Hasil untuk "Water supply for domestic and industrial purposes"

Anwar Assefa Adem, Abebe Senamaw, Mulatie Mekonnen et al.

Abstract The northeastern region of Ethiopia faces significant water scarcity challenges, including drought, repeated crop failures, food insecurity, and famine. To address this issue, water harvesting has emerged as a highly viable approach. This study aimed to identify potential sites for water harvesting practices in the moisture-stressed areas of the North Wollo Zone, Ethiopia. The site selection process considered various factors, including topography (slope), hydrology (rainfall, drainage density, and runoff), soil (texture and depth), agronomy (land use and cover), and socioeconomic factors (proximity to roads). An Analytical Hierarchical Process (AHP) and weighted overlay analysis were employed as the geospatial-based multicriteria decision-making methods. The results showed that less than 1% (13.8 km2) of the study area was highly suitable, while 39.3% (4,802.6 km2) was classified as moderately suitable for water harvesting practices. These moderately suitable areas present promising opportunities for installing water harvesting structures to benefit local communities. However, a significant portion of the study area, 60.2% (7,348.7 km2), was only marginally suitable. Verification of existing water harvesting structures revealed that 74% (28 out of 38) were located in moderately suitable areas, while the remaining 26% were in marginally suitable areas, indicating the community’s adaptive use of available land. The findings highlight that integrating geospatial and multicriteria approaches can effectively guide sustainable water resource planning in drought-prone regions. Future studies should incorporate additional socioeconomic parameters and higher-resolution datasets to refine the identification of suitable water harvesting sites and support evidence-based watershed management strategies.

Amir Hossein Shakarami, Laleh Divband Hafshejani, Parvaneh Tishehzan et al.

This research explored the root causes of hidden pollution and key factors affecting spatial changes, as well as identifying the best inputs for water quality modeling. The study used principal component analysis (PCA), artificial neural network models (MLP), gene expression programming (GEP), and support vector machine (SVM) to achieve its objectives. The dataset included 11 different parameters collected monthly over 10 water years (2012-2021) from the Zohreh River, Iran. Initially, PCA was applied to reduce parameters and calculate the Water Quality Index (WQI). Two input models (parameters before and after PCA) were then created using artificial intelligence to determine the most accurate model for predicting the WQI. The Kaiser-Meyer-Olkin measure (KMO) was 0.6524, indicating the dataset was suitable for factor analysis. Bartlett's sphericity test was also significant at the 0.05 alpha level. PCA identified five significant principal components, explaining 70.66% of the total variance. The combined SVM and PCA model showed the best prediction ability, with an R² of 0.889, RMSE of 0.052, and MAE of 0.038.

seyed reza mousvi, نادر برهمند, Akbar Ghanbari et al.

<p>Introduction: Considering the effects of building dams in downstream basins, the need to investigate their condition and effects is one of the important issues. In this study, the optimal form of concrete double-arched dams was investigated under various interactions against earthquakes.<br /><br />Methods: The volume of concrete used was considered as the goal function of the optimization problem and the design variables were the geometric parameters of the dam. The twenty geometrical parameters were investigated. First, the dam-water-foundation system was simulated using the finite element method. Then the optimization was done using the particle swarm optimization (PSO).<br /><br />Results: To check the performance of the method used to optimize arch dams, the Moropont dam was selected as a real structure and optimized under different conditions against the Centro earthquake. The calculation parameters of the PSO algorithm showed the proper performance of this algorithm.<br /><br />Conclusion: To check the random nature of the optimization algorithm, four independent executions were performed for the PSO method and their results were analyzed separately. The results showed that with the number of 10,000 analyzes, the volume of concrete used was equal to 346,000 m3.</p>

Hong Tu, Bihong Tian, Shunshun Chen et al.

Abstract In this study, we synthesized ten g-C3N4-based covalent organic frameworks (COFs) and identified CN-306 as the most effective catalyst for visible-light-driven hydrogen peroxide (H2O2) production. Systematic optimization revealed that increasing ethanol proportions in the reaction medium significantly enhanced H2O2 yield, achieving a remarkable production rate of 5352 μmol g−1h−1 with a surface quantum efficiency of 7.27% at λ = 420 nm. Intriguingly, mechanistic investigations uncovered that excessive generation of singlet oxygen (1O2) acts as a critical inhibitory factor, impeding H2O2 accumulation. Multimodal characterization techniques combined with density functional theory (DFT) calculations were employed to unravel the origin of CN-306’s superior performance. Theoretical analyses demonstrated that CN-306 exhibits enhanced electron-hole separation efficiency, attributed to its reduced energy gap between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), which facilitates photocarrier migration and suppresses detrimental recombination. Furthermore, this work elucidates the structure-function relationships governing site-specific functional group modifications in COFs and their profound influence on photocatalytic activity. These findings provide molecular-level insights into rational catalyst design for optimizing surface structures and advancing solar-driven H2O2 synthesis applications.

Jing Zhang, Pei Li, Junfeng Wan et al.

Proper management of nitrogen-containing pig manure is crucial to realize its benefits of supporting plants-grow as fertilizer while minimizing its impact on the environment and climate change. Dry collection, rinsing and water submerging are manure cleaning techniques adopted in different types of pig farms and in different regions. As the first step of manure management, manure cleaning technique affects manure generation and nitrogen flow in the subsequent treatment and utilization processes. This short communication is to discuss different manure cleaning techniques and their impacts on nitrogen flow through pig manure management processes. Reducing nitrogen losses should focus on solid manure treatment such as composting when manure is dry collected. More diversified pathways of nitrogen losses are possible when manure is cleaned using water submerging technique. It is thus needed to develop proper and specific nitrogen management strategies and technologies, taking into account the manure cleaning technique adopted in pig farms.

Jaxson Brown, Duc-Son Pham, Sie-Teng Soh et al.

Industrial Control Systems (ICSs) are complex interconnected systems used to manage process control within industrial environments, such as chemical processing plants and water treatment facilities. As the modern industrial environment moves towards Internet-facing services, ICSs face an increased risk of attacks that necessitates ICS-specific Intrusion Detection Systems (IDS). The development of such IDS relies significantly on a simulated testbed as it is unrealistic and sometimes hazardous to utilize an operational control system. Whilst some testbeds have been proposed, they often use a limited selection of virtual ICS simulations to test and verify cyber security solutions. There is a lack of investigation done on developing systems that can efficiently simulate multiple ICS architectures. Currently, the trend within research involves developing security solutions on just one ICS simulation, which can result in bias to its specific architecture. We present ICS-SimLab, an end-to-end software suite that utilizes Docker containerization technology to create a highly configurable ICS simulation environment. This software framework enables researchers to rapidly build and customize different ICS environments, facilitating the development of security solutions across different systems that adhere to the Purdue Enterprise Reference Architecture. To demonstrate its capability, we present three virtual ICS simulations: a solar panel smart grid, a water bottle filling facility, and a system of intelligent electronic devices. Furthermore, we run cyber-attacks on these simulations and construct a dataset of recorded malicious and benign network traffic to be used for IDS development.

Saleh Baqer, Theodoros P. Horikis, Dimitrios J. Frantzeskakis

In this work, we investigate non-classical wavetrain formations, and particularly dispersive shock waves (DSWs), or undular bores, in systems exhibiting non-convex dispersion. Our prototypical model, which arises in shallow water wave theory, is the extended Korteweg-de Vries (eKdV) equation. The higher-order dispersive and nonlinear terms of the latter, lead to resonance between dispersive radiation and solitary waves, and notably, the individual waves comprising DSWs, due to non-convex dispersion. This resonance manifests as a resonant wavetrain propagating ahead of the dispersive shock wave. We present a succinct overview of the fundamental principles and characteristics of DSWs and explore analytical methods for their analysis. Wherever applicable, we demonstrate these concepts and techniques using both the classical KdV equation and its higher-order eKdV counterpart. We extend the application of the dispersive shock fitting method and the equal amplitude approximation to investigate radiating DSWs governed by the eKdV equation. We also introduce Whitham shock solutions for the regime of traveling DSWs of the eKdV model. Theoretical predictions are subsequently validated against direct numerical solutions, revealing a high degree of agreement.

Qiyu Chen, Huiyuan Luo, Haiming Yao et al.

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.

Harshita Jain

Abstract This work examines how graphene-based nanoparticles can be integrated into membranes to improve the effectiveness of water treatment in osmotic desalination processes. This is important since sustainable practices can help address the world's water scarcity. Water treatment, desalination, and resource recovery are areas where osmotic desalination shows great potential. However, membrane performance constraints frequently impede its efficacy. High mechanical strength, superior hydrophilicity, and the ability to lessen internal concentration polarisation are just a few of the remarkable qualities that make graphene-based nanoparticles stand out. In order to increase the membranes' overall functionality, these nanoparticles were created and added to them. Comparing the study to conventional membranes, the main goals were to increase water flux rates and salt ion rejection capacities. It was shown by experimental results that the membranes strengthened with graphene-based nanoparticles performed better. They outperformed conventional membranes in terms of water flow growth and salt ion rejection rates improvement. In order to advance osmotic desalination technologies towards more effective and sustainable water treatment options, this study highlights the revolutionary potential of graphene-based nanoparticles. Graphene-based nanoparticles provide an attractive option for tackling major water issues worldwide by improving membrane characteristics that are essential for osmotic desalination, such as permeability and selectivity. Water management techniques that are environmentally sustainable are supported by their integration into membranes, which also enhances performance metrics. This study opens the door for creative approaches to resource recovery and water treatment by providing important insights into the creation of cutting-edge materials specifically designed for osmotic desalination applications.

Jason M Unrine, Nina McCoy, W Jay Christian et al.

To increase our understanding of the factors that influence formation of disinfection byproducts (DBPs) in rural drinking systems, we investigated the spatial and seasonal variation in trihalomethane (THM) and haloacetic acid (HAA) concentrations in relation to various chemical and physical variables in a rural public drinking water system in Martin County, Kentucky, USA. We collected drinking water samples from 97 individual homes over the course of one year and analyzed them for temperature, electrical conductivity, pH, free chlorine, total chlorine, THMs (chloroform, bromodichloromethane, dibromochloromethane, dichlorobromomethane, and bromoform) and HAAs (monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, bromoacetic acid, and dibromoacetic acid). Spatial autocorrelation analysis showed only weak overall clustering for HAA concentrations and none for THMs. The relationship between modeled water age and TTHM or HAA5 concentrations varied seasonally. In contrast, there was strong variation for both HAA and THMs, with concentrations of HAA peaking in mid-summer and THMs peaking in early fall. Multiple regression analysis revealed that THM concentrations were strongly correlated with conductivity, while HAA concentrations were more strongly correlated with water temperature. Individual DBP species that only contained chlorine halogen groups were strongly correlated with temperature, while compounds containing bromine were more strongly correlated with conductivity. Further investigation revealed that increased drinking water conductivity associated with low discharge of the Tug Fork River, the source water, is highly correlated with increased concentrations of bromide. Discharge and conductivity of the Tug Fork River changed dramatically through the year contributing to a seasonal peak in bromide concentrations in the late summer and early fall and appeared to be a driver of brominated THM concentrations. Brominated DBPs tend to have higher toxicity than DBPs containing only chlorine, therefore this study provides important insight into the seasonal factors driving risk from exposure to DBPs in rural drinking water systems impacted by bromide.

Esrat Farhana Dulia, Syed A. M. Shihab

Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence mass eVTOL production with our model, enabling them to make optimal decisions and profits even under potential disruptions.

Jihu Lei

This study investigates the efficient strategies for supply chain network optimization, specifically aimed at reducing industrial carbon emissions. Amidst escalating concerns about global climate change, industry sectors are motivated to counteract the negative environmental implications of their supply chain networks. This paper introduces a novel framework for optimizing these networks via strategic approaches which lead to a definitive decrease in carbon emissions. We introduce Adaptive Carbon Emissions Indexing (ACEI), utilizing real-time carbon emissions data to drive instantaneous adjustments in supply chain operations. This adaptability predicates on evolving environmental regulations, fluctuating market trends and emerging technological advancements. The empirical validations demonstrate our strategy's effectiveness in various industrial sectors, indicating a significant reduction in carbon emissions and an increase in operational efficiency. This method also evidences resilience in the face of sudden disruptions and crises, reflecting its robustness.

Mykhailo Koshil, Tilman Wegener, Detlef Mentrup et al.

Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. The task is to identify the presence of an anomaly given an image, e.g., a missing component on an image of a circuit board, for subsequent manual inspection. While industrial anomaly detection has seen a surge in recent years, most anomaly detection methods still utilize knowledge only from normal samples, failing to leverage the information from the frequently available anomalous samples. Additionally, they heavily rely on very general feature extractors pre-trained on common image classification datasets. In this paper, we address these shortcomings and propose the new anomaly detection system AnomalousPatchCore~(APC) based on a feature extractor fine-tuned with normal and anomalous in-domain samples and a subsequent memory bank for identifying unusual features. To fine-tune the feature extractor in APC, we propose three auxiliary tasks that address the different aspects of anomaly detection~(classification vs. localization) and mitigate the effect of the imbalance between normal and anomalous samples. Our extensive evaluation on the MVTec dataset shows that APC outperforms state-of-the-art systems in detecting anomalies, which is especially important in industrial anomaly detection given the subsequent manual inspection. In detailed ablation studies, we further investigate the properties of our APC.

Nusrat Zahan, Yasemin Acar, Michel Cukier et al.

Cyber attacks leveraging or targeting the software supply chain, such as the SolarWinds and the Log4j incidents, affected thousands of businesses and their customers, drawing attention from both industry and government stakeholders. To foster open dialogue, facilitate mutual sharing, and discuss shared challenges encountered by stakeholders in securing their software supply chain, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) organize Secure Supply Chain Summits with stakeholders. This paper summarizes the Industry Secure Supply Chain Summit held on November 16, 2023, which consisted of \panels{} panel discussions with a diverse set of \participants{} practitioners from the industry. The individual panels were framed with open-ended questions and included the topics of Software Bills of Materials (SBOMs), vulnerable dependencies, malicious commits, build and deploy infrastructure, reducing entire classes of vulnerabilities at scale, and supporting a company culture conductive to securing the software supply chain. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain.

S. K. Ray

Valeria Kandou, J. Janga, Gaurav Verma et al.

Mismanagement of existing water supplies is threatening the sustainability of these resources, resulting in the degradation of source water quality and decreasing water supplies. Notably, substantial amounts of source water are withdrawn and treated for industrial use, irrigation, thermoelectric power, and mining purposes; only a small portion is allocated for municipal and domestic purposes. The increasing incidence of extreme weather events further accentuates both water quality and quantity concerns, stressing the need for increased efficiency to improve water supply sustainability. To that end, substituting source water with recycled water can help improve water supply sustainability in many places across the United States and the rest of the world. In this article, we focus on the case of Joliet, a town in Illinois where the supporting aquifer verges on the point of nonviability. To assist Joliet, Chicago's City Council approved a plan for the Chicago Department of Water Management to reallocate Lake Michigan drinking water from the Chicago basin to Joliet. In this study, we evaluate the environmental sustainability of four possible scenarios: 1.) proposed water-use cycle from the water treatment process, with delivery to all users, 2.) supplying recycled water to industries and other potential non-potable uses without additional treatment, 3.) supplying recycled water treated with additional disinfection to industrial and other non-potable uses, and 4.) supplying recycled water from an alternative water reclamation plant with an existing disinfection unit. We perform a life cycle assessment to compare the environmental impacts in each scenario. Findings of the study show that treated wastewater in industrial applications would significantly reduce environmental emissions compared to the proposed no-reuse scenario. The results suggest that water reuse can help save energy, reduce potable water consumption, generate revenue, and reduce the environmental load on water bodies.

Tran Thi Tuyen, Nadhir Al-Ansari, Dam Duc Nguyen et al.

Abstract Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

G. Mazzolani, F. G. Ciliberti, L. Berardi et al.

Water Supply Systems are essential infrastructures for the socio-economic life of urban cities. To improve their reliability, water utilities undertake several short- and long-term operational tasks based on technical and economic constraints. These activities are motivated by many factors, including increasing leakage rates due to infrastructure aging, increased consumer demands and need for sustainable use of water and energy. European and national regulatory bodies have promoted investment programs for allowing water utilities to reach common standards of reliability and quality of service among countries. Targets of management and operational achievements are usually measured using specific performance indicators. The Italian Regulatory Authority for Energy, Networks and Environment (ARERA) recently introduced the Regulation of the technical performances of water utilities. Performances on leakage management and investment plans of the utilities are thus based on two indicators named M1a (linear leakage index) and M1b (percentage leakage index). This paper analyzes in details the inconsistencies of the percentage leakage index (M1b), mainly due to its mathematical formulation and the ambiguity of defining water consumption as part of the total system inflow. The discussion is supported by a real case study, where both indicators have been calculated to assess their impact on management decisions and investment plans. The inconsistencies of the percentage leakage index are further demonstrated for various layouts of water supply systems.

Sepideh Bahadoripour, Ethan MacDonald, Hadis Karimipour

The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

Saeedeh Naseri, Javad Zahiri, Ahmad Jafari

Scouring is a natural phenomenon that occurs as a result of the erosive action of water flow in alluvial waterways. This phenomenon is considered a serious threat to the stability of structures located in the flow path, such as the foundations of bridges. One of the most important and effective factors in the destruction and failure of bridges is scouring around bridge foundations and supports. Today, with the progress of science and technology, the use of intelligent computer systems for modeling complex and non-linear phenomena has become increasingly important. In this research, using real data, the efficiency of artificial intelligence systems, which include a combination of multilayer perceptron neural network and genetic algorithm, has been investigated. Among the models with different number of hidden neurons, the artificial neural network with three hidden neurons has the least error. Comparing the values ​​of the difference ratio between the proposed neuro-genetic model and the existing common equations shows that the accuracy of the neuro-genetic model has a higher efficiency compared to other equations. The root mean square error in the proposed model was calculated as 0.51, while this value was calculated above 0.89 for the existing experimental equations.