notch end sills installed on stepped spillways were not investigated enough. The challenge in using this type of end sills is to know the ideal angle of the notch. The second challenge is to link the energy dissipation percent and the scour hole characteristics with the hydraulic parameters of the spillway and the soil properties. This paper aims to investigate the V-notch end sills in stepped spillways and to assess their effect on energy dissipation and scour. For this purpose, physical models of four steps were built on stepped spillways in the laboratory. V-notch end sills were installed at the end of each step of the spillway. The dimensional analysis was used to link the various parameters influencing the studied phenomena. All V-notch end sill angles used enhanced energy dissipation, reduced the relative scour depth, length, and volume. Minimizing the angle of V-notch end sills enhanced energy dissipation. Three scour equations were developed to predict scour characteristics from easy-to-record measurements.

Understanding lake evaporation and the climate change role in evaporation is paramount for any water resources management system. The prediction of the climate's future changes is a very important step in planning lake future management decisions. This study analyzed Lake Nasser's evaporation in southern Egypt. Meteorological parameters were compiled from Aswan meteorological station near Lake Nasser. Also, CORDEX predicted climatological parameters from 2021 to 2050 were collected. Lake Nasser's evaporation prediction model using artificial neural networks technique was built. Statistics were calculated in the calibration and validation stages to find the most adequate model of the Lake evaporation calculation. The predictions of future evaporation were extracted from the model using predicted climatological data from CORDEX regional climate models. Trend analysis was done to assure the impacts of climate change on the lake. ANN model was developed and implemented successfully on Lake Nasser, which could be used to handle evaporation calculation over Lake Nasser. ANN model with training algorithm with 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22 neurons with 5 input variables were tested to find the best model evaporation estimation of the lake with the least number of neurons. ANN model with training algorithm with 20 neurons with 5 input variables performed the best for evaporation estimation of the lake. According to predicted climate data, about a 2% increase in lake Nasser evaporation could be predicted in the year 2050. It was noticed that the maximum predicted values of evaporation are in July to August months with a range from 7.04 mm/day to 9.64 mm/day. The peak value, the outlier of maximum evaporation, is about 11.16 mm/day. The minimum predicted values are in December to January months with a range from 3.50 mm/day to 6.81 mm/day. Trend analysis showed that the predicted climatological parameters are slightly higher than historical records.