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Finding the most suitable closing law is essential to decrease the shock wave pressure caused by transient flow and minimize the potential damage to equipment. The closure of a valve can occur instantly, rapidly, or gradually, and the appropriate law can be convex, linear, or concave, depending on various factors. These factors include the pipe's characteristics (type, diameter, roughness, and length), the conveyed fluid (nature and temperature), and operating conditions (pressure and flow rate). Other factors that receive less attention, such as the duration of slow closure and the impact of soil load on the pipe, are also considered in this study. The main focus of this article is to investigate how the optimal law evolves based on the time it takes for a valve to gradually close, specifically in the case of a valve located at the end of an underground gravity supply pipe. The findings reveal that when the slow closure time (t) exceeds 0.50 times the return period (t4), the exponent of the optimal law becomes a damped periodic function. Each closure time corresponds to a unique optimal law, and as the valve closure time increases, the range of optimal laws becomes narrower.

This paper provides the first survey and assessment of the composition of bottled waters (BW) of Maghreb Arab countries. Parameters reported on labels of 74 (BW) brands were used as datasets. According to the Maghreb, EEC and WHO legislations and using PCA, HCA, KMC and ANOVA analysis in conjunction with analytical and empirical approaches, the study discussed the water quality and classification. The results showed that (BW) constituents comply with natural mineral (MW), spring (SW) and table waters (TW) standards for human consumption. It appears that Ca-HCO3 is the dominant facies in Algerian and Tunisian MW but in Morocco, they are Ca-HCO3 and Na-HCO3 facies. All Algerian and majority of Moroccan and Tunisian SW are Ca-HCO3 type, while both Tunisian and Moroccan TW are mainly Na-Cl type. Some of Maghreb BW are sulfated, chlorinated, bicarbonated, containing calcium, sodium and fluoride and adapted to a low sodium diet. Classification showed that BW could be categorized into four different groups. The first includes five brands of MW, rich in salts with Na + K-Cl facies. Meanwhile, two facies mark the waters of the second (Ca + Mg-SO4 and Ca + Mg-Cl), whereas the waters of the third and fourth are essentially low in salts and marked by Ca + Mg-HCO3 facies.

The present study offers a quality assessment of the mineral and spring waters marketed in Algeria within the national and international legislations, examine the potential contribution of bottled waters to essential elements intake and effects on public health based on empirical, graphical tools, multivariate statistical techniques and (DRI) system. The study covered a dataset of 30 mineral and 33 spring brands. The parameters included, from bottle labels, were of physicochemical nature. All brands comply with national and WHO norms for the bottled waters, except for (Brand#63) in which NO2− exceeded the maximum permissible limit for mineral water and (Brands#4 and #21) where TH and TDS exceeded the Algerian recommended guidelines for spring water. Nearly 5% of the total brands were of bicarbonate nature belonging to mineral water, while 25% of all brands were suitable for low-sodium-diet. PCA and HCA showed that bottled waters could be classified into two distinct groups, according to degree of mineralization. The DRI-system revealed that Algerian bottled waters contributed substantially to the daily intake for Mg2+ with up to (63%), Na+ (40.36%) and Ca2+ (36%) for spring water for different ages and genders, whereas mineral water exceeded the maximum recommended daily intake for Ca2+ (128%) and Na+ (148.36%) for adults.

The water hammer phenomenon, characterized by transient pressure surges due to rapid fluid deceleration in pipelines, poses significant risks to hydraulic systems. Valve closure time is a critical parameter influencing pressure magnitude, necessitating precise calibration to ensure system safety. While numerical methods like the MacCormack scheme provide accurate solutions, their computational intensity limits practical applications. This study addresses this limitation by proposing a machine learning (ML) framework employing a multilayer perceptron (MLP) artificial neural network (ANN) to predict optimal pressure values—defined as the lowest maximum pressure obtained for several closure laws at a given closure time—corresponding to specific valve closure times. The ANN was trained on 637 simulations generated via the MacCormack method, which solves the hyperbolic partial differential equations governing transient flow in a reservoir-pipeline-valve (RPV) system. Performance evaluation metrics demonstrate the ANN’s exceptional robustness and accuracy, achieving a root mean square error (RMSE) of 0.068, Nash-Sutcliffe efficiency (NSE) of 0.99, and a correlation coefficient (R) of 0.99, with a maximum relative error below 1%. The results highlight the ANN’s superior predictive accuracy and flexibility in capturing complex transient flow dynamics, outperforming conventional numerical methods. Notably, the ANN reduced computational time from days for iterative simulations to mere seconds, enabling rapid prediction of pressure-time curves critical for real-time decision-making. This framework offers a computationally efficient and reliable alternative for optimizing valve closure strategies, mitigating water hammer risks, and enhancing pipeline safety. By bridging numerical rigor with machine learning, this work enhances hydraulic infrastructure resilience across industrial and urban networks.

The objective of this research was to arrive at a better assessment of the quality of surface water in the Constantine region. The focus was on the comparison of three classical indices WQINSF (National Sanitation Foundation Water Quality Index), WQICCME (Canadian Council of Ministers of the Environment Water Quality Index) and WQIAP (weighted arithmetical Water Quality Index), the development of a new index and the prediction by ANN (artificial neural network) of WQI indices. The principal components analysis (PCA) allows the selection of 10 parameters to be used in the calculation of the classical WQI, and eight principal components to be used as input for the new proposed index (regularized WQI). However the ANN is applied for the search for prediction models of classical WQI and developed WQI. The results show that the WQIAP index assesses water quality better, and that the regularized WQI further promotes the assessment of water quality. WQIR shows that, after the pollution peak, the water quality does not return to its initial state. The modeling approach by ANN offers an effective alternative to predict the WQI, it subsequently appears that the ANN predicts the new index WQIRregularized (R2 = 0.999) better than the classic model WQIAP (R2 = 0.99).

Water hammer refers to pressure fluctuations caused by changes in fluid speed in hydraulic systems. This research proposes a model using the MacCormack method to study and control water hammer, specifically focusing on managing valve closure to reduce shock wave pressure during transient flow. Numerical simulations compare linear and quadratic closure laws for both rapid and gradual valve closure, with the goal of identifying the optimal laws. The findings show that with faster closure times, the overpressure at the valve section decreases linearly in the second quarter of the return period (t4). The application of the quadratic law results in reduced pressures at the valve compared to the linear law, with a maximum pressure difference of 0.05 bar between the highest and lowest values. When searching for the optimal valve closure law to mitigate overpressure, it is found that the exponent ‘m’ falls within the range of 1.117–1.599. As the slow closing time increases gradually, the range of variation for ‘m’ decreases. Furthermore, in the case of underpressure prevention, the exponent ‘m’ ranges from 0.95 to 1.419, and the range of ‘m’ remains relatively constant with the increase in closing time.

The study presents a new hybrid model, called MARS-WOA, which predicts the impact of three-dimensional (3D) and suction-induced effects on soil slope stability. The MARS-WOA model combines the Multivariate Adaptive Regression Spline (MARS) with the Whale Optimization Algorithm (WOA) and applies it to four slope stability datasets. These include 2D and 3D datasets to evaluate the 3D effect in saturated soil slopes and NS (no suction) and WS (with suction) datasets to assess the suction-induced effect in unsaturated soil slopes. The MARS-WOA model demonstrated superior predictive modeling capability and performance compared to two other machine learning models, Support Vector Regression (SVR) and Ensemble Boosting Trees (EBT). This was evidenced by the impressively low Root Mean Squared Error (RMSE ≤ 0.04472) and high R-squared (R 2  ≥ 0.93) values achieved by the MARS-WOA model across all scenarios. The relative importance analysis indicates that the ratio B/H, representing the 3D effect, moderately influences slope stability design, with a relative importance (RI) value of 15.41%. Similarly, the ratio δαn, which indicates the suction-induced effect, moderately contributes to the slope stability model, with an RI value of 15.73%. These findings suggest that the MARS-WOA model is valuable for soil slope stability analysis and design researchers. The model provides valuable insights into the critical factors affecting slope stability, enabling the creation of more dependable slope designs.