Hasil untuk "Water supply for domestic and industrial purposes"

Ahmed Shalby, Sobhy R. Emara, Mohammed R. Elmenshawy et al.

Abstract Water scarcity poses major constraints to sustainable rural development, particularly in arid regions. In Egypt, limited freshwater resources are increasingly prioritized for domestic use, compelling proposed large-scale land reclamation projects to rely on brackish groundwater. However, marginal water quality restricts cultivation to salt-tolerant crops, undermining the long-term profitability of ongoing agribusiness activities. This study is the first to evaluate the techno-economic viability of integrating decentralized desalination systems into the Moghra development area. A systematic hydrochemical assessment of 73 wells, using the Irrigation Water Quality Index (IWQI), classified 49 as “Severe Restriction” and 24 as “High Restriction”, confirming widespread concerns about groundwater suitability. A two-stage reverse osmosis (RO) desalination system powered by photovoltaic (PV) energy was designed to achieve a 70% recovery rate. An optimization model identified blending ratios that maximize post-treatment water quality while minimizing the desalinated water volume. Results showed substantial improvements: the average sodium adsorption ratio (SAR) decreased by 66%, and IWQI increased from 34 to 77. Consequently, 68 wells were reclassified as “Low Restriction” and 5 as “Moderate Restriction”, enabling a shift from salt-tolerant olives to higher-value crops (e.g., wheat–maize rotation). A cost–benefit analysis assessed trade-offs between desalination costs and resulting economic returns. Under the abstraction limit, the proposed RO–PV blending strategy yielded a 35% higher net present value (NPV) and a 15.7% internal rate of return (IRR), demonstrating both technical and financial viability. These findings provide actionable insights for policymakers, stakeholders, and investors to enhance water productivity and agricultural sustainability in arid regions.

Taras V. Gerya, Nickolas M. Bardi, Shun-ichiro Karato et al.

The spinel phase (wadsleyite, ringwoodite) in the mantle transition zone (MTZ), can contain up to 1-2 weight percent of water. However, whether these water reservoirs in the MTZ are filled is debated. Here, we investigate water dynamics in the MTZ numerically by using a newly developed empirical model of deep hydrous mantle melting combined with 2D thermo-hydro-mechanical-chemical (THMC) upper mantle models. Numerical modeling results suggest that water-induced buoyancy triggers the development of hydrous solid-state mantle upwellings in the MTZ. On time scales of some tens of millions of years, they rise to and interact with the spinel-olivine phase transition. Depending on the water content and temperature of these thermal-chemical plumes, this crossing may trigger hydrous melting by water release from the wadsleyite upon its conversion to olivine. The melts are less dense than the solid matrix and continue rising upward in the form of either diapirs or porosity waives. Similar dehydration-induced melting process3 is also documented for the lower MTZ boundary, where hydrous downwellings (such as subducted slabs) generate buoyant melt diapirs rising through the MTZ. We therefore suggest that the MTZ operates as a transient water reservoir. Relatively small amounts of water (less than 0.1 weight percent, smaller than 0.2 ocean masses) and a geologically moderate duration (80-430 Myr) of the transient water storage should be characteristic for the MTZ, which may play a key role in stabilizing the surface ocean mass on Earth and Earth-like rocky exoplanets.

Mahamed Omer Yusuf, Dahir Yosef Abdi

Among various possible options to address water scarcity problems in the Somali region, the use of hand-dug shallow Wells for extracting groundwater holds considerable importance. Nevertheless, its actual utilization has not met expectations. This study aimed to assess determinants for the use of hand-dug wells as a source of irrigation and domestic water consumption in the Harawa district. Mixed (quantitative and qualitative) approach was used for the data collection. Descriptive statistics and Binary Logistic regression model were applied for the analysis. From the fourteen explanatory variables fitted in binary logistic model, education status, average distance to water sources, labor availability, training, and farming experience showed statistically positive significance (p < 0.05) effect on the agro-pastoralists’ decision to use hand dug shallow, where, the age and livestock ownership variables were found to have a significant (p < 0.05) negative relationship with the utilizations of the HDSWs. In conclusion, targeted interventions addressing these key factors could substantially promote the utilization status of HDSWs, thereby increasing water access and supporting sustainable livelihoods in agro-pastoral communities

Maiwand Omary

Pollution and over-exploitation of groundwater aquifers in Kabul Province have led to deteriorated water quality and decreased quantity. In its turn, these have caused elevation of physical, chemical, and biological contaminant concentrations in the province’s groundwater beyond national and international drinking water quality standards. The article describes the outcomes of a pilot photovoltaic reverse osmosis (PV-RO) system for drinking water treatment in Qala-e-Gulbaz Village, Kabul Province, Afghanistan. The main system’s components include PV solar panels, three dosing pumps, two alternative current pumps, two alternative-to-direct-current inverters, sand and carbon filters, two sediment filters, one ultra-violet filter, two water flow meters, and a complete water supply distribution system. The study was assisted by the Danish Committee for Aid to Afghan Refugees (DACAAR) ground monitoring well (GMW) system; quality analysis of different physical, chemical, and biological parameters of raw and filtered water samples; measuring filtered water and wastewater flow rates; determining associated limitations and pressures; as well as valuating different related expenses during cost per capita calculations. Water quality testing has shown the system’s removal efficiency of 97% for salinity, 97% for total dissolved solids, above 90% for other chemicals (anions & cations), 65.52% for turbidity, and 100% for bacteria. The plant’s mean quantity efficiency has amounted to 50%. The study highlights certain key challenges including high initial cost, significant wastewater volume, and need for professional operators leading to high operational and maintenance expenditures, with the initial system’s cost per capita of USD79.54. Therefore, under the condition of optimizing efficiency, improving wastewater management, reducing original costs, implementing training programs, and developing the necessary policy framework PV-RO water treatment systems represent a viable alternative.

Zhaozhou Peng, Jiangyuan Sun, Panpan Li et al.

Abstract High levels of nitrates and nitrites not only threaten aquatic ecosystems and drinking water safety but also impair the biodegradation efficiency of industrial wastewater. In this study, micro-nano-MoS2-1013 (0.04 g/L) enhanced denitrification by 56.9% and 29.6% in steel pickling and meat processing wastewaters, respectively, and improved chemical oxygen demand (COD) removal by 136.7% in refinery cooling wastewater under continuous-flow conditions using a 3.5 L upflow anaerobic sludge blanket (UASB) reactor. The catalytic effect of micro-nano-MoS2 on denitrification was achieved by stimulating an increase in the abundance of denitrification genes and the transcript levels of narL (93.82 times), narG (16.34 times), and nirK (12.27 times) within the bacterial cells, which led to an increase in the expression levels of denitrifying enzymes. These findings have significant implications for the design and optimization of biodegradation processes and bio-denitrification systems, particularly for the treatment of high-concentration nitrate wastewater.

Saeed Rajabi, Fahimeh Ahmadian, Sobhan Maleky et al.

Abstract Oily wastewater from machining processes poses significant environmental challenges due to its high organic load and stable emulsions, rendering conventional treatment methods ineffective. This study investigated the optimization of the coagulation/flocculation process for treating machining oily effluent using response surface methodology (RSM). Systematic optimization of the impact of critical parameters, including FeCl3 dosage, pH, temperature, and reaction time, was conducted to achieve the optimum chemical oxygen demand (COD) removal efficiency. Under 2.5 g/L FeCl3, pH of 7, 25 °C temperature, and 30 min reaction time, optimal conditions, a maximum 80.42% COD reduction was achieved. The RSM model demonstrated good prediction ability, reflecting strong interactions between the process variables and predicting the efficiency of the treatment. Unlike conventional coagulation procedures, this optimized approach raises the pollutant removal rate while reducing chemical consumption and sludge generation, making it a greener industrial option. The general study also indicated the influence of wastewater components on defining coagulation efficiency, requiring process versatility in different industrial settings. Such findings form part of developing cost-effective, environmentally friendly wastewater treatment technologies consistent with legislative discharge requirements. Future research must focus on further integrating this method with advanced treatment technologies to improve overall efficiency and sludge management.

Yujie Tang, Meiling Wang, Yinan Deng et al.

In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot must capture and update scene changes and plans continuously. However, current object navigation approaches primarily focus on the semantic level and lack the ability to dynamically update scene representation. In contrast, this paper captures the relationships between frequently used objects and their static carriers. It constructs an open-vocabulary Carrier-Relationship Scene Graph (CRSG) and updates the carrying status during robot navigation to reflect the dynamic changes of the scene. Based on the CRSG, we further propose an instance navigation strategy that models the navigation process as a Markov Decision Process. At each step, decisions are informed by the Large Language Model's commonsense knowledge and visual-language feature similarity. We designed a series of long-sequence navigation tasks for frequently used everyday items in the Habitat simulator. The results demonstrate that by updating the CRSG, the robot can efficiently navigate to moved targets. Additionally, we deployed our algorithm on a real robot and validated its practical effectiveness. The project page can be found here: https://OpenIN-nav.github.io.

Maxim Trushin, Daria V. Andreeva, Francois M. Peeters et al.

Water, a subject of human fascination for millennia, is likely the most studied substance on Earth, with an entire scientific field -- hydrodynamics -- dedicated to understanding water in motion. However, when water flows through one-dimensional or two-dimensional channels, its behavior deviates substantially from the principles of hydrodynamics. This is because reducing the dimensionality of any interacting physical system amplifies interaction effects that are beyond the reach of traditional hydrodynamic equations. In low-dimensional water, hydrogen bonds can become stable enough to arrange water molecules into an ordered state, causing water to behave not only like a liquid but also like a solid in certain respects. In this review, we explore the relationship between water's ordering and its ability to flow in low-dimensional channels, using viscosities of bulk water, vapor, and ice as benchmarks. We also provide a brief overview of the key theoretical approaches available for such analyses and discuss ionic transport, which is heavily influenced by the molecular structure of water.

Boyu Yao, Andrey Bernstein, Yury Dvorkin

Rising electricity demand underscores the need for secure and reliable generation expansion planning that accounts for upstream supply chain constraints. Traditional models often overlook limitations in materials, manufacturing capacity, lead times for deployment, and field availability, which can delay availability of planned resources and thus to threaten system reliability. This paper introduces a multi-stage supply chain-constrained generation expansion planning (SC-GEP) model that optimizes long-term investments while capturing material availability, production limits, spatial and temporal constraints, and material reuse from retired assets. A decomposition algorithm efficiently solves the resulting MILP. A Maryland case study shows that supply chain constraints shift technology choices, amplify deployment delays caused by lead times, and prompt earlier investment in shorter lead-time, low-material-intensity options. In the low-demand scenario, supply chain constraints raise investment costs by $1.2 billion. Under high demand, persistent generation and reserve shortfalls emerge, underscoring the need to integrate upstream constraints into long-term planning.

E. P. van Geest, B. Can, M. Makurat et al.

For commercial grade electronic devices, stable structures are required to ensure a long device life span. When such devices contain nanomaterials like graphene, it is crucial that these materials resist industrial processes and harsh environments. For environments that contain water, graphene delamination is a notorious drawback, as water intercalation and eventually liftoff readily occur in aqueous and especially in alkaline solutions. This limitation renders graphene incompatible with key wafer-processing steps in the semiconductor industry. In this work, a covalent pyrene-based adhesion layer is synthesized in a facile, two-step procedure. Through π-π interactions, the adhesion of graphene to silicon wafers was maintained under conditions that resemble harsh processes, i.e. acidic and alkaline solutions, several organic solvents, and sonication. Moreover, they could be produced with a device measurement yield up to 99.7% and reproducible device-to-device electronic performance on 4-inch silicon wafers. Our results show that a straightforward functionalization of silicon wafers with an adhesive layer can be directly applicable in industrial-scale fabrication processes, giving access to robust graphene field effect devices that are built to last long.

Seyed Alireza Hosseinitabar, Fatemeh Sabouhi, Ali Bozorgi-Amiri

William Delgado, Ana Córdova, Timothy Beach et al.

We perform an economic feasibility analysis for installing grid-connected solar arrays with batteries to power ten water supply, distribution, and treatment stations in Ciudad Juárez in tandem with the existing local grid. This article covers detailed results from the four stations that represent the topographical and geographical variability of the city and the variable electricity demand across sites. Using data from the city’s water utility and calculations from the HOMER and RETScreen models, we find contrasting results. The HOMER model finds that solar arrays with or without batteries are economically viable while RETScreen reaches the opposite conclusion. One key reason for this is the two models’ differing predictions on the amount of electricity the proposed solar arrays would supply. Further, having reliable battery storage is paramount to overcoming grid outages and the intermittent availability of solar power to ensure the success of solar projects. Global scientific consensus recognizes the need for more renewable energy. With a sunny-climate and its status as a manufacturing hub on the US-Mexico border, Ciudad Juárez has the potential to lead the way forward in promoting solar energy. The comparative results of the HOMER and RETScreen models provide a more complete understanding of the feasibility of solar power for Ciudad Juárez’s water infrastructure as well as vital insight into the factors that shape the success of solar projects.

Mohamad Javad Zoqi

In response to the increasing color pollution in industrial wastewaters and the need for effective treatment methods, this research investigates the application of the Fenton and microwave-enhanced Fenton processes for color removal. The aim of this study is to compare the effectiveness of these two processes and to identify the optimal conditions for the maximum removal of color pollutants. This study evaluates the efficiency of the Fenton and microwave-enhanced Fenton processes in dye removal from wastewater. To examine the impact of parameters such as pH, dye concentration, Fe2+, and H2O2 on the efficiency of removing the Reactive Red 198 dye and COD, these variables were altered during the experiments. The mechanism of dye molecule removal was analyzed through UV-Vis spectroscopy. The optimal conditions for the microwave-enhanced Fenton process included an initial dye concentration of 300 mg/l, a pH of 3.5, an Fe2+ concentration of 20 mg/l, and an H2O2 concentration of 100 mg/l. Under these conditions, a COD reduction of up to 85% and dye removal of over 98% were observed. The findings suggest that the microwave-enhanced Fenton process can effectively reduce color pollutants and improve the quality of industrial wastewater. This method offers high reaction speed and remarkable efficiency under various treatment conditions, making it a suitable solution for treating wastewater with high dye loads.

Bach Viet Do, Xingyu Li, Chaoye Pan

Operational disruptions can significantly impact companies performance. Ford, with its 37 plants globally, uses 17 billion parts annually to manufacture six million cars and trucks. With up to ten tiers of suppliers between the company and raw materials, any extended disruption in this supply chain can cause substantial financial losses. Therefore, the ability to forecast and identify such disruptions early is crucial for maintaining seamless operations. In this study, we demonstrate how we construct a dataset consisting of many multivariate time series to forecast first-tier supply chain disruptions, utilizing features related to capacity, inventory, utilization, and processing, as outlined in the classical Factory Physics framework. This dataset is technically challenging due to its vast scale of over five hundred thousand time series. Furthermore, these time series, while exhibiting certain similarities, also display heterogeneity within specific subgroups. To address these challenges, we propose a novel methodology that integrates an enhanced Attention Sequence to Sequence Deep Learning architecture, using Neural Network Embeddings to model group effects, with a Survival Analysis model. This model is designed to learn intricate heterogeneous data patterns related to operational disruptions. Our model has demonstrated a strong performance, achieving 0.85 precision and 0.8 recall during the Quality Assurance (QA) phase across Ford's five North American plants. Additionally, to address the common criticism of Machine Learning models as black boxes, we show how the SHAP framework can be used to generate feature importance from the model predictions. It offers valuable insights that can lead to actionable strategies and highlights the potential of advanced machine learning for managing and mitigating supply chain risks in the automotive industry.

Saeed Balahang, Masoud Ghodsian

Abstract This paper investigated the efficiency of the traditional weir equation (TWE), Domínguez, adjusted Domínguez, and Schmidt approaches, as an alternative to the De Marchi procedure, for computing discharge of a sharp-crested triangular side weir. Comprehensive experimental data were used for the analysis, including 342 data from the present study and 140 data from other sources. The effects of approach Froude number Fr1, the ratio of weir height to upstream flow depth p/y 1, and weir apex angle θ on the discharge coefficients obtained from different methods were studied. Sensitivity analysis using the partial swarm optimization-support vector regression method indicated that Fr1, p/y 1, and θ affect the discharge coefficients. It was found that Fr1 with sensitivity indices equal to 1.89, 3.74, and 4.04 has the most substantial effect on the De Marchi coefficient, TWE coefficient, and adjusted Domínguez coefficient; meanwhile, p/y 1 has the most significant impact on Domínguez coefficient and Schmidt coefficient with sensitivity index equal to 1.57. In addition, it was found that θ had the lowest sensitivity indices in estimating discharge coefficients. New equations for forecasting sharp-crested triangular side weir discharge coefficient were presented based on dimensional analysis. The new De Marchi coefficient executed better for calculating triangular side weir discharge than earlier De Marchi coefficients. Moreover, TWE, Domínguez, adjusted Domínguez, and Schmidt methods performed better than the De Marchi procedure (with MSE = 4.581) in calculating sharp-crested triangular side weir discharge. However, considering the simplicity of the TWE approach compared to other methods, this approach with R 2 = 0.975, NSE = 0.975, MSE = 3.610, MRE = 0.097, and CP10% = 71.36 was introduced as the superior procedure.

Rosario Iturbe-Argüelles, Alejandrina Castro-Rodríguez, José Antonio Mendoza-Millán et al.

Se propone una metodología de indicadores de sostenibilidad en acuíferos que describen cuantitativa y cualitativa cambios en el sistema. No existe en la bibliografía consultada otra metodología propuesta para evaluar la sostenibilidad de los acuíferos con todos los indicadores que aquí se proponen y con esta ponderación por escenarios, lo cual es la principal aportación de este trabajo, donde se muestra la evaluación de sostenibilidad para cualquier acuífero en el país. Los indicadores propuestos son los siguientes: 1) contribución del agua subterránea al flujo base de las corrientes superficiales; 2) suministro de agua; 3) descarga/recarga; 4) riesgo de contaminación del agua subterránea; 5) dependencia del agua subterránea; 6) calidad del agua subterránea; 7) crecimiento de ciudades; 8) vulnerabilidad acuífera; 9) intrusión salina y salinización, y 10) inversión en materia de agua. Es factible evaluar la sostenibilidad acuífera con una metodología empírica, sistemática y simple de indicadores con valores asignados a las variables involucradas. Los pesos fueron definidos por los autores al indicador de acuerdo con la relevancia sobre la sostenibilidad acuífera. Se proponen escenarios con valor numérico para cada indicador que muestren la condición del acuífero. La puntuación final del índice de sostenibilidad relaciona todos los indicadores. Este índice se clasificó en cuatro categorías: sostenible, moderadamente sostenible, baja sostenibilidad e insostenible. La evaluación se realizó a nivel nacional (653 acuíferos); los resultados muestran siete acuíferos sostenibles, 623 moderadamente sostenibles, 23 baja sostenibilidad y ninguno insostenible. Se debe poner mayor énfasis en sitios con índice de sostenibilidad menor, analizando los indicadores que le confieren tal categoría para mejorar su condición.

Yuan Luo, Song Han, Jingjing Liu

Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.

Anitabha Ghosh, Partha Pratim Adhikary, Biswajit Bera et al.

Abstract Shortage of potable water is a global problem, and this problem can be met by searching new areas where groundwater is available. GIS is an effective and necessary tool to identify groundwater potential zones in an area. In the present study, groundwater potential zones (GWPZs) were identified in the Kangsabati River basin of east India having an area of about 6488 km2 using multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP). The criteria like geology, geomorphology, elevation, slope, drainage, lineament, curvature, topographic wetness, land use/land cover, and soil were extracted from satellite data and the weights for each parameter and its sub-parameters were assigned through analytical hierarchy process based on their respective relevance as influencing factors for groundwater recharge. Very low, low, moderate, high, and very high groundwater potentiality represent 28.93%, 30.56%, 19.75%, 14.62%, and 6.11% area, respectively. The low-lying flat plains of the southeastern section, as well as the centrally located dam, are ideal for groundwater recharge, while the upland plain of the northwestern part, with its hard rock terrain, is less so. This outcome has been verified using pre-monsoon and post-monsoon groundwater depth data, indicating that the strategy is most appropriate for this region. Thus, the groundwater potential zone maps remain very useful for conducting extensive ground-based hydrogeological studies that facilitate the identification of suitable bore well/dug well sites.

Nafise Izadi, َAbbas khasheiSiuki, Mohsen PourrezaBilondi et al.

Given the growing population of the world especially in countries with limited water resources, investigating and controlling the quantity and quality of water resources especially groundwater resources which is the main source of drinking water supply, can help these communities in the face of the water crisis which will plague mankind in the near future. In this survey, first, 22 wells were selected from Birjand plain aquifer. Then the aquifer was studied using the Groundwater chemical Pollution Index (PIG) and ecological hazard index (ERI) of heavy metals using zoning maps and final maps of PIG and ERI indices were prepared. According to PIG, 56.74% of the aquifer has a lot of Pollution, and according to ERI, 0.91% of the aquifer has a lot of Pollution. Then, na ew index of Pollution and Environmental Risk of Groundwater (PERG) was introduced according to both Groundwater chemical Pollution Index and heavy metals Environmental Risk Index. PERG detects chemical pollution and heavy metals in the aquifer and leads to better summarization and decision-making in order to collect water from groundwater aquifers. In terms of PERG, aquifer pollution is 1.755% of the total area.

Swapan Talukdar, Javed Mallick, Showmitra Kumar Sarkar et al.

Abstract The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.