The concern over increasing needs for drinking water and awareness for development of systems to improve water quality both for drinking purposes and for effluents from wastewater treatment and industrial facilities have provided incentives to develop new technologies and improve performance of the existing one. Adsorption technology has many advantages over other treatment methods such as simple design, low investment cost, limited waste production, etc. Synthetic water with a dosing of artificial copper solution (Cu No3) was passed through a PVC column (15 cm diameter, 100 cm length) containing limestone as a filter media in three different sizes, using three different hydraulic rates, and three initial influent copper concentrations (7.04, 4.39, 1.72) ppm .For this study, three experiments have been conducted; continuous batch and field experiment. The up flow roughing filtration is the suitable technique to recover heavy metals present in aqueous solutions, without the need of adding further substances. The filtration results demonstrated that the smaller size of filter media (3.75) mm gave higher removal efficiency (93.75 – 98.80) % than larger filter media (9.50) mm which gave removal efficiency of (67.61 – 94.0) %. This is due to the large specific surface. The smaller size of limestone achieved the longer detention time (49) min, so the removal of Cu was more than (90) % for the (50) min of experiment. At lower flow rate (0.16) L/min, the removal efficiency was higher than at higher flow rate (0.77) L/min. At high flows, there is a reduced period of surface contact between the particles and copper solution. This study also involved three different batch experiments .The removal efficiency was (93- 97) % for the three types of limestone which indicates the importance of limestone media in the removal process. This also indicates that the removal efficiency was increasing with the increase of the limestone volume. Field experiment has been conducted using wastewater from Al- Dura Electric Station on the three types of limestone so that to ensure the laboratory tests. It was achieved good removal efficiency range from (87.5) % to(97.5) % at the high adsorbent dose .To calibrate the physical model, a computer program of multiple regressions is used to assess the relative importance of the predicted variables. The partial correlations indicate that influent concentration of copper, surface loading (flow rate), and detention time are the most important variables while the size of limestone is not important as others.
Local scour is a primary reason for bridge collapse, presenting a complex challenge due to the numerous factors influencing its occurrence. The complexity of local scour increases with clay-sand beds, particularly in predicting scour depth, as empirical equations are inadequate for such calculations. This study aims to predict local scour around cylindrical bridge piers in clay-sand beds using an artificial neural network (ANN) model. The ANN model was developed using 264 observations from various laboratory experiments. Eight variables were included in the ANN model: clay fraction, pier diameter, flow depth, flow velocity, critical sediment velocity, sediment particle size, bed shear strength, and pier Reynolds number. Sensitivity and statistical analyses were conducted to evaluate the impact of each variable and the accuracy of the ANN model in predicting local scour depth in clay-sand beds. The findings indicate that the ANN model predicted local scour with high accuracy, achieving a mean absolute percentage error (MAPE) of 14.6%. All dimensional variables significantly influenced the prediction of local scour depth, particularly clay fraction and bed shear strength, which were identified as the most crucial parameters. Finally, the MAPE values for local scour depth calculated using empirical equations were significantly higher than those for the ANN model, leading to an overestimation of local scour depth by the empirical equations.
In recent years, a number of researchers have adopted the wet packing (WP) approach to design different types of concrete mixes. Particle grading is a key to the optimization of the wet compactness density; for that reason, all empty spaces that exist in between large-size particles need to be completely filled with particles of smaller size. Previously-conducted studies in this field have been focused on measuring the particle size distribution’s packing density (PD) of the of granular matrices is the purpose of investigating how to increase the PD of cementitious materials. Thus, literature lacks models capable of predicting the optimal PD value. The current study collected and analyzed 216 datasets in order to construct a model for accurate prediction of PD. The main datasets were organized into two categories: modeling datasets and validation datasets. To configure the model in the best way, a hybrid gravitational search algorithm-artificial neural network (GSA-ANN) was also developed in this study. The findings confirmed ANN as an effective alternative for measuring the ultimate PD of cementitious pastes. ANN provided high levels of accuracy, practicality, and effectiveness in the process of predicting the PD value. Based on the final results, the implementation of the hybrid GSA-ANN technique causes a significant decrease in the number of tests conducted on experimental samples, which results in not only saving time and money, but also reducing the CO2 emission volume.