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Blockages in water supply systems can arise from diverse causes, leading to pollution, energy loss, and reduced system performance. Detecting and addressing these blockages is crucial for managing pressurized systems like water supply and pipe networks. Analyzing pressure signals is a common method for detecting defects, with transient pressure signals being particularly effective compared to steady-state signals. This study focuses on experimentally investigating the impact of extended blockages on pressure signal characteristics in viscoelastic pipelines within the time and frequency domains. Elastic blockages of varying lengths and diameters were used in a laboratory experimental model of a viscoelastic pipeline. The findings reveal that blockages cause changes in the pressure signal shape, resonance frequencies, and phase in different domains. These changes are more pronounced with longer and higher percentage blockages. Increasing blockage length amplifies phase shifts and wave reflections, with pressure increase delayed by damping. Higher blockage percentages induce phase shifts and amplitude reflections during transient flow. Transient flow intensity affects only amplitude, not phase, with more pronounced reflections at high intensity. Blockage location influences the distribution of phase and amplitude frequencies. Geometric changes induce phase shifts and alter amplitude frequencies, while hydraulic characteristics solely impact amplitude. [ABSTRACT FROM AUTHOR]

Water service providers are confronted with multifaceted challenges ranging from ensuring service quality to addressing technical issues such as pipeline leaks. Manifestations of such leaks are often detected through diminished water pressure, yet the monitoring systems employed by these providers remain suboptimal. Traditional methodologies for identifying and inspecting distribution issues rely heavily on manual, analog procedures. This study seeks to harness the Internet of Things (IoT) for the development of an enhanced water distribution monitoring system. Employing a quantitative experimental methodology, the proposed system integrates an Arduino Uno microcontroller with sensors for water pressure, flow, and GPS location. Data captured by these sensors is synchronized with the Thinger.io platform via an ESP-32 module, facilitating real-time monitoring. Upon evaluation, it was observed that IoT implementation via Thinger.io could effectively track fluctuations in water discharge and pressure. Recorded data revealed a maximum water discharge of 303 liters, with water pressure values ranging from a low of 0.30 bar to a high of 4.07 bar. The outcome of this research is a visual monitoring framework that provides a dynamic assessment of water discharge and pressure, enabling water service providers to conduct routine evaluations with enhanced precision. This IoT-based approach not only streamlines the monitoring process but also introduces a level of automation that could significantly improve service reliability for drinking water infrastructures. [ABSTRACT FROM AUTHOR]

Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods' complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method's leak detection-only credibility. This paper also implements EMD variants to show the novel EMD's importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method's prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%. [ABSTRACT FROM AUTHOR]

In wireless sensor network-based water pipeline monitoring (WWPM) systems, a vital requirement emerges: the achievement of low energy consumption. This primary goal arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the need for frequent battery replacement. Given that sensor nodes in such applications are typically battery-powered and often physically inaccessible, maximizing energy efficiency by minimizing unnecessary energy consumption is of vital importance. This paper presents an experimental study that investigates the impact of a hybrid technique, incorporating distributed computing, hierarchical sensing, and duty cycling, on the energy consumption of a sensor node in prolonging the lifespan of a WWPM system. A custom sensor node is designed using the ESP32 MCU and nRF24L01+ transceiver. Hierarchical sensing is implemented through the use of LSM9DS1 and ADXL344 accelerometers, distributed computing through the implementation of a distributed Kalman filter, and duty cycling through the implementation of interrupt-enabled sleep/wakeup functionality. The experimental results reveal that combining distributed computing, hierarchical sensing and duty cycling reduces energy consumption by a factor of eight compared to the lone implementation of distributed computing. [ABSTRACT FROM AUTHOR]

The estimation of water pollutant loads is of crucial importance as it decides data inputs for both subsequent water quality models and watershed pollutant mitigation strategy. However, the generation of water pollutant loads at high resolution and the life‐cycle pathway of them from sources, through pipelines and wastewater treatment plants, finally to recipient water bodies remain unclear. This study aims to establish a process‐based Water Pollutant Loads Tracking model and applies it to a rapidly urbanizing watershed in Taihu Lake Basin, China at the resolution of 5 m × 5 m in 2017, whose terrain is flat, slowly lowing from west to east. Results show that, of 261.55 tons of the total phosphorus (TP) generation, 71.28% is collected, and 64.32% is treated, with only 26.46% discharged to the water bodies and 1.25% to the target ones. The spatial hotspots of TP generation are mainly concentrated in residential and industrial areas. Direct discharges from point sources and nonpoint runoffs, especially in rainy seasons, are recognized as the main challenges for local water pollution control. The main innovation of this study is to quantify the generation of water pollutant loads at high resolution and to trace the subsequent pathway to collection and discharge, and then to identify the potential substantial gap between generation and discharge, demonstrating the efficacy and broader applicability of this model. Key Points: A process‐based mass balance model is developed to quantify the generation pattern of water pollutant loads at high resolutionThe model is a reliable tool for tracing water pollutants from sources to recipient water bodies without any topographic limitationSubstantial positive gap between generation and discharge indicates the effectiveness of local pollution control measures [ABSTRACT FROM AUTHOR]

Corrosion control for oil and gas industry equipment and pipelines is paramount. In order to ensure increased reliability in its production, the oil industry must develop and optimize corrosion monitoring, control and management methods. In this context, our study is carried out at an oil field located in the South of Algeria (field Hassi Berkine (HBNS)) and in order to treat the problem of corrosion occurred on the steels API 5L of grade X60 of pipeline lines transporting petroleum products to the processing center (CPF). The detection of failures caused by corrosion is done by inspection of pipeline lines by non-destructive ultrasonic testing, while corrosion monitoring allows the evaluation of corrosion rate as a function of time. This will allow us to follow the behavior of the steel as well as the evaluation of the performance of the corrosion inhibitors. Ultrasonic techniques have allowed us to perform a complete examination of the pipelines due to the ability to inspect inaccessible areas. The inhibitors used led to the formation of an impermeable organic film preventing metal-water contact and thus reducing the corrosion rate. We note that the lifespan of the pipes depends on a good continuous monitoring and a rigorous inspection of all the integrity of the pipes and the implementation of safety and protection measures against corrosion such as: corrosion inhibitors, coatings and cathodic protection. [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pipeline asset management derives from pipelines' physical conditions, condition rating, and serviceability through investigating, monitoring, and analyzing the rupture history. The remaining asset life and structural condition of the pipeline network running near and under bodies of water are often hard to predict. In case of a pipeline failure, major damage may occur to the surrounding environment, adding up to disruptions in service and repair costs. This paper develops multinomial logistic regression (MLR) and binary logistic regression models to predict how the bodies of water could affect the soil surrounding wastewater interceptors. The models were developed based on data from the City of Fort Worth, Texas. This study concludes that the pipe diameter, pipe age, location of the pipeline with reference to bodies of water (far or near), and the pipe material are the most significant variables that affect the surrounding conditions and remaining life of wastewater interceptors. In future, a clearer perception through increased software development and machine learning for managing pipeline asset management would provide impacts on different parameters on pipelines' expected life. [ABSTRACT FROM AUTHOR]

Isobutanol, one of the four isomers of butanol (C4H9OH), possesses some favorable properties that make it an attractive fuel for internal combustion engines. For instance, when compared to ethanol, isobutanol features a higher heating value and lower hygroscopicity (which prevents corrosion and enables it to be transported via pipelines). Moreover, its addition to gasoline does not distort the fuel blend's vapor pressure to the same extent as ethanol does. All of this while having a high octane rating. Those advantages over ethanol suggest that isobutanol has the potential to be used as a gasoline oxygenate or even as a neat fuel. Furthermore, the advances made in biotechnology have enabled isobutanol to be produced from biomass more efficiently, allowing it to be used in compliance with existing renewable energy mandates. This article reviews some of the relevant literature dedicated to isobutanol as a motor fuel, covering its merits and drawbacks. Several studies on its combustion characteristics are also discussed. Most of the included literature refers to the use of isobutanol in spark-ignition (SI) engines, as its properties naturally lend themselves to such applications. However, isobutanol's utilization in diesel engines is also addressed, along with a couple of low-temperature combustion examples. [ABSTRACT FROM AUTHOR]

The escalating global water usage and the increasing strain on major cities due to water shortages highlights the critical need for efficient water management practices. In water-stressed regions worldwide, significant water wastage is primarily attributed to leakages, inefficient use, and aging infrastructure. Undetected water leakages in buildings' pipelines contribute to the water waste problem. To address this issue, an effective water leak detection method is required. In this paper, we explore the application of edge computing in smart buildings to enhance water management. By integrating sensors and embedded Machine Learning models, known as TinyML, smart water management systems can collect real-time data, analyze it, and make accurate decisions for efficient water utilization. The transition to TinyML enables faster and more cost-effective local decision-making, reducing the dependence on centralized entities. In this work, we propose a solution that can be adapted for effective leakage detection in real-world scenarios with minimum human intervention using TinyML. We follow an approach that is similar to a typical machine learning lifecycle in production, spanning stages including data collection, training, hyperparameter tuning, offline evaluation and model optimization for on-device resource efficiency before deployment. In this work, we considered an existing water leakage acoustic dataset for polyvinyl chloride pipelines. To prepare the acoustic data for analysis, we performed preprocessing to transform it into scalograms. We devised a water leak detection method by applying transfer learning to five distinct Convolutional Neural Network (CNN) variants, which are namely EfficientNet, ResNet, AlexNet, MobileNet V1, and MobileNet V2. The CNN models were found to be able to detect leakages where a maximum testing accuracy, recall, precision, and F1 score of 97.45%, 98.57%, 96.70%, and 97.63%, respectively, were observed using the EfficientNet model. To enable seamless deployment on the Arduino Nano 33 BLE edge device, the EfficientNet model is compressed using quantization resulting in a low inference time of 1932 ms, a peak RAM usage of 255.3 kilobytes, and a flash usage requirement of merely 48.7 kilobytes. [ABSTRACT FROM AUTHOR]

Water leakage from aging water and wastewater pipes is a persistent problem, necessitating the improvement of existing leak detection and response methods. In this study, we conducted an analysis of essential features based on data collected from leak detection sensors installed at water meter boxes and water outlets of pipelines. The water pipeline data collected through the vibration sensor were preprocessed by converting it into a tabular form by frequency band and applied to various machine learning models. The characteristics of each model were analyzed, and XGBoost was selected as the most suitable leak detection model with a high accuracy of 99.79%. These systems can effectively reduce leak detection and response time, minimize water waste, and minimize economic losses. Additionally, this technology can be applied to various fields that utilize water pipes, making it widely applicable. [ABSTRACT FROM AUTHOR]

Detecting and localizing leakages in underground water pipelines continue to be challenging in large-scale water distribution networks (WDNs) having a low density of acoustic sensors per unit pipeline. Many previous acoustic research analyses have been restricted to either (1) lab-scale WDN setups having simulated leakage conditions, or (2) field-scale WDNs with high densities of acoustic sensors. To address the current challenges, this study develops a generic and practical acoustic data analysis framework to analyze acoustics signals for leakage detection and localization in large-scale WDNs, where the proposed framework encompasses multi-stage systematic analyses, namely: (1) data quality assessment; (2) acoustic features generation and analysis; (3) leakage event detection; and (4) leakage event localization. In collaboration with Public Utility Board (PUB), Singapore's National Water Agency, our proposed framework has been verified in large-scale WDNs in Singapore having more than 1,100 km of underground water pipelines and 82 permanently installed monitoring stations, each of which is instrumented to measure acoustics, via hydrophone equipment, pressure, and water quality parameters, and by using historical data collected between August 1, 2019 and August 31, 2020, where numerous leakage events were reported to within 1,000 m, or less, connected pipelines from independent hydrophones. [ABSTRACT FROM AUTHOR]

Corrosion monitoring plays a determining role in corrosion management in industries, particularly oil and gas systems. Corrosion's effective monitoring could be achieved through the properly selected monitoring methods, using suitable reassessment intervals as well as application of generated data. When carried out properly, corrosion monitoring can also be served as an early warning system before the onset of corrosion-associated failures. This paper provides a literature review of pipelines' internal corrosion using electrochemical technique. Several electrochemical techniques namely electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), galvanic sensors, electrochemical noise (ECN), harmonic distortion analysis (HDA) and wire beam electrode (WBE) have been discussed in this review; as well as examples of exploiting these techniques have been presented. The main focus of this paper is reviewing field applications of mentioned techniques rather than laboratory experiments. The same trends can be exhibited through the corrosion rates determined by ECN, LPR, and EIS. Studies have implied that ECN can be a very powerful tool for monitoring corrosion rate and corrosion mechanism. LPR has been mainly utilized for water pipelines or those that have at least 30% free water. HDA has been rarely used due to the complexity of interpretation. Developed galvanic sensor system cannot numerically express the corrosion rate, but it can only provide the information about the presence of corrosion. Reliable EIS measurements could not be obtained with regularity in operating oil and gas pipelines but there some field cases of this technique has been discussed. For studying pitting tendency, WBE is a better and more accurate technique than traditional cyclic potentiodynamic polarization method. Application of potentiodynamic testing in the field is primarily limited to inhibitor evaluations. Results revealed that corrosion rate obtained by using WBE technique has good correlation with other techniques. There are numerous references, which would be very helpful in finding the details of each above-described technique. Finally many case studies for the application of electrochemical techniques in oil and gas pipelines have been gathered and tabulated as a guideline for target users which by comparing the capability and inability of each method can select the best tailored techniques for intended se-rvice. [ABSTRACT FROM AUTHOR]

Water supply pipeline leakage is a major issue around the world. Leak detection and remediation can prevent water scarcity and some other problems. As a result, investigating pipeline leak-detecting technology has a high practical value. This study employs a promising technique for detecting pipeline leaks using Acoustic Emission (AE) signals. A dytran acceleration sensor was used to collect leakage signals in the time domain. The time-domain signal is transformed into a frequency domain by employing Fast Fourier Transform (FFT). The produced frequency signal has many dimensions which can be reduced to 17 by Principal Component Analysis (PCA). Intelligent leakage diagnosis techniques should eliminate time, and human intervention, and increase effectiveness. Machine learning (ML) models come into play at this point. To detect leakage, the hybrid ML model is proposed and it is compared with the conventional ML models. The best model for detecting water leakage is identified using the classification metrics. [ABSTRACT FROM AUTHOR]

A pipeline is the most efficient way to transport water from one place to another. Due to aging, corrosion, and external factors, the pipeline is prone to damage, which causes leaks. Many machine learning (ML) and deep learning (DL) methods are available to address this issue. This paper does an experimental study on available methods in ML and DL for leak detection for the collected data using an acousto-optic sensor. The experimental setup comprises of an acousto-optic sensor made of an erbium-doped fiber laser (EDFL), galvanized iron pipeline, a tank, a pump, and a data acquisition unit. The dimensions of the galvanized pipeline looped with the water tank are a length of 40 m, an inner diameter of 89 mm, and an outer diameter of 90 mm. The diameter of the simulated leak aperture is 5 mm. The methods analyzed in this study are k-means, k-medoids, Naive Bayes (NB), support vector machines (SVM), k-nearest neighbor (KNN), decision tree (DT), categorical boosting (CatBoost), random forest (RF), XGBoost, AdaBoost, and one-dimensional convolutional neural network (1DCNN). ML algorithms need a feature extraction technique because the data collected from the experiment is too large and contains redundant information. Feature extraction reduces the data size by extracting essential information. This paper extracts ten features from raw data. Among the ML algorithms, AdaBoost gives the highest prediction accuracy of 98.02%. This paper also implements eight models of 1DCNN, and Model 1 shows the best prediction accuracy of 98.16%, which is the highest compared with all the other classifiers in ML and DL for one-dimensional time series acousto-optic sensor data. [ABSTRACT FROM AUTHOR]

Concrete pipes are used widely in sewage pipeline networks due to their superior stiffness, bearing capacity, and low price. However, as the service age increases, the microorganisms inside the pipeline react with the concrete pipe walls and induce concrete pipe wall corrosion. Microbiologically induced corrosion (MIC) is serious corrosion in concrete sewage pipe walls, resulting in a reduction of the wall's thickness and causing the cover soil above the buried pipeline to collapse. The real-time corrosion in concrete sewage pipe walls was simulated in this study. A numerical simulation of the MIC in concrete sewage pipes was performed using the software COMSOL Multiphysics, in which the randomness of the MIC was considered by introducing the random distribution of concrete porosity and corrosive substance concentration; the influence of the turbulence and the transfer rate of H2S were considered by zoning the section of the pipe wall. Combined with the probability density evolution theory, a probability model is proposed to predict the maximum corrosion depth of the concrete sewage pipe wall. The results show that the maximum corrosion depth in the pipeline is more likely to occur in the vicinity of the sewage level and the pipe crown, and its dispersion increases with time and decreases as corrosive substance concentration increases. After verification, the model presented can be used to predict the time-dependent reliability and the service life of concrete sewage pipes. [ABSTRACT FROM AUTHOR]

The article offers reviews on a several products in fluids and solids handling: DENIOS' FALCON full-size funnel for efficient IBC totes refilling, Kaeser Compressors' i.Comp 8 and 9 oilless air compressors for high-performance applications.

Water, sewerage, and gas infrastructures play a crucial role in optimising the housing quality of buildings and cities. On the other hand, water, sewer, and gas pipelines constantly need maintenance, checks, and repairs. These interventions require large budgets, and therefore scrupulous investment planning is necessary. In this study, Multiple Regression Analysis (MRA) is applied to estimate the urban renovation costs related to the works on water, sewage, and gas networks. The goal is to build a reliable cost estimator that is easy to apply and has a minimum number of explanatory variables. Four regressive models are tested: linear, linear-logarithmic, logarithmic-linear, and exponential. The analysis is implemented on two datasets of projects carried out in Italy: the first collects the data of 19 projects made in historical centres, while the second collects the data of 20 projects made in the peripheries. The variables that impact costs the most are selected. In terms of results, the estimated functions return an average error of 1.25% for historical centres and 1.00% for peripheral areas. The application shows that a differentiation of cost functions based on the urban context is relevant. Specifically, two different functions are detected: exponential for historical centres and linear for peripheral areas. In conclusion, we interpret that the exponential growth of costs in historical centres depends on a series of critical issues (logistical, architectural, etc.), present to a lesser extent in the peripheries, which complicate the execution of the interventions. The approach adopted, which led to the detection of cost functions differentiated based on the urban context, allows us to benefit from more accurate modelling that considers the places' specificities. [ABSTRACT FROM AUTHOR]

Inter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measured data to establish a model. Therefore, based on a data-driven method, this paper diagnoses and restores the important parameters of the water diversion projects, including the elevation of pipeline and water level data, which can be used to establish the adaptive hydraulic transition model of the water diversion projects. Firstly, the abnormal data of the elevation of pipeline were identified using expert data annotation and support vector classification (SVC), with the identification accuracy of abnormal data being as high as 91%. Then, the single and continuous abnormal data were restored using an interpolation method and multiple linear regression algorithm (MLR), and the restored data were found to be consistent with the judgment of expert knowledge. Secondly, K-medoids was used to classify the complex multi-dimensional water level data, combined with the 3-sigma method to identify the outliers in each class. The gradient boosting decision tree algorithm (GBDT) trained on normal data restored outliers in a predictive manner, and the mean absolute percentage error (MAPE) was 0.003%, 0.025% and 0.091% in each class, respectively, showing the best accuracy compared with other models. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of fiber optic distributed temperature sensing (DTS) to detect and locate leaks is still in its infancy in water infrastructure, despite its promising capabilities. Only few experiments tested this technology, and none of these studies focused on small but persistent leaks, like background leakages, which are ubiquitous and generally go undetected with the technology currently available, thus posing a serious threat to the available water resource. To test the feasibility of detecting and locating background leakages with fiber optic DTS, this study provides a detailed analysis on flow and temperature alterations around leaking water pipelines in presence of small leaks (5, 25, and 125 L/d) with small to moderate temperature differences with the surrounding soil, under 3 different pipe defect configurations, either in absence or in presence of pipe thermal insulation. Transient 3D heat transfer-unsaturated flow numerical simulations showed that there is potential to use temperature alterations to detect and locate incredibly small leaks with fiber optic DTS, like background leakages, despite the influence of pipe temperature on the surrounding soil. The analysis showed that extent, distribution, and magnitude of these alterations are convection dominated at a given temperature difference between leaked water and undisturbed soil, and that it may not be strictly necessary to place the optical fiber directly below the pipe. Indeed, optical fibers located within the utility trench at the sides of the pipe and below its bottom showed comparable or even better performance, thus giving new opportunities to retrofit existing pipelines as well. [ABSTRACT FROM AUTHOR]

Aiming at establishing the transient flow characteristics of gas–liquid two-phase flow in high-undulation water pipelines, based on the bubble distribution law measured using physical tests, the bubble distribution law function was input into the hump-pipe fluid domain model, and CFD numerical simulation was carried out for different flow rates and different air contents. The CLSVOF two-phase flow model and the RNG k-ε turbulence model were used to analyze the flow pattern evolution and pressure pulsation propagation in the process of gas–liquid two-phase flow through a hump pipe. The results show that the bubble size has a lognormal distribution, the equivalent diameter is between 3 mm and 10 mm, and the evolution of the flow pattern in the hump pipe is complex and violent. In the horizontal pipe section, there are three main flow patterns: bubble flow, wavy flow and segment plug flow. In the vertical pipe, there are two main flow patterns, slug flow and churning flow, and the flow pattern is affected by the flow rate and the air content rate. When air bubbles or air pockets in the pipeline flow through a certain area, this leads to a steep increase and decrease in the pressure pulsation amplitude in the region, and the pressure fluctuation is extremely frequent. Compared with the water flow rate, the air content is the main factor affecting the relative pressure pulsation amplitude under the condition of a 0.15-air content operating mode, which is generally approximately two to six times that of the 0-air content operating mode. The results of the research should facilitate the prediction of stagnant gas pipeline system bursts and water hammer protection, providing a theoretical basis and calculation parameters. [ABSTRACT FROM AUTHOR]

The health issues of urban tap water are of great concern in the context of sustainability challenges to the environmental quality of water and the security of the water supply. In this work, tap water from the main urban areas in Wuhan and surface water from the Yangtze River and the Hanjiang River were collected during summer (June) and winter (December), 2022. The concentrations of 10 heavy metals including Fe, Al, Mn, Co, Ni, Cu, Se, Cd, Cr and Pb were determined for water quality evaluation and health risk assessment. The results demonstrated that almost all of the tap water samples contained metal concentrations below the Chinese national standard limits for drinking water (GB 5749-2022). The risk of heavy metals in tap water to human health was evaluated, and the results showed that the total carcinogenic risk (TCR) was in the range of 10−6 and 10−4 and the hazard index (HI) was much lower than one in both summer and winter. The current tap water in Wuhan is generally in a relatively safe state and will not cause acute hazards or chronic diseases in the short term, but the long-term cancer risk is still noteworthy. The heavy metal pollution index (HPI) showed that the overall water quality of urban drinking water sources in Wuhan has been satisfactory, despite its slightly polluted state in winter. Pipeline corrosion was considered as one of the important sources of heavy metals in Wuhan tap water, which can explain, to a certain extent, the increase in the heavy metal concentrations of tap water outlets relative to the finished water reported by waterworks, such as Fe, Ni, Cd and Pb. This study has implications for the formulation of better urban water supply security management strategies and associated sustainability challenges. [ABSTRACT FROM AUTHOR]

Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the realtime wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42° and a mean absolute error of 5.85°. [ABSTRACT FROM AUTHOR]

This document summarizes recent archaeological excavations in the East Riding of Yorkshire and Lancashire. In Driffield, evidence of Roman occupation and agricultural activity was found, including the possible foundations of a stone structure. In Brough-on-Humber, excavations focused on the defenses of Roman Brough and uncovered sections of the original sand rampart and pottery fragments. In Broughton, the remains of the Roman road Margary 70d were discovered during the excavation of a water pipeline trench. In Catterall, the Roman road from Ribchester to Catterall was uncovered, and in Caton, investigations were conducted to confirm the course of the Roman road, revealing road construction and milestone locations. [Extracted from the article]

Solar-hydrogen energy for spacecraft, space interplanetary stations, space bases on the planets of the solar system must reliably provide energy: spacecraft during peak periods (repairs at a space facility, energy-consuming experiments, cleaning of facilities, regeneration of waste to extract oxygen and hydrogen, etc..d.) and bases on planets, especially where the night lasts for a long time. The onboard hydrogen module of the solar-hydrogen plant includes: a container with water; distiller; deionizer; water lines with adjustable shut-off valves; electrolyzer; cryogenic side tanks with hydrogen and oxygen; fuel cell, hydrogen safety sensor systems based on leakage and concentration sensors; level gauges; cryogenic and gas pipelines of hydrogen and oxygen; onboard cryo-refrigerator; automatic control system for the hydrogen module. The article presents the results of experimental studies of static, dynamic and mass-dimensional characteristics of power conversion equipment (hereinafter - PCE) in the power supply system (hereinafter - PSS) of the spacecraft (hereinafter - spacecraft) with a digital control system. The purpose of experimental research is to verify the theoretical results obtained earlier in the design of digital PCE control systems of high-voltage PSS spacecraft, including the study of static and dynamic characteristics. We developed and manufactured an experimental PCE sample with an output power of 2500W in this research. It includes: four voltage stabilization modules and the 100V load supply output bus filter. Each voltage stabilization module includes a battery energy conversion channel, a solar battery energy conversion channel and a digital control system unit. The power conversion channel of the accumulator battery is designed and made on the basis of a bridge volt-booster inverter-transformer circuit. Voltage control in energy converter of accumulator battery is based on phase shift in control pulses of transistors in adjustable inverter rack related to unregulated one. The power conversion channel of the solar battery is designed and made on the basis of a two-phase circuit of a direct voltage converter of an increasing type with switching throttles. The digital control system of the voltage stabilization module is designed on the basis of analogues in the modern electronic component base in the Space class and consists of four identical channels. Each channel of the control system controls its own voltage stabilization module. We obtained the following results during our experimental studies: some pulsations of the output voltage of the energy conversion equipment during operation on an active load, transient processes of the output voltage of the energy conversion equipment during pulsations of the load current without changing the mode of operation, transient processes of the output voltage of the energy conversion equipment when changing the modes of its operation and weight and size characteristics for different values of the output power. The obtained results of experimental studies confirm the previously obtained theoretical results, and also showed that the use of a distributed digital control system will increase the specific energy characteristics of the newly developed PSS spacecraft while ensuring the given static and dynamic characteristics of output voltage quality. The results of the work are designed to create a new on-board power conversion equipment for power supply systems of promising spacecraft. The area of application of the project results is space instrumentation. [ABSTRACT FROM AUTHOR]

Starting up water installations is typically a task that falls within the purview of water utility companies. These operations involve the presence of two separate fluids (water and air) that can be analyzed in terms of consideration two distinct behaviors (hydraulic and thermodynamic). During a filling process, trapped air pockets exhibit a trend of declining volume, generating pressure surges that are typically not addressed under current worldwide regulations. This research introduces an innovative mathematical approach based on physical equations to investigate filling operations in water installations involving trapped air, incorporating an unsteady friction model (using the Brunone friction coefficient), in combination with the rigid water column model. The validation of the proposed model is carried out in an experimental facility measuring 7.36 m in length. The proposed model is then applied to a case study involving a 460 m long pipeline with an internal pipe diameter of 150 mm, featuring an undulating profile composed of three branches, to demonstrate how the gravity term should be calculated in real-world water installations. The results showed that the proposed model, considering an unsteady friction model, is suitable for simulating the start up of water pipelines for the experimental facility analysis and the case study. The Swamee–Jain formula yielded the best results compared to other formulations for computing the friction factor. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The low level of water and regulation of the Don River runoff led to an irreversible transformation of natural hydrological and hydrochemical processes. At the end of 2022, despite the shortage of river water, the region began laying a water pipeline with a capacity of up to 300 000 m3 of water per day to the Donbass. Are there enough water reserves for the increasing economic needs throughout the year? This work is based on a targeted study of the chemical composition of water under different regimes and sea levels on the Taganrog seashore of the Don Bay and delta. The experiment covers the period from December 2, 2022, until January 30, 2023, and single measurements, for comparison, on February 21, March 30, and April 17, 2023. In total three types of ionic composition typical of the Svinoe delta arm of the Don River are distinguished: first, marine with a dominant (>80%) total amount of sodium and potassium ions, chlorides, and salinity up to 7–9 g/L. Second, the waters of the Don correspond to low salinity (<2 g/L), an increased proportion of Na+ + K+ (50–60%), and a close amount of SO and HCO (30–40% each). Third, since 2018, groundwaters have been regularly recorded when the level decreases. These are medium brackish waters with a salt content >5 g/L. They typically have a high content of sodium and potassium (>60%) and sulfate ions (60–80%). Monitoring shows that the deficit of fresh drinking water will increase, and with increasing withdrawal of water, the deficit will accelerate in time. It will be replaced (up to 5–7 g/L) by mineralized groundwater and water from the Black Sea. The proportions of slightly brackish (2–4 g/L) and medium brackish (4–8 g/L) waters to be pumped (redistributed by seasons) into water pipelines still need to be explored. [ABSTRACT FROM AUTHOR]

An adequately calibrated hydraulic model is critically to the water distribution digital twin, which requires accurate representation of a water distribution network (WDN) in near real-time. It is thus imperative to construct an extended-period simulation model that captures the network's baseline normal and abnormal diurnal demand patterns, which are usually derived using monitored flow data. However, from a practical field aspect, it remains challenging to calibrate multiple demand groups of varying diurnal patterns in large-scale WDNs, as flow data are typically collected sparsely due to cost considerations, while pressure sensors are commonly deployed throughout the network. In this paper, we propose a new pressure-based demand aggregation and pattern calibration method that leverages on monitoring pressure data to aggregate demands, identifying abnormal consumptions, and calibrating the diurnal patterns of various demand groups. The new method is integrated with previously developed model calibration framework and applied to a large-scale WDN system having more than 330 km of underground water pipelines with weekly averaged pressure and flow data, as derived from a maximum historical period of nine months. Key findings from our case study analysis for the seven averaged days (Monday to Sunday) include: (1) calibrating the system's flow balance to within 99% average accuracy by identifying and calibrating five unique demand patterns, inclusive of those associated with abnormal consumptions, via grouping 34 available pressure sensors; (2) calibrating the system's energy balance to within 95% average accuracy by iterating the simulated pressures against representative monitored pressure profiles of the different demand groups during the flow calibration process; and (3) achieving an average 2.5% accuracy improvement for the overall energy calibration, relative to that of the previous calibration approach. Throughout the solution process, significant engineering judgment is adopted, coupled with optimization analyses, to calibrate the system's flow and energy balances while meeting the model constraints and data availability. [ABSTRACT FROM AUTHOR]

A growing population and urbanization place increased demands on water supply and distribution networks. Pipelines are one of the most critical components of water supply systems. It is, therefore, necessary to identify the relevant factors that affect the deterioration of water distribution pipelines. This will help decision makers in future planning and prioritization of the required maintenance. In this study, a systematic review is performed to identify critical factors that affect the failure of water pipelines. A meta-analysis is conducted to determine the relative importance of each factor that contributes to pipe failure. In addition, the source of contradictory results across studies is investigated. The results show that climatic factors, such as air temperature, minimum antecedent precipitation index, and net evaporation, contribute to water pipe failure. Additionally, the results of subgroup meta-analyses show that primary sources, such as pipe material and water pipe size, can lead to high heterogeneity across studies. This study is expected to help water utility owners to collect relevant data and make timely renewal decisions. [ABSTRACT FROM AUTHOR]

The inner walls of the drinking water distribution system (DWDS) are expected to be clean to ensure a safe quality of drinking water. Complex physical, chemical, and biological processes take place when water comes into contact with the pipe surface. This paper describes the impact of leaching different compounds from the water supply pipes into drinking water and subsequent risks. Among these compounds, there are heavy metals. It is necessary to prevent these metals from getting into the DWDS. Those compounds are susceptible to impacting the quality of the water delivered to the population either by leaching dangerous chemicals into water or by enhancing the development of microorganism growth on the pipe surface. The corrosion process of different pipe materials, scale formation mechanisms, and the impact of bacteria formed in corrosion layers are discussed. Water treatment processes and the pipe materials also affect the water composition. Pipe materials act differently in the flowing and stagnation conditions. Moreover, they age differently (e.g., metal-based pipes are subjected to corrosion while polymer-based pipes have a decreased mechanical resistance) and are susceptible to enhanced bacterial film formation. Water distribution pipes are a dynamic environment, therefore, the models that are used must consider the changes that occur over time. Mathematical modeling of the leaching process is complex and includes the description of corrosion development over time, correlated with a model for the biofilm formation and the disinfectants–corrosion products and disinfectants–biofilm interactions. The models used for these processes range from simple longitudinal dispersion models to Monte Carlo simulations and 3D modeling. This review helps to clarify what are the possible sources of compounds responsible for drinking water quality degradation. Additionally, it gives guidance on the measures that are needed to maintain stable and safe drinking water quality. [ABSTRACT FROM AUTHOR]

Due to the rapid development of today's industry, it not only greatly accelerates the consumption of energy, but also causes serious pollution to the environment. Therefore, reducing energy waste and recovering energy has become an important research direction today. This paper introduces the future development trend of renewable energy, discusses the progress of recovery of nonpolluting energy such as marine, bioenergy and thermal energy, and analyzes the efficiency and social benefits of various types of energy recovery. Energy recovery methods for natural gas pipelines are briefly analyzed, and current research advances in energy pressure recovery devices are provided, leading to a summary of methods to improve the efficiency of fluid energy recovery. Finally, it is concluded that although the current energy sources are diversified, the most valued energy sources at present and in the future should be solar energy and ocean, which are infinitely recyclable. This paper will benefit researchers to further understand and study the various aspects of energy recovery. [ABSTRACT FROM AUTHOR]

This study examines the impact of sub-atmospheric pressures in water pipelines during emptying manoeuvres with air admitted. Previous research has looked at this issue but has not studied it in detail. This research presents a two-dimensional model using the OpenFOAM software to analyse different emptying manoeuvres in a single pipeline with entrapped air. The results show the sensitivity of the ball valve opening percentage, which show that absolute pressure drop can reduce to 23% for each 5% of ball valve opening percentage. The influence of the size of the entrapped air pocket and different air-admission orifices was also analysed. The numerical model showed that the selection of the percentage and times of opening drainage valves in pipelines with air-admission orifices is crucial in controlling sub-atmospheric pressure conditions. Finally, this study demonstrates the ability of the two-dimensional model to show the sensitivity of hydraulic drainage parameters in pipelines with entrapped air. [ABSTRACT FROM AUTHOR]

This investigation focuses on the ranking of pipeline leakage susceptibility of two categories of pipeline systems. One consists of three oilfield water-handling pipeline systems and the other consists of five dry crude systems. Online corrosion monitoring (OCM) method was used for both systems, forthermore, in-line inspection (ILI) method, with Magnetic Flux leakage (MFL) tool, was also used for the dry crude systems. The OCM leakage susceptibility potential is derived from the combined severity of general corrosion, pitting corrosion, fluid corrosivity and sessile bacteria population density in the process streams. The ILI leakage susceptibility potential is derived from the average number of corrosion anomalies greater than 10% of the wall thickness and the maximum depth of the anomalies (as percentage of the wall thickness). In spite of such distinctions, the ranking of pipeline leakage susceptibility for the five dry crude pipeline segments turned out to be identical for the two methodologies. [ABSTRACT FROM AUTHOR]

In recent years, ICSs have become increasingly commonplace in virtually every industry. The abbreviation "ICSs" refers to industrial control systems. These are specially designed computers used for monitoring, managing, and controlling procedures and tasks across a wide range of industries and vital infrastructure sectors. Production, power, disinfection of water, transport, and other sectors all greatly benefit from ICS use. The authors of this paper aim to detect ICS cyber hazards in industry. This article is the result of the writers' extensive research on ICS programs and the impact of cyberattacks on them as well. The study narrowed its attention to just three ICS applications because there are simply too many to count: power plants, water reservoirs, and gas pipelines. The present paper focuses on the development and evaluation of neural networks for use in cyberattacks. An early form of neural network, the residual system, came first in the field. When a breach is detected in the ICS, the neural network sorts it into one of several categories. The produced datasets must not compromise users' privacy or cause harm to the relevant industry if they fall into the wrong hands. An encoding device, decoder, pseudo-encoder, and critical model neural networks work together to generate random data. Finally, a set of trials is conducted in which a residual neural network is utilized to classify cyberattacks based on both the created and original datasets. Results from a series of studies indicate that using the created dataset is an effective technique to train high-quality neural networks for use in cybersecurity on a large amount of data without sacrificing the accuracy of the models. The Kullback-Leibler and Jensen-Shannon divergences also serve as the theoretical foundation and technique, respectively. In particular, the paper recommends operational and maintenance cybersecurity standards for ICS. This entails such things as secure password practices, patch management, and anti-malware defense. Physical safeguards for ICS is another topic that is covered. [ABSTRACT FROM AUTHOR]

Copyright of Nuclear Safety is the property of Nuclear & Radiation Safety Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The hydraulic pipeline vibration noise is one of the main noise sources in submarine stealth conditions. Taking the local hydraulic system of a certain type of submarine as the research object, a model is first developed to simulate water hammer pressures and to study the influence of component parameters on the generation and transmission of water hammers. Then, using the maximum water hammer as the excitation, fluid–structure interaction (FSI) vibration characteristics analysis of the pipeline is carried out. Additionally, the simulation method of clamp bolt pre-tightening is discussed. Finally, the modal test of various specifications of the pipeline is carried out. The results show that the error between the simulation and the test results is within 10%, which verifies the correctness of the model settings. On this basis, with the position of the clamp as the independent variable and the maximum stress of pipelines as the dependent variable, the optimization of pipeline passive vibration control is carried out by genetic algorithm, and the finite element verification shows that the pipeline vibration stress is effectively reduced. [ABSTRACT FROM AUTHOR]

Research on the dispersion and deposition of solid particles in sanitation networks is crucial due to its role in channel blockage and overflow of wastewater and stormwater systems. Conventional detection methods are excessively costly and demand a significant time investment, while predictive mathematical models are prone to uncertainties. This study aims to assess the influence of solid particles on fluid flow and incorporate the effects of added mass and pressure gradient into the equation governing particle behavior. It is motivated by observations in Algeria, where the density of solid particles is notably high, thereby accentuating their impact on wastewater flows. To achieve these objectives, a bidirectional Eulerian-Lagrangian coupling method is employed, combining the advantages of various turbulence models, including the k-ω-sst model and the standard discrete random walk (DRW) model. This approach enhances our understanding of solid particle dispersion and deposition in sanitation networks, contributing to more efficient management and prevention of pipe obstructions, with implications for environmental preservation and the sustainability of urban sanitation systems. The use of turbulence models recommended in this study is inspired by Kolmogorov's pioneering work on turbulence, while the integration of added mass and pressure gradient forces falls within the context of particle dynamics in suspension. By leveraging in-situ data and incorporating the aforementioned forces, this innovative approach deepens our understanding of the processes involved in solid particle dispersion and deposition in urban drainage networks. These advancements are pivotal for the management and prevention of pipe obstructions, thus contributing to the preservation of the environment and the sustainability of urban sanitation systems. [ABSTRACT FROM AUTHOR]

The installation and maintenance of water infrastructure in South African cities poses a challenge to communities, municipalities, and installation contractors, who face problems such as inadequate access to homes, traffic jams, inadequately barricaded trenches, leaking water pipes, water supply cutoffs, vandalism, and theft of water. The research problem, therefore, involves an inadequate understanding of what drives these problems and how to mitigate them through better management. The aim of this study was to investigate the challenges of managing water infrastructure as perceived by the citizens of a South African municipality. The study was a descriptive and cross-sectional survey, with data collected, via a literature derived, emailed questionnaire, from a self-selected nonprobability sample of 402 City of Tshwane residents. Analysis, via SPSS Version 27, used descriptive and inferential statistics. The findings revealed that both the municipality and the contractors contributed to the challenges experienced by citizens, with maintenance challenges being more serious than installation challenges. Municipal and political challenges were the main drivers, with political ownership of construction companies and political interference being the biggest problems. The findings contributed to a better understanding of the challenges inhibiting effective installation and maintenance of water infrastructure, and are important to the municipality, and the community at large, because they contribute to a better understanding of service delivery from the citizens' perspectives. [ABSTRACT FROM AUTHOR]

Air valves are protective devices often used in pressurised water pipelines, ideally admitting air to limit sub-atmospheric pressures and controlling the release of entrapped air. This work summarises a comprehensive sensitivity analysis of the transient behaviour in a rising water pipeline with an air valve following a pump trip. The paper examines the water hammer stages associated with a pump trip, namely, the initial depressurisation, followed by air admission, then air expulsion, and finally the creation of a secondary pressure wave. For each air valve location and specific set of design conditions, the relationship between the transient magnitude and air valve outflow capacity is found to be non-linear, but to roughly follow the shape of a logistic curve having a lower left plateau for attenuated (type 1) behaviour and transitioning through type 2 behaviour to a higher right plateau for water-hammer-dominated (type 3) behaviour. Through an extensive set of simulations covering a wide range of conditions, the study identifies the size of the critical outflow orifices associated with both type 1 and type 3 responses and assesses the influence of the location of the air valve on the transient magnitude and on the timing of air pocket collapse. Furthermore, the paper highlights that a non-slam air valve is capable of effectively mitigating transient magnitudes provided that its design parameters are judiciously chosen and account for both the system's attributes and the characteristics of the transient event. [ABSTRACT FROM AUTHOR]

The article deals with a diagnosis of multiple leaks from liquid transmission pipelines using analytical methods. Such solutions, based on advanced mathematical models of pipeline flow dynamics, usually turn out to be very complex and time-consuming. However, under certain operating conditions, a simpler approach may also be useful. Such an idea is presented in this paper, proposing two simplified methods for diagnosing double leakages. In principle, these methods apply to both simultaneous and non-simultaneous leaks. The first one uses a static model of a pipeline involving two leaks and takes advantage of the minimization of the objective function defined as the squared deviation of the modeled pressures from the pressures measured on the pipeline. The second method uses a pipeline flow model of a static type in combination with a gradient indicator aimed at detecting leaks and employing algorithms assigned to determining the location and size of leaks. The results of methods' validation, based on tests carried out with the use of measurement data obtained from an experimental water pipeline, were also presented. The outcomes of the performed tests proved the methods' effectiveness in terms of detection, isolation, localization, and intensity estimation of both simultaneous and non-simultaneous double leakages. [ABSTRACT FROM AUTHOR]

This paper investigates the use of Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) to find the best route for a pressurized water transmission line. When planning a water transfer project, it is essential to consider economic, environmental, social, and technical criteria. The decision-making process involves comparing various options based on different criteria. Different approaches can be employed to analyze project criteria, allowing for the integration of technical, environmental, social, and other elements. In this research, the Analytical Hierarchy Process (AHP) was utilized as a strategic decision-making method to determine the optimal route for the water transmission pipeline in southern Iran, connecting the Persian Gulf to the central provinces. To identify the best route, a questionnaire was created to gather expert opinions on the key criteria influencing the water transmission route. The criteria were then weighted based on these opinions. Subsequently, the reclassified weighted layers of these criteria were overlaid within the GIS environment to identify the most cost-effective path among the available options. [ABSTRACT FROM AUTHOR]

This review paper investigates unprecedented recent investment and capital spending in cross-provincial water management and transfer infrastructure in Iran. Although numerous cross-provincial water transfer plans have been implemented in the country, the scale, approach, and stakeholders involved in this recent national plan are unprecedented. This notable national plan includes long water transfer pipelines that pass through seven provinces, aiming at transferring desalinated water from the Persian Gulf to the interior. Regarding the scale, there have been a few cases of transferring water at the cross-provincial level, but mostly across two provinces. The approach has also changed in this plan. Long-term efforts to construct dams for electricity or farming with limited geographical impact have been shifted to desalinating water, long-distance pipe-laying, large-scale electric-powered water pumping, and long-distance underground tunnels. Stakeholders have become more diverse, from the council members of a small village to high-level decision makers at the national level. This paper elaborates on these extraordinary alterations by exemplifying a few other contemporary case studies of water transfer plans in Iran. It also examines the fundamental logic, bottlenecks, and future scenarios of this large-scale plan. [ABSTRACT FROM AUTHOR]

Corrosion is still the most common contributor to failures in Water Distribution Networks (WDNs), causing detrimental techno-socio-economic impacts. Although the corrosion process has been the subject of several studies, factors influencing this process remain a source of contention due to the complexity of the process and its influence by the surrounding environment. Considering the prior reviews, this comprehensive review is considered an early attempt to thoroughly cover the most influential corrosion factors in water pipelines. Corrosion factors have been classified into three main categories: 1) environmental factors; soil factors, external factors, and stray current factors; 2) pipe-related factors, and 3) operational factors. A fault tree analysis diagram was used to map, discuss, and analyze all significant corrosion causes of the buried water pipelines to facilitate easy visualization from basic factors to their intermediate and parent factors. Furthermore, the techno-socio-economic impacts of corrosion on water pipelines and beyond are appropriately addressed to demonstrate the issue's multi-dimensional importance. The research is expanded to rank these factors using the fuzzy analytical hierarchy process to provide a better understanding of the currently focused research investigation and to enable the extraction of gaps and existing limitations in scholarly literature. The findings revealed that water quality is the most investigated factor, followed by electrical infrastructure and soil quality. Conversely, operational factors exhibit the greatest relative weight (0.428), followed by environmental factors (0.337). These findings highlight areas where further research is needed, and the article proposes potential directions for future studies to address these gaps. [ABSTRACT FROM AUTHOR]

Leak detection of water pipelines is of great significance to ensure water supply safety and conserve water resources. In the context of the prevalence of deep learning which can automatically extract features, deep learning-based acoustic leak detection methods are flourishing. The study compares the deep learning methods and traditional machine learning methods which need human-involved feature engineering, from aspects of detection performance, data requirement, and computational complexity. Furthermore, a hybrid leak detection method that integrates handcrafted features into deep learning networks is proposed. In this study, there are 70 features extracted from acoustic signals, constructing an extreme gradient boosting (XGBoost) classifier. Then three commonly used deep learning classifiers and the hybrid classifier are developed to carry out comparisons. Results show that handcrafted features, especially linear prediction features, still play an important role in acoustic leak detection at the current stage. The XGBoost classifier with key features can outperform deep learning classifiers and achieve acceptable computational complexity. Limited by insufficient data, deep learning classifiers cannot fully exploit their ability to directly extract useful features from model inputs (e.g., acoustic signals' waveforms in the time or frequency domain). The hybrid classifier, requiring neither careful selection of handcrafted features nor complex deep learning network structures, obtains the best detection performance using the same amount of data, showing great promise in practical applications. [ABSTRACT FROM AUTHOR]

Pipeline leakage and water deposits can cause serious consequences, such as environmental pollution, safety accidents, and economic losses. Therefore, effective detection of these flaws is of critical importance. Currently, most of the detection methods rely heavily on experienced inspectors and specialized equipment, which is labor-intensive and costly. To this end, this paper presents a one-dimensional residual convolutional neural network (1D-ResNet) based percussion method, for detecting pipeline leakage and water deposit. The proposed method uses sound produced by tapping the pipe as input to 1D-ResNet, which can directly extract features from the audio signal, avoiding hand-crafted feature extraction process. The effectiveness of the proposed method is validated through experiments, showing strong performance in pipeline fault detection. Furthermore, the 1D-ResNet method exhibits better classification performance and stronger noise robustness compared to other methods. In summary, this study presents a novel approach for the detection of pipeline leakage and deposit through the innovative introduction of 1D-ResNet deep learning technology, which has significant application prospects. [ABSTRACT FROM AUTHOR]

Buried parallel pipelines may cause environmental pollution and explosive accidents after leakage due to corrosion, third-party damage or other factors. Here, an actual oil pipeline in-situ is employed as a prototype to establish a two-dimensional incompressible leakage and diffusion model. The model was applied to analyze the effects of different parameters including pipe diameters, burial depths and flat spacing to other pipelines on the diffusion distance of spilled diesel fuel in porous media. The results show that the existence of natural gas pipeline can change both of the polluted shape and the distance range. The natural gas pipeline can effectively inhibit the diffusion range of diesel with the increase of pipe diameter and the decrease of buried depth. A smaller spacing between parallel transmission pipelines, also results in a reduced horizontal diffusion rang. Meanwhile, the dimensionless formulas are fitted based on the Π theorem regarding diesel diffusion range versus diameter of gas pipeline, buried depth, and spacing between parallel pipelines. This study can develop appropriate protocols for detecting and recovering contaminants from paralleling pipeline. [ABSTRACT FROM AUTHOR]