Hasil untuk "Water supply for domestic and industrial purposes"

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Manel Mebarki, Gabriella Joge Ngale, Mathieu Lapointe

Abstract A compact three-in-one water treatment process, combining a flocculant, a fibrous super-bridging agent, and a screen-based floc retention system, simultaneously improves water treatment and sludge dewatering. The presence of fibrous materials allows for the formation of very large flocs, efficient floc separation via screening (without settling), and sludge dewatering through a compact press-filter system. The implementation of this three-in-one process is possible due to the formation of very large fiber-based flocs. The sludge containing fibers was subsequently dewatered using a screen-based press filter without further chemical addition. The use of fibers also significantly improved the removal of total organic carbon, nanoplastics, and microplastics. This three-in-one process could be used for decentralized water treatment in drinking water and wastewater applications in small cities, marginalized communities, and developing countries. The compact process, which also performs sludge dewatering, would reduce the risks associated with mismanaged sludge to the environment and human health.

K.O. Mada, J. C. Egbai

The integration of Vertical Electrical Soundings (VES) and pump test analysis has successfully determined the aquifer transmissivity and hydraulic conductivity in Kwale, Delta State, Nigeria. The VES data provided a detailed view of subsurface resistivity variations, identifying potential aquifer zones and layers with reduced permeability. Complementary pump test results confirmed these findings, showcasing the aquifer’s ability to sustain a stable water yield suitable for industrial and domestic purposes. Notably, data from VES 16 near the pump test site highlight localized aquifer property variations, likely influenced by lithological differences. This study delivers crucial insights for groundwater resource management, offering a valuable framework to support water supply planning and sustainable land use in the region.

Seyed Pedram Jazayeri Farsani, Ramtin Moeini

Pressure and residual chlorine concentration are among the key parameters in urban water distribution networks that require continuous monitoring and control. These networks must ensure that consumer water demands are met with adequate pressure while optimizing water quality parameters, such as residual chlorine concentration, to maximize service satisfaction. In this study, the Najaf Abad urban water distribution network was selected as a real large-scale case study. A simultaneous optimization model was developed to determine nodal average pressure, residual chlorine concentration, and network combined reliability. The multi-objective optimization problem was solved using the NSGA-II algorithm under two extreme water consumption scenarios-maximum and minimum water withdrawal during warm and cold seasons. A Pressure-Driven Analysis approach was employed to calculate network parameters. Additionally, three objective functions were optimized using the NSGA-II multi-objective optimization algorithm. The optimal solution was selected from the Pareto front using the TOPSIS method. The network under study includes four operational pressure-reducing valves; after determining their optimal set pressure values, the average network pressure was reduced by 2.9% during ward days and 13.5% during cold days. The average residual chlorine concentration did not undergo significant changes however, its further reduction was prevented through optimization, effectively achieving this objective as well. Lastly, the combined reliability increased by 1.7% and 1.3% for warm and cold days, respectively.

Ishfaq Showket Mir, Ali Riaz, Julie Fréchette et al.

Abstract Bacterial cellulose composites hold promise as renewable bioinspired materials for industrial and environmental applications. However, their use as free-standing water filtration membranes is hindered by low compressive strength, fouling, and poor contaminant selectivity. This study investigates the potential of bacterial cellulose-graphene oxide composites membranes for fouling resistance in pressure-driven filtration. Graphene oxide dispersed in poly(ethylene glycol) (PEG-400) is incorporated as a reinforcing filler into 3D network of bacterial cellulose using an in-situ synthesis method. The effect of graphene oxide on in situ fermentation yield and the formation of percolated-network in the composites shows that the optimal membrane properties are reached at a graphene oxide loading of 2 mg/mL. The two-dimensional graphene oxide nanosheets uniformly dispersed into the matrix of bacterial cellulose nanofibers via hydrogen-bonded interactions demonstrated nearly twofold higher water flux (380 L m−2 h−1) with a molecular weight cut-off ranging between 100–200 KDa and a sixfold increase in wet compression strength than pristine BC. When exposed to synthetic organic foulants and bacterial rich feed solutions, the composite membranes showed more than 95% flux recovery. Additionally, the membranes achieved over 95% rejection of synthetic natural organic matter and bacterial rich solutions, showcasing their enhanced fouling resistance and selectivity.

Carmine Cesarano, Vivi Andersson, Roberto Natella et al.

In Go, the widespread adoption of open-source software has led to a flourishing ecosystem of third-party dependencies, which are often integrated into critical systems. However, the reuse of dependencies introduces significant supply chain security risks, as a single compromised package can have cascading impacts. Existing supply chain attack taxonomies overlook language-specific features that can be exploited by attackers to hide malicious code. In this paper, we propose a novel taxonomy of 12 distinct attack vectors tailored for the Go language and its package lifecycle. Our taxonomy identifies patterns in which language-specific Go features, intended for benign purposes, can be misused to propagate malicious code stealthily through supply chains. Additionally, we introduce GoSurf, a static analysis tool that analyzes the attack surface of Go packages according to our proposed taxonomy. We evaluate GoSurf on a corpus of widely used, real-world Go packages. Our work provides preliminary insights for securing the open-source software supply chain within the Go ecosystem, allowing developers and security analysts to prioritize code audit efforts and uncover hidden malicious behaviors.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Haiming Yao, Yunkang Cao, Wei Luo et al.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

Venketesa Palanichamy N, .. Kalpana M

Ground water is the most accessed source of water for domestic, industrial, and agricultural purposes. Significant social and economic repercussions could result from a declining water table and the depletion of groundwater resources that are economically accessible. Domestic water supply is given top emphasis in both National and State water policy formulation. Recently, there has been a rise in water transfers to satisfy the needs of the industrial and residential sectors.  With the success of the state water supply, many are heralding groundwater transfer as the quickest, least expensive and most environmentally benign solution to large cities water supply and reliability problem. In order to satisfy urban domestic and industrial water demand, the majority of water transfers concentrate on buying water from farmers who are prepared to sell it to them. The present study was undertaken mainly to study the impacts of economic and environmental gains and losses related to the groundwater transfer in Tiruppur district. Without doubts groundwater transfer from agriculture to industrial uses would benefit individual sellers, buyers and the Nation as whole. The adverse direct economic impact in groundwater selling or water transferring areas to total revenue in agriculture was Rs. 54.32 lakhs per every crop season. Scarcity of water resulted in shifting of irrigated agriculture to rainfed agriculture and labour intensive to labour less intensive crops. The total employment lost per hectare of land was 198.33 man-days. Secondly, another adverse indirect economic and environmental impact of water transfer is discharge of large quantum of industrial effluent water. Moreover, there is indirect economic and environmental impact on effluent receiving areas due to highly polluted industrial effluent discharge into open lands and river/streams could cause a Rs. 22,296 net personal income loss for every hectare of land. At larger perspective impacts of groundwater transfer could be considered insignificant.

Avelino Virgílio Fernandes Monteiro de Oliveira, Javier Ruiz Ramírez, João Carlos Antunes Ferreira Mendes

Nowadays, pumps are used worldwide in a very wide range of facilities and for countless purposes, including HVAC, domestic and commercial buildings, district energy, industrial processes and water treatment, municipal wastewater and water supply, agriculture and irrigation, among others. Accordingly, the number of applications is also very diverse. The purpose of present contribution is to introduce an Excel Workbook that represents a friendly easy to use tool that enables the design of pumping systems. It was first thought for Hydraulic Machines Master lectures, but its use might be looked at in a wider perspective. The Workbook includes 17 worksheets (Figure 1), all linked to each other, addressing different aspects of the design. Special attention is paid to the major and minor head losses calculation, the cavitation phenomenon, the use of dimensionless coefficients to determine the rotation speed to obtain a specific operating point and to the calculation of the system curve. Energy efficiency represents today an important goal in every pumping facility; therefore, one of the objectives of this tool is to enable the user to quantify both the shaft power and the efficiency of different operating points thus allowing a sustained definition of the best solution.

Y. S. Tsopoe, L. Sangtam, S. Umadevi et al.

Abstract: Water scarcity has become a serious global threat due to hazardous population growth frequent droughts and changing climate pattern (Carolina B. Mendez et. al). Water is scarce natural resource, even though 71% of land is covered by water out of total water on the earth near about 25% are fresh which is being utilized for various purposes viz. domestic, irrigation and industrial are common. Nowadays the need of commercial water is magnifying tremendously in a developing country like India which has long tradition of rural culture. Here in this study, an attempt was made to estimate the potential of rooftop rainwater harvesting in the state of Nagaland, India. The results showed that the total domestic water demand of Nagaland was estimated at 62,012,868,550 liters and domestic supply potential only from rooftop rainwater harvesting was estimated to fulfill about 64.70% of total water demand and the shortage which is about 35.3% can be through state governments water supply scheme, from the private sector and also other sources like pond, river, spring water, etc

Kai Chen, Linyao Wang, Youqing Wang et al.

The optimization of reservoir operations is vital for rational water resource allocation, ensuring a stable supply for industrial, agricultural, and domestic purposes, and mitigating the risks of floods and droughts. This study approaches reservoir cooperation by considering stakeholder coordination and balancing individual interests with overall system benefits. It constructs a corresponding game model using the coalition formation (CF) method from multi-agent systems (MAS) and provides an optimal coalition formation strategy for multi-reservoir water resource allocation based on the genetic algorithm (GA). The results demonstrate that this method can reduce water supply costs while meeting the water demands of all stakeholders. Additionally, it effectively addresses the issues of water resource waste and low allocation efficiency in the Puyang River basin reservoirs of Pujiang County.

Elaheh Foroudi Sefat, Mohammad Mehdi Ahmadi, Kourosh Qaderi et al.

AbstractIntroduction: Accurate forecasting of runoff and flooding to avoid human and financial losses is one of the most challenging tasks in hydrological studies of a given locale. Therefore, researchers have paid more attention to the development of accurate flood forecasting models, including the use of artificial intelligence methods.Methods: In this investigation, the efficiency of 3 models, ANN, GMDH and ARIMA, has been investigated in order to simulate the flood of a part of Halil river basin in Kerman province. ANN model is a non-linear modeling method that improves its performance over time. The GMDH composed code is an artificial intelligence model with exploratory self-organizing features, at the conclusion of which a complex system with optimal performance is formed. Composed ARIMA code builds a model to describe the structure of the data and then predict the time series. The input data to the above models included discharge, precipitation, temperature, wind and monthly humidity, and the simulated runoff values ​​were compared with the observed values.Findings: In order to evaluate the accuracy of the models in this research, statistical indices were used and the results showed that the ANN model (RMSE=0.042, MSD=0.001, MAE=0.027) had the possibility to estimate the runoff with higher accuracy compared to the GMDH model (RMSE=0.068, MSD=0.005, MAE=0.056) and the ARIMA time series (RMSE=0.096, MSD=0.009, MAE=0.063) in the studied basin. The mean error in runoff estimation with ANN model has been reduced by 38.23% and 56.25%, respectively, compared to the values estimated with GMDH and ARIMA models. According to the results obtained in this study, the artificial neural network model has been able to show a better performance than the other two models in predicting the outputs due to its suitable structural ability to find the nonlinear relationship between the input and output data.

Alex Saturday, Susan Kangume, Wilson Bamwerinde

Abstract The purpose of this study was to investigate the content and dynamics of nutrients in the shallow (max. 6 m) Lake Mulehe. We collected 54 water samples from nine sampling stations between the wet season (March–May 2020 and dry season (June–August 2020). Nutrients; ammonia–nitrogen (NH4–N), nitrate–nitrogen (NO3–N), nitrite–nitrogen (NO2–N), total nitrogen (TN), total phosphorus (TP) and soluble reactive phosphorus (SRP) were investigated in accordance with APHA 2017 standard procedures. Besides, physical parameters: Temperature, pH, turbidity, electrical conductivity and dissolved oxygen were measured in situ. The water quality index (WQI) was used to determine the water quality of Lake Muhele  using drinking water quality standards developed by the Uganda National Bureau of Standards and the World Health Organization. Results indicated that nutrients (TN, NO3–N, TP, NH4-N, NO2–N and SRP) did not differ substantially between study stations (p > 0.05) but did reveal significant differences (p < 0.05) across study months. Besides, nutrient levels differed significantly between seasons (p < 0.05) except for SRP and NH4–N. The WQI values varied from 36.0 to 74.5, with a mean of 58.69. The recorded overall WQI value places Lake Mulehe’s water quality into the ‘poor’ category in terms of worthiness for human consumption. The study, therefore, recommends continuous pollution monitoring and enforcement of local regulations to reduce pollution in the lake as a result of anthropogenic activities.

Jalal Jalili, Meisam Palash, Khalil Jalili et al.

Iran is the thirteenth country in terms of wheat production in the world. Kermanshah Province is known as the west agricultural pole of the country. It has about 700,000 hectares of agricultural land, and more than 173,000 hectares of high quality water land. Irrigation is one of the most important effective factors in grain production in hot and dry climates. Research has shown that the use of modern pressurized irrigation systems reduces water consumption and increases the water use efficiency. Considering the issue of water shortage that has been raised in the country in recent years, The simultaneous investigation of the effects of sprinkler and strip drip irrigation systems in three wheat cultivars including Baharan, Rakhshan and Heydari cultivars on yield, yield components and water use efficiency in this province seemed necessary.This research was carried out in the research farm of ACECR, Kermanshah province unit during 2021. Wheat seeds of Baharan, Rakhshan and Heydari cultivars were cultivated on 08/30/2020 after health control and detoxification. This study was conducted in the form of a split plot in a randomized complete block design with three replications. In the main plots, the irrigation methods including sprinkler and strip drip irrigation were evaluated and in the sub-plots, wheat cultivars including Baharan, Rakhshan and Heidari were evaluated. From each treatment, one square meter of samples was taken from the middle of the plot. These samples were placed separately in special bags (by installing specifications on each sample) and were immediately transferred to the laboratory. According to the dimensions of the plots (60 m2), during the growth period of the wheat plant, the irrigation amount was 633.50 mm in sprinkler irrigation method and 436 mm in strip drip irrigation method. Finally, after data collection, statistical analysis, including an analysis of variance and comparison of means, was performed using Duncan's multi-range test at a five percent probability level with SAS Ver 9.4 software.Based on the results of the statistical analysis of the irrigation method, there was a significant effect on the characteristics of plant height, number of seeds per spike, seed yield, biomass yield, and water use efficiency of the crop. Additionally, the effect of cultivars was significant in plant height, number of seeds per spike, seed yield, biomass yield, and water use efficiency. The results of the analysis of variance showed significant effects of irrigation method and cultivars for crop yield traits and water use efficiency. The comparison of means revealed that the highest yield of 9458 kg/ha was observed in the strip drip irrigation treatment in Baharan cultivar, which had a statistical difference with the sprinkler irrigation treatment in similar cultivars. Furthermore, the sprinkler irrigation treatment in Heydari cultivar with a yield of 6539 kg/ha had the lowest yield. Based on the obtained results, the highest and lowest water use efficiency productivity with values of 2.13 and 1.03 kg/m3 were observed in the strip drip irrigation treatments of Baharan variety and sprinkler irrigation of Heydari variety, respectively. The results of the research showed that the use of strip drip irrigation method compared to the sprinkler irrigation method in Baharan, Rakhshan, and Heydari cultivars resulted in an increase of 20.07%, 29.76%, and 20.50% in crop yield, respectively.Optimum use of water seems necessary considering the climatic conditions of the country and the recent droughts. One of the important and effective solutions is to use modern irrigation systems. The results revealed that the use of different irrigation systems caused a significant difference at the levels of 1% and 5% on the yield and yield components of wheat in investigated cultivars. Based on the results in the tape irrigation method, the yield increased in most of the analyzed parameters compared to the sprinkler irrigation method. The results exhibited that, the use of tape method saved water consumption by 31% and increased the yield of the product and finally increased the water efficiency compared to the sprinkler irrigation method.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

F. Conte, S. Massucco, F. Silvestro et al.

This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation technique. This allows the control to be adaptive, i.e., able to adjust its decisions to the system characteristics, which vary over time due to the daily distribution of users hot water consumption. To answer the demand response requirements, aggregated power variations are realized by modifying the temperature set-points of the water heaters without compromising the users comfort. The developed approach allows tracking a regulation signal and mitigating the so-called rebound, i.e., the recovery of energy needed by the aggregate at the end of the service to return to the baseline thermal state. Analyses in a simulation environment allow the validation of the potentialities of the proposed method.

Ivo Daniel, Andrea Cominola

Leakages in drinking water distribution networks pose significant challenges to water utilities, leading to infrastructure failure, operational disruptions, environmental hazards, property damage, and economic losses. The timely identification and accurate localisation of such leakages is paramount for utilities to mitigate these unwanted effects. However, implementation of algorithms for leakage detection is limited in practice by requirements of either hydraulic models or large amounts of training data. Physics-informed machine learning can utilise hydraulic information thereby circumventing both limitations. In this work, we present a physics-informed machine learning algorithm that analyses pressure data and therefrom estimates unknown irregular water demands via a fully connected neural network, ultimately leveraging the Bernoulli equation and effectively linearising the leakage detection problem. Our algorithm is tested on data from the L-Town benchmark network, and results indicate a good capability for estimating most irregular demands, with R2 larger than 0.8. Identification results for leakages under the presence of irregular demands could be improved by a factor of 5.3 for abrupt leaks and a factor of 3.0 for incipient leaks when compared the results disregarding irregular demands.

M. Di Martino, P. Linke, E. N. Pistikopoulos

The energy-water demands of metropolitan regions and agricultural ecosystems are ever-increasing. To tackle this challenge efficiently and sustainably, the interdependence of these interconnected resources has to be considered. In this work, we present a holistic decision-making framework which takes into account simultaneously a water and energy supply system with the capability of satisfying metropolitan and agricultural resource demands. The framework features: (i) a generic large-scale planning and scheduling optimization model to minimize the annualized cost of the design and operation of the energy-water supply system, (ii) a mixed-integer linear optimization formulation, which relies on the development of surrogate models based on feedforward artificial neural networks and first-order Taylor expansions, and (iii) constraints for land and water utilization enabling multi-objective optimization. The framework provides the operational profiles of all energy-water system elements over a given time horizon, which uncover potential synergies between the essential food, energy, and water resource supply systems.

Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen et al.

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.