Hasil untuk "Water supply for domestic and industrial purposes"

Nynke Hofstra, Martina Flörke, Edward Jones et al.

Poor water quality negatively affects human health. Large-scale knowledge of present and future water quality and associated health impacts remains limited, hindering the development of effective policies and regulations to reduce water quality related health risks. This paper collates current knowledge on global water quality relevant for human health at present and in the future and identifies key knowledge gaps and ways forward. Six relevant water quality constituent groups include (1) microorganisms, (2) organic micropollutants, (3) heavy metals, metalloids and constituents of geogenic origin, (4) nitrate/nitrite, (5) salts/salinity, and (6) plastics. These have a variety of health impacts ranging from gastroenteritis to cancer. An analysis of monitoring data and approaches for past and future water quality and health risk demonstrates that data availability for the assessment of water quality impacts on human health is very limited, while integration of these approaches provides some opportunities to reduce knowledge and data gaps in this field. We call for an integrated, solution-oriented approach that should incorporate interactions between multiple constituents, study compound impacts on health, and focus on synergies and trade-offs of interventions that can improve human health. In addition, this approach should include human health alongside other impacts, such as animal health, aquatic ecosystems and food production. A holistic approach would provide more comprehensive understanding of present and future water quality and consequent human health impacts, which is a prerequisite for intervention strategies, and the realisation of sustainable development goals (SDGs) 6 for clean water and 3 on good health and well-being.

Mingjie Bi, Dawn M. Tilbury, Siqian Shen et al.

In recent years, the frequent occurrence of disruptions has had a negative impact on global supply chains. To stay competitive, enterprises strive to remain agile through the implementation of efficient and effective decision-making strategies in reaction to disruptions. A significant effort has been made to develop these agile disruption mitigation approaches, leveraging both centralized and distributed decision-making strategies. Though trade-offs of centralized and distributed approaches have been analyzed in existing studies, no related work has been found on understanding supply chain performance based on the network attributes of the disrupted supply chain entities. In this paper, we characterize supply chains from a capability and network topological perspective and investigate the use of a distributed decision-making approach based on classical multi-agent frameworks. The performance of the distributed framework is evaluated through a comprehensive case study that investigates the performance of the supply chain as a function of the network structure and agent attributes within the network in the presence of a disruption. Comparison to a centralized decision-making approach highlights trade-offs between performance, computation time, and network communication based on the decision-making strategy and network architecture. Practitioners can use the outcomes of our studies to design response strategies based on agent capabilities, network attributes, and desired supply chain performance.

Tanay Kumar, Hongying Zhao, Xuehua Zhang

Drying concentrated slurry waste is slow, particularly due to the entrapment and limited accessibility of water entrained between the particles in the slurry. A sailboat evaporator with a root-like structure is a new system that enables wind-assisted interfacial evaporation of concentrated particle slurries. In this work, we create access to the disconnected water pockets in concentrated slurry waste, facilitating faster water conduction and efficient evaporation at extremely high solid concentration. The evaporator's long roots effectively extracted water beneath 150 cm deep supernatant water layer. Through replantation of the evaporator to a separate location, an impressive evaporation rate (ER) of 4 kg/(m^2*h) close to 80 wt% solid concentration, a 25% increase to a non-replanted sample. Furthermore, long periods of efficient of evaporation was achieved even at high solid concentration through hydrodynamic flushing of roots. Outdoor experiments achieved substantial volumetric reduction, yielding dried residues with over 75 wt% solid concentration. These results underscore the system's reliable performance against highly concentrated slurries, yet to be by conventional industrial methods, including flocculation and tail-lift drying. The integration of renewable energy coupled with efficient enhancement techniques makes the sailboat evaporator a scalable and sustainable pathway for industrial wastewater dewatering.

Mostafaali Ayubirad, Zeng Qiu, Hao Wang et al.

In this paper, a predictive constraint-aware control scheme is formulated within the Command Governor (CG) framework for localized hydration management of a proton exchange membrane (PEM) fuel cell system. First, a comprehensive nonlinear dynamic model of the fuel cell system is presented which includes a pseudo 2-dimensional (P2D) model of the stack, reactant supply and cooling subsystems. The model captures the couplings among the various subsystems and serves as the basis for designing output feedback controllers to track the optimal set-points of the air supply and cooling systems for power optimization. The closed-loop nonlinear model is then used to analyze the dynamic behavior of membrane hydration near the anode inlet, the driest region of the membrane in a counter-flow configuration, under various operating conditions. A reduced-order linearized model is then derived to approximate hydration behavior with sufficient fidelity for constraint enforcement. This model is used within the CG framework to adjust the air supply set-points when necessary to prevent membrane dry-out. The effectiveness of the proposed approach in maintaining local membrane hydration while closely tracking the requested net power is demonstrated through realistic drive-cycle simulations.

Giuseppe Pagano Giorgianni

This paper examines how natural gas supply shocks affect Italian firms' pricing decisions and inflation expectations using quarterly survey data from the Bank of Italy's Survey on Inflation and Growth Expectations (SIGE). We identify natural gas supply shocks through an external IV-VAR approach exploiting likely unexpected news about interruption to gas supplies to Europe. Our findings show that although gas supply shocks do not have huge effects on gas quantity and only modest effect on gas inventories, they are quickly transmitted to spot electricity prices with persistent effects. We then estimate a proxy internalizing BVAR incorporating firm-level variables from SIGE, documenting that gas supply shocks raise firms' current and expected prices as well as inflation uncertainty. Finally, we uncover substantial nonlinearities using state-dependent local projections: under high inflation uncertainty, firms successfully pass cost increases on to consumers, sustaining elevated prices; under low uncertainty, recessionary effects dominate, leading firms to cut prices below baseline.

Aiswarya Pati, Showkat Ahmad Mir, Archana Padhiary et al.

Abstract Jeera River of Bargarh District, Odisha faces serious deterioration due to massive human intervention. It is particularly susceptible to degradation because it receives industrial and waste water emissions from surrounding organizations and municipal bodies. The river was formerly a flourishing tributary of the massive Mahanadi River that possessed excellent navigability, an array of aquatic ecosystems, and a well-established basin with an expanding agricultural sector. The current condition of the Jeera River is deplorable, leaving behind only minimal economic and ecological values. The study emphasizes analyzing the seasonal variation of the water quality rating of Jeera River in terms of the Water Quality Index (WQI). WAWQI (Weighed Arithmetic Water Quality Index) values show that almost all sampling sites have poor or unsuitable quality. During the monsoon season, the water quality deteriorated the most, with an average WQI score of 516.430 compared to pre- and post-monsoon with average WQI values of 154.558 and 276.014 respectively. CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) values indicate that water quality ranges from marginal, and poor to fair. This study concludes that out of the eight sampling sites, station 5 (Dumerpali) is observed to be the most polluted site. Many water quality parameters including iron, turbidity, nitrate, phosphate, E. coli, and Total coliform are found to exceed the permissible limits prescribed by WHO and BIS. Reducing sewage outflow, blocking direct stormwater discharge, and avoiding continuous solid garbage disposal by neighbouring populations are ways to improve river water quality. Graphical abstract

Hatem AbuEltayef, Khaldoun AbuAlhin, Khalil Alastal

Abstract In the realm of water resource management, the complex relationship between Non-Revenue Water (NRW) and Key Performance Indicators (KPIs) is a vital task for water resources management experts. KPIs serve as vital metrics for evaluating water utility efficiency, while NRW presents a substantial challenge, especially in water-scarce regions. This study emphasises the need to move beyond simplistic percentage-based NRW indicators, advocating for a more comprehensive approach that includes various performance indicators. The study aims to build a model to evaluate and quantify NRW based on selected performance indicators, supporting utilities in their pursuit of improved water loss management. The article explains how to collect, assess, classify, and select NRWPIs using the IWA framework. It then standardised these selected NRWPIs to cubic meters per year to enable fair comparisons. A comprehensive classification system based on value ranges aids in assessing performance. It provided an implementation plan, along with a monitoring and evaluation plan. The methodology includes collecting, assessing, classifying, and selecting data for a strong NRWPI model necessary for creating strategies to reduce water loss. The article highlights critical areas such as failures in water distribution pipelines, issues related to governmental and municipal services, utility premises, water mains pipelines, customer meter readings, and meter errors. These insights are used to construct a detailed action plan targeting these areas, with specific activities, timelines, and responsible bodies identified. In conclusion, this innovative approach NRWPIs, coupled with the strategic action plan and monitoring and evaluation framework, to drive substantial improvements in NRW reduction. Collaborative efforts among various departments and vigilant tracking of progress aim to optimize water distribution systems, reduce water losses, and enhance operational efficiency for sustainable water management.

Gashaw Gebey Addis, Sileshie Mesfin Leyew

Yichen Wang, Huanbo Zhang, Chunhong Yuan et al.

In the evolving digital landscape, network flow models have transcended traditional applications to become integral in diverse sectors, including supply chain management. This research develops a robust network flow model for semiconductor wafer supply chains, optimizing resource allocation and addressing maximum flow challenges in production and logistics. The model incorporates the stochastic nature of wafer batch transfers and employs a dual-layer optimization framework to reduce variability and exceedance probabilities in finished goods. Empirical comparisons reveal significant enhancements in cost efficiency, productivity, and resource utilization, with a 20% reduction in time and production costs, and a 10% increase in transportation and storage capacities. The model's efficacy is underscored by a 15% decrease in transportation time and a 6700 kg increase in total capacity, demonstrating its capability to resolve logistical bottlenecks in semiconductor manufacturing. This study concludes that network flow models are a potent tool for optimizing supply chain logistics, offering a 23% improvement in resource utilization and a 13% boost in accuracy. The findings provide valuable insights for supply chain logistics optimization.

Pietro Gravino, Giulio Prevedello, Emanuele Brugnoli

The digital age provides new challenges as information travels more quickly in a system of increasing complexity. But it also offers new opportunities, as we can track and study the system more efficiently. Several studies individually addressed different digital tracks, focusing on specific aspects like disinformation production or content-sharing dynamics. In this work, we propose to study the news ecosystem as an information market by analysing three main metrics: Supply, Demand, and Diffusion of information. Working on a dataset relative to Italy from December 2019 to August 2020, we validate the choice of the metrics, proving their static and dynamic relations, and their potential in describing the whole system. We demonstrate that these metrics have specific equilibrium relative levels. We reveal the strategic role of Demand in leading a non-trivial network of causal relations. We show how disinformation news Supply and Diffusion seem to cluster among different social media platforms. Disinformation also appears to be closer to information Demand than the general news Supply and Diffusion, implying a potential danger to the health of the public debate. Finally, we prove that the share of disinformation in the Supply and Diffusion of news has a significant linear relation with the gap between Demand and Supply/Diffusion of news from all sources. This finding allows for a real-time assessment of disinformation share in the system. It also gives a glimpse of the potential future developments in the modelisation of the news ecosystem as an information market studied through its main drivers.

Julien Feiguel, Mouhamed NDiaye, Pascal Chambaud et al.

Rehabilitating cast iron pipes through lining offers several advantages, including increased durability, reduced water leaks, and minimal disruption.This approach presents a cost effective and environmentally friendly solution by sealing cracks and joints, extending the pipeline's lifespan, and reducing water wastage, all while avoiding the need for trench excavation. However, due to the relining process, branch connections are sealed and need to be reestablished. To address the issue of rehabilitating small-diameter water pipes, we have designed a modular robot capable of traversing and working within 200 meter long, 100 mm diameter cast iron pipes. This robot is equipped with perception functions to detect, locate, and characterize the branch connections in cast iron pipes and relocate them after lining, as well as machining functions. A first prototype of this system has been developed and validated on an 8 meter long section, in a laboratory environment.

Vahid Esfandiyari, Shapour Zarifian, Amir Isanezhad et al.

The main challenges facing the agricultural sector in Sistan plain include scarcity of water resources, inadequate water rationing, and suboptimal cultivation patterns. Based on this, the main objective of this study was to determine the optimal cultivation pattern of agricultural crops by considering the virtual water and water footprint and assessing the impact on water consumption in this region. To achieve this objective, virtual water indicators and green, blue and gray water footprints of agricultural crops were calculated to quantify water consumption in the Sistan region. Then, considering the virtual water and water footprint, the optimal pattern of plant cultivation was determined using a fuzzy ideal programming model. The optimization model results were tested based on a system dynamics approach in the Vensim PLE8.3.5 software. The results showed that the virtual water content of agricultural products in this region was 2.69 m3/kg. The annual water requirement of agricultural crops was 1019 MCM, of which 62% is met. After optimizing the cultivation pattern, the water requirement reduced to 600 MCM per year, of which 89% is supplied. Additionally, the optimization of cultivation patterns increased water supply to the environmental sector from 138 to 240 MCM per year.

Weizi Chen, Zipeng Xing, Na Zhang et al.

Abstract Hierarchical Bi2Fe4O9/BiOI S-scheme nanoflower heterostructures are prepared by hydrothermal method, which exhibit exceptional photo-piezoelectric catalytic performance. The tight binding between the sheets ensures the efficient electron transport, and provides a large interface area and adequate reaction sites for photo-piezoelectric catalytic reactions. At the same time, because the water flow in the water body produces hydraulic shear force on the material, the material produces piezoelectric effect. Bi2Fe4O9/BiOI exhibit a remarkable degradation efficiency of 99.4% for tetracycline and a hydrogen production rate of 4089.36 µmol h−1 g−1. The observed behavior can be explained by the combined influence of the formation of S-scheme structure and the process of photo-piezoelectric catalysis, confirmed by in-situ XPS, transient/steady-state fluorescence and piezoelectric response force test. The excellent stability of the material suggests its possible use in the sectors of energy and environment. This work introduces novel concepts for the future advancement of photo-piezoelectric synergistic catalysis.

Lennart Bader, Jan Pennekamp, Emildeon Thevaraj et al.

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs.

Jay Lee, Hanqi Su

The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the "6S Principle" is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.

Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon

This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The model aims to provide accurate credit assessment prediction for new supply chain borrowers with limited historical data. Here, the two-stream domain adaptation architecture with correlation alignment (CORAL) loss is used as a core model and is equipped with transformer, which provides insights about the learned features and allow efficient parallelization during training. Thanks to the domain adaptation capability of the proposed model, the domain shift between the source and target domain is minimized. Therefore, the model exhibits good generalization where the source and target do not follow the same distribution, and a limited amount of target labeled instances exist. Furthermore, we employ Local Interpretable Model-agnostic Explanations (LIME) to provide more insight into the model prediction and identify the key features contributing to supply chain credit assessment decisions. The proposed model addresses four significant supply chain credit assessment challenges: domain shift, cold start, imbalanced-class and interpretability. Experimental results on a real-world data set demonstrate the superiority of TransCORALNet over a number of state-of-the-art baselines in terms of accuracy. The code is available on GitHub https://github.com/JieJieNiu/TransCORALN .

Nozar Nozary , Reza Mastouri , Seyyed Mohammad Mirhosseini

Burst of the water pipe causes soil settlement, which can cause deformation in structures and trees, depending on the type of soil and weight of structures. Damages caused by this phenomenon in Iran are covered by fire building insurance. Fire station is under the supervision of the municipality of Tehran. The analysis of four-year data of urban water pipe burst in District 5 of Tehran has been done. This analysis helps to find places with burst to find a way to preserve trees after water pipes burst. In this research, the bursting of the water pipe, which has caused damage to the building, asphalt, etc., has been selected in the places where the data was available, and then the effect of the bursting of the water pipe on the trees has been investigated and analyzed. In these accidents, trees and utilities around the place where the water pipe burst were examined to eliminate the effects of wind or pests and sunlight, etc., that affect the trees, so that by comparing the pipe burst on them, the area of ​​the urban water pipe burst could have been managed to cause less damage to the trees and utilities.

Efrain Lujano, Rene Lujano, Juan Carlos Huamani et al.

El pronóstico de caudales de un río es de gran importancia para el desarrollo de sistemas de alerta temprana. Los algoritmos de inteligencia artificial han demostrado ser una herramienta eficaz en la modelación hidrológica basado en datos, pues permiten establecer relaciones entre los datos de entrada y salida de una cuenca hidrográfica, y de esta manera tomar decisiones basado en datos. Este artículo investiga la aplicabilidad del algoritmo k vecino más cercano (KNN) para el pronóstico de caudales medios diarios del río Ramis en la estación hidrométrica Ramis. Como insumo de entrada al algoritmo de aprendizaje automático KNN utilizamos un conjunto de datos de precipitación media de la cuenca y caudal medio diario de estaciones hidrometeorológicas con varios rezagos. El rendimiento del algoritmo KNN se evaluó cuantitativamente con métricas de habilidad hidrológica, como el error porcentual absoluto medio (MAPE), anomalía del coeficiente de correlación (ACC), eficiencia de Nash-Sutcliffe (NSE), eficiencia de Kling-Gupta (KGE') y ángulo espectral (SA). Los resultados para realizar pronóstico de caudales del río Ramis con el algoritmo de aprendizaje automático KNN alcanzaron altos niveles de confiabilidad, sobre todo con rezagos de caudales de uno y dos días, y precipitación con tres días. El algoritmo utilizado es simple, pero robusto para efectuar pronósticos de caudales a corto plazo, y puede ser integrado como una alternativa para el fortalecimiento del pronóstico hidrológico diario del río Ramis.

Himshweta Singh, Gaurav Pant

Abstract In today's fast-paced society, environmental breakdown is an ecumenical concern. This dilemma is tangled up with the unaccountable anthropogenic activities that are causing significant public health issues. Because of the rapid pace of development, urban soil and water pollution is progressively acting as a sink for a variety of contaminants, including heavy metals, pesticides, petroleum waste, radioactive moieties, etc. Phytoremediation is a low-cost, high-efficient method of extracting or removing pollutants from the environment. Water is transpired by green plants in order to move nutrients from the soil to the site of photosynthesis. During this process, pollutants in the water are also taken up and sequestered, digested, or drained out. This technique has many benefits over conventional approaches which is aesthetically pleasing and has long-term applicability. Phytoremediation could be a viable option to decontaminate heavy metal-polluted sites, particularly when the biomass produced during the process could be economically utilized in the form of bioenergy. Depending on the method used and nature of the contaminant involved, phytoremediating areas where metals and other inorganic compounds exist may utilize one of several techniques as discussed in the present study which are phytodegradation, phytoextraction, phytostimulation, phytostabilization, phytofiltration, phytovolatalization, etc. Phytoremediation is an emerging technology that employs the natural, biological, chemical, or physical processes of plants to remove, detoxify, or immobilize environmental contaminants in a growth matrix. This approach is hence an innovative tool with a great potential to decontaminate soil and water. Phytoremediation is a promising technique for ensuring the sustainability of future generations and reducing pollution.

Rakesh Ahirwar, Mohammad Subzar Malik, Jai Prakash Shukla