Leakage Management Research Articles

Water Leakage Detection and Management System

In this era of globalization, water leakage significantly contributes to water waste and can adversely affect the environment if left unchecked. Much water is wasted due to unsupervised and poorly managed actions. The primary objective of leak detection is to locate leaks and measure their dimensions in secured products and systems. The process begins with hardware development, which is crucial for obtaining raw data by measuring water flow velocity at specific crack locations. Software-based methods use computer systems to monitor pressure and flow rate data to detect leaks continuously. This involves converting the velocity data into audio frequency signals. The gathered signals are then processed in MATLAB, a high-performance technical computing software, through four phases: segmentation, pre-processing, feature extraction, and classification, with Internet of Things (IoT) features integrated. Finally, the data is analyzed using the k Nearest Neighbour (kNN) classifier for classification.

The effect of water loss on demand–supply departure of Addis Ababa, Ethiopia

Water scarcity is a critical issue affecting numerous regions, including Addis Ababa, Ethiopia. The city’s growing population and rising water consumption place significant pressure on its existing water infrastructure. This study evaluates the impact of water loss due to leakages in the distribution system on the demand–supply gap in Addis Ababa, utilizing the WB EasyCalc software for analysis. Data analysis from 2023 indicate that monthly water loss contributes to an additional unmet demand of 5 million cubic meters (MCM), emphasizing the urgent need for effective water loss reduction strategies to enhance supply reliability. Currently, the city achieves only 42% water supply coverage, with an average loss of 300 L per connection per day and approximately 25,000 L lost per kilometer of pipeline daily. Furthermore, non-revenue water (NRW) accounts for 41.8% of total production, with real losses constituting 64%. The Infrastructure Leakage Index (ILI) score of 27 underscores the critical need for targeted leakage management interventions. The significance of this research lies in its quantification of water loss and its direct correlation with unmet demand in Addis Ababa. The findings provide essential insights for policymakers and stakeholders, highlighting the necessity for improved infrastructure management and strategic interventions to bolster water supply reliability. Implementing effective leakage reduction measures could substantially mitigate water scarcity challenges, thereby supporting sustainable urban development and enhancing public health outcomes in a city where access to clean water is vital for economic and social stability.

Additional over-the-scope-clip closure can shorten endoscopic vacuum therapy for postoperative upper gastrointestinal leakage.

Background and study aims Endoscopic vacuum therapy (EVT) has become the most effective therapeutic option for upper gastrointestinal leakage. Despite its efficiency, this treatment can necessitate a long hospitalization. The aim of this study was to evaluate whether additional use of an over-the-scope-clips (OTSC) closure after successful EVT can shorten leakage therapy. Patients and methods All patients treated with EVT for leakages in the upper gastrointestinal tract at our center from 2012 to 2022 were divided into two propensity matched cohorts (EVT+OTSC vs. EVT only). The EVT+OTSC patients received OSTC application at the end of successful EVT directly after removal of the last sponge. The primary endpoint was the time interval from leakage diagnosis until discharge. Secondary endpoints included EVT efficacy, complications, and nutritional status at discharge. Results A total of 84 matched patients were analyzed. EVT efficacy was 100% in both groups. The time interval from leakage until discharge was significantly shorter in the EVT+OTSC vs. EVT group (33 [19-48] vs. 46 days [29-77] P = 0.004). No patient in the EVT+OTSC group required additional procedures for leakage management, whereas five (12%) in the EVT group needed additional stent placement ( P = 0.021). More patients could be discharged on sufficient oral nutrition in the EVT+OTSC group (98% vs. 60%; P < 0.001). Conclusions The addition of OTSCs after successful EVT is safe and has the potential to shorten leakage therapy, enabling earlier discharge along with better functional outcomes.

Advancing nodal leakage estimation in decentralized water networks: Integrating Bayesian optimization, realistic hydraulic modeling, and data-driven approaches

The expansion of cities escalates the demand on water utilities amid global water scarcity, making leakage management a critical challenge for water sector sustainability. On this subject, the study introduces an advanced practical approach for estimating nodal leakage through the calibration of decentralized networks using available data. The approach includes conducting a pressure step-test for data collection, employing the DBSCAN algorithm for outlier detection, and employing Bayesian optimization for effective calibration. Hydraulic leakage models, particularly the power and modified orifice equations, are innovatively applied to model and identify existing leaks at each network node through deep comparison. The computational analysis demonstrated efficiency and accuracy in overcoming vast computational complexity and time constraints, with the calibration of a real-life network resulting in a cumulative MSE of 0.892 and an average R² and NSE values near 0.98. Additionally, realistic leakage modeling revealed inaccuracies in the acknowledged connection between the modified orifice equation and the leakage power equation. This study also provides key insights for enhancing water loss management and conducting on-site inspections in an environmentally conscious manner, especially crucial for budget-constrained water utilities and authorities experiencing revenue declines.

Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery.

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

From advanced hydraulic modelling to performance indicator for the efficiency of investments in leakage management of pressurized water systems

IntroductionThe digital transition is meant as the review of processes using digital technologies and strategies to increase efficiency based on a simpler collection of representative data. The process of interest here is the leakage management in systems distributing water. ObjectiveTo develop a novel key performance indicator (KPI) for leakage management considering the needs of ongoing digital transition in the water sector, which is opening a new era in the management of drinking water infrastructures. MethodologyThe novel KPI, named Asset Management Support Indicator, is developed starting from advanced hydraulic modelling and the physical laws governing leakage outflows, in order to be physically based and rational for increasing the efficiency of leakage management activities using representative process data. ResultsThe Asset Management Support Indicator supports effective leakage management strategies by driving towards efficiency, as discussed and demonstrated using several real case studies. ConclusionThe novel indicator is consistent with the digital transition perspective and current need of increasing the efficiency of water utilities, and it is also suitable to be adopted by water authorities to benchmark their performances, because it overcomes the weaknesses of traditional KPIs.

Review of research on performance indicators for water utilities

Abstract Water utilities are affected by various social, environmental, and technological factors and are increasingly required to enhance their infrastructure and long-term efficiency. Performance indicators are useful tools for assessing the operational, financial, environmental, and social aspects of water systems. Given this background, the author reviewed the literature on performance indicators for the water sector and summarized the research trends as follows. As a perspective, there are a lot of good mathematical and theoretical studies on distribution pipes and leakage management. Future research should address the problems of water utilities, by using multiple levels of performance indicators including social and environmental context in the long term. Asset management and utility management studies address diverse and current problems faced by water utilities. However, there is still room for improvement in standardizing the methodology for data collection, processing, and integration. In addition, it is recommended for future research, to include carbon neutrality aspect, to include pipeline materials and soil information in leakage management, to extend asset management studies to treatment plants, with including additional indicators about human and financial resources.

Leakage Risk Assessment of Urban Water Distribution Network Based on Unascertained Measure Theory and Game Theory Weighting Method

Assessing the risk of water leakage within urban water distribution networks (UWDN) is crucial prior to implementing any control measures. Conducting a risk assessment facilitates the development of effective water leakage management plans. By comprehensively analyzing the probability and loss factors that contribute to the risk of leakage in UWDN, this paper presents an evaluation index system for pipeline leakage risk. This index system utilized both quantitative and qualitative data on influencing factors derived from an actual pipeline network. In order to determine the precise level of pipeline leakage risk, an index theory-based pipeline leakage risk evaluation model was established. This model consisted of a single-index measure function and a multi-index comprehensive measure vector. The combined weight of evaluation indices through game theory was used to determine the weight of each index, thereby minimizing the negative effects of a single weight determination method. A risk assessment model that evaluated the leakage risk of specific pipelines was constructed based on actual data from the water distribution network in a certain area of China. The analysis showed that the risk of pipeline leakage in this area was mainly classified as a third-level risk, which is consistent with the actual evaluation results obtained from field visits.

Properties and Application of Chemical Grouting Materials for Construction Joint Leakage

This paper aims to disclose the properties of the chemical grouting materials under different conditions. Firstly, the water-soluble polyurethane (WPU) slurry, oil-soluble polyurethane (OPU) slurry, epoxy resin (EP) slurry, a slurry mixed by WPU slurry, and OPU slurry marked as (WPU + OPU) slurry were selected to test the properties. The setting time, volume shrinkage, and adhesive strength were measured under room conditions and water conditions. Then, the tunnel construction joints were simulated, and the injectability and water blocking effect were analyzed to evaluate the suitability of the different grouting slurry. In addition, the results were applied in the water leakage management of the Taihu Lake tunnel project. The test results showed that each of the four materials had its performance advantages and disadvantages and provided vital guidance for the engineering application.

The Water–Energy Nexus of Leakages in Water Distribution Systems

Leakages in water distribution systems (WDSs) profoundly affect their operations, elevating water production demand and treatment and pumping costs. Moreover, they strain the energy system by increasing power requirements at pumping stations. In regions heavily reliant on hydropower, such as Brazil, there is a nuanced implication: diminishing reservoir water levels due to increased WDS flow withdrawal. This not only immediately affects hydropower generation by reducing available head but, over time, may lead to interruptions in hydropower generation. This paper investigates the water–energy nexus, specifically focusing on WDS leakages in Brazil. It begins with an overview of the current situation and future outlook, considering evolving policies to enhance WDS efficiency and also the evaluation of different climate change scenarios. A more in-depth case study explores a reservoir utilized for both energy and water production. In this context, leakage management assumes critical importance, given the various water uses within the reservoir that impact the available energy and water resources. Overall, this study offers a comprehensive perspective on the water–energy nexus within WDSs, underscoring the critical importance of leakage control and its direct and indirect consequences, particularly on energy generation capacity, the environment, and the economy.

Anthropogenic activities and COVID-19 effects on natural water bodies: Arroyo Seco’s case

Concerns about pollution in Arroyo Seco, an important natural water body in the Monterrey Metropolitan Area, have been reported due to possible wastewater discharges and solid waste mismanagement, turning the river into a potential public health hazard in the context of the COVID-19 pandemic. As a result, a volunteer clean-up campaign denominated “Arroyo Vivo” has been promoted by Distrito Tec and the Campana-Altamira initiative. To aid in the efforts, ammonium, nitrates, nitrites, and sulfate concentrations, total solids, and total organic matter were measured in parallel with SARS-CoV-2 detection and quantification in river water samples. Compared with applicable regulations (NOM-001-SEMARNAT-2021, NOM-127-SSA1-2021), total suspended solid levels (55-365 mg/L) were found above the maximum limit (84 mg/L), while ammonium (0.0175-0.198 mg/L), nitrite (0.0585-0.169 mg/L), nitrate (1.065-3.285 mg/L), sulfate (91.2-111 mg/L), and chemical oxygen demand (16.95-43.1 mg/L) were consistently below the maximum limits (0.50, 0.90, 11.00, 84.00, 400.00, and 100.00 mg/L, respectively), showing that no large-scale wastewater discharges had taken place in Arroyo Seco. However, SARS-CoV-2 genetic material was detected at three sampling sites, indicating some degree of sewage leakage and inadequate management of solid waste containing respiratory fluids of infected patients. While reports indicate that water bodies are not sources of SARS-CoV-2 infection in surrounding populations, it proves that waste disposal policies must be enforced more strictly to ensure water quality and environmental protection towards sustainable development. Nonetheless, continued efforts to screen concerning contaminants are still needed to fully assess the environmental status of the stream and propose relevant public policy.

Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems

The reduction of pipe leakage is one of the top priorities for water companies, with many investing in greater sensor coverage to improve the forecasting of flow and detection of leaks. The majority of research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), with the aim of identifying bursts after occurrence. This study is a step towards development of ‘self-healing’ water infrastructure systems. In particular, the concepts of machine-learning (ML) and deep-learning (DL) are applied to the forecasting of water flow in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset for ~2500 DMAs in Yorkshire, containing flow time-series recorded at every 15-minute interval over the period of a year. Firstly, the method of isolation forests is used to identify anomalies in the dataset which are verified as corresponding to entries in water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid framework of DL models - such as recurrent neural networks (RNNs) and transformer neural networks) - and state-space ML algorithms - such as Kalman filter and autoregressive integrated moving average (ARIMA). The ML algorithms are trained to forecast the stationary component of the expected flow patterns in real-time, which is then combined (through Bayesian updating) with the non-stationary component obtained from DL models. As well as providing expected day-to-day flow demands, this framework aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies which best utilise resources to minimise leakage and disruptions by addressing both detected and predicted burst events.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices.

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Anatomic patterns of anastomotic leaks after Ivor Lewis esophagectomy for cancer: Impact on management and outcomes

BackgroundAnastomotic leakage presentation after Ivor Lewis esophagectomy may vary on imaging. Such variations may influence anastomotic leakage management and outcomes. MethodsAll consecutive patients who underwent Ivor Lewis esophagectomy for cancer between 2012 and 2019 in 2 referral centers were included. Anatomical patterns of anastomotic leakage were defined on imaging as follows: eso-mediastinal anastomotic leakage was a leak contained in the posterior mediastinum, eso-pleural anastomotic leakage was a leak involving the pleural cavity, and eso-bronchial anastomotic leakage was a leak communicating with the tracheobronchial tract. According to the Esophageal Complications Consensus Group definition, management and 90-day mortality were evaluated according to these patterns. ResultsAmong 731 patients, 111 (15%) developed anastomotic leakage consisting of eso-mediastinal anastomotic leakage (n = 87, 79%), eso-pleural anastomotic leakage (n = 16, 14%) and eso-bronchial anastomotic leakage (n = 8, 7%). There was no difference among these groups regarding preoperative characteristics or time to anastomotic leakage diagnosis. There was a significant difference in initial management according to anastomotic leakage anatomic patterns (P = .001). More than half of patients who experienced eso-mediastinal anastomotic leakage (n = 46, 53%) were initially treated conservatively without requiring intervention (Esophageal Complications Consensus Group type I), whereas most patients with eso-pleural anastomotic leakage (n = 14, 87.5%) and all with eso-bronchial anastomotic leakage (n = 8, 100%) initially required interventional or surgical treatment (Esophageal Complications Consensus Group type II–III). Anastomotic leakage anatomic patterns had a statistically significant impact on 90-day mortality, intensive care unit stay, and total hospital stay (P < .001). ConclusionAnastomotic leakage anatomic patterns after Ivor Lewis esophagectomy influence outcomes. Further studies are warranted to validate it in a prospective setting. Anastomotic leakage anatomic patterns may help in guiding anastomotic leakage management.

Treatments for Staple Line Leakage after Laparoscopic Sleeve Gastrectomy.

The number of laparoscopic sleeve gastrectomies (LSGs) performed in patients with obesity who are eligible for bariatric and metabolic surgery is currently much lower in Japan than in other countries. Considering the large number of potential patients with obesity and type 2 diabetes and the unique Japanese national health insurance system that guarantees fair healthcare delivery, there is room to increase the number of LSGs in Japan in the near future. However, strict health insurance regulations may limit access to mandatory devices needed to treat postoperative complications, such as staple line leakage, which can cause severe morbidity and even mortality. Therefore, understanding the pathogenesis and treatment options for this complication is crucial. This article examined the current situation in Japan and its impact on staple line leakage management, including the role of endoscopic treatment in reducing reoperation. The authors suggest increasing education and collaboration between healthcare professionals to optimize management and improve patient outcomes.

Assessment of the efficacy of Handmade Vacuum-Assisted Sponge Drain for Treatment of Anastomotic leakage after Low Anterior Rectal Resection.

Anastomotic leakage is one of the major complications of colorectal surgery, which might lead to reoperation, increased hospital stays, further intervention and mortality. Vacuum-assisted closure by devices such as Endo-SPONGE® produced by (B-Braun Medical B.V.) is currently being used to treat leakage and fistula. In this study, we aimed to assess the handmade vacuum-assisted sponge drain for anastomotic leakage following low anterior resection. This prospective study included 22 patients who had undergone sponge drain placement to treat anastomotic leakage. All patients had anastomotic leaks or defects after left anterior rectal resection (LAR) without ileostomy. They were treated with neo-adjuvant chemotherapy before the surgery and then subjected to rigid recto-sigmoidoscopy for 30days following the operation. Any sign of leakage, such as perianal and pelvic pain, was immediately identified and followed up with a CT scan and another recto-sigmoidoscopy. Twenty-two patients were enrolled in this study, 12 men (54.5%) and 10 women (47.4%). All patients had received neo-adjuvant chemotherapy with an average follow-up of 22.30 ± 3.81. 75% of patients (15 cases) were successfully treated, and 17 patients (85%) underwent successful ostomy closure. Treatment failed in 5 patients (25%), including three men and two women. This study shows that handmade vacuum-assisted sponge drain is a cost-effective method of anastomotic leakage management with efficacy similar to that of Endo-SPONGE®.

Socio-economic aspects of hydrogen energy: An integrative review

Hydrogen can be recognized as the most plausible fuel for promoting a green environment. Worldwide, developed and developing countries have established their hydrogen research, investment, and policy frameworks. This analysis of 610 peer-reviewed journal articles from the last 50 years provides quantitative and impartial insight into the hydrogen economy. By 2030, academics and business professionals believe that hydrogen will complement other renewable energy (RE) sources in the energy revolution. This study conducts an integrative review by employing software such as Bibliometrix R-tool and VOSviewer on socio-economic consequences of hydrogen energy literature derived from the Scopus database. We observed that most research focuses on multidisciplinary concerns such as generation, storage, transportation, application, feasibility, and policy development. We also present the conceptual framework derived from in-depth literature analysis as well as the interlinkage of concepts, themes, and aggregate dimensions, to highlight research hotspots and emerging patterns. In the future, factors such as green hydrogen generation, hydrogen permeation and leakage management, efficient storage, risk assessment studies, blending, and techno-economic feasibility shall play a critical role in the socio-economic aspects of hydrogen energy research.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Integrated Smart Management in WDN: Methodology and Application

Urbanization and population growth have been responsible for a significant increase in consumption of water and energy at a global scale. A careful management of water resource and infrastructures is crucial for Energy Transition, as well as for achieving a sustainable efficiency of these systems. High pressure values along with the ageing of the systems contribute to high leakage levels of Water Distribution Networks (WDN). The simultaneous control of excess pressure and containment of water losses are mainly performed by using Pressure Reducing Valves (PRVs) in WDN, which dissipate the surplus of hydraulic energy. Instead of being dissipated, energy can be recovered by the transformation of the excess pressure into electrical energy with the use of Pump as Turbines (PAT), which results in an increased reliability, reduction of cost and an overall improvement in the efficiency of WDN. The work aims on presenting an integrated efficiency management methodology in terms of Effectiveness (E), capability (ηpi), reliability (μpi) and sustainability (χpi) with values between 47 to 98%, also associated with the pressure and leakage management, and energy recovery. This research presents a modelling of a real WDN of a District Metering Areas (DMA) of Beloura endowed with seasonal consumption variability to better show its applicability. Additionally, an economic analysis to assess the solution’s feasibility is presented concluding an annual energy recovered of 9.8 MWh and a saving of about 30% of water leakage, which correspond, in the analyzed case study, to about 3523 m3. The payback period found is around 9 or 12 years, for only one PAT or two different PATs installed, due to the small available energy of the analyzed case study. Acknowledging the synergy between water and energy efficiency and taking advantage of these integrated smart management methodology exemplification, it resulted in more efficient systems to achieve both effectiveness solutions, digital and energy transition in the water sector.

Optimum location for energy recovery and leakage reduction in water distribution networks

Abstract The excess pressure in water distribution networks (WDNs) can be utilized to reduce leakage and produce energy by installing energy recovery devices (ERDs) like microturbines or pumps-as-turbines. The major challenge in adopting ERDs in pipe networks is to locate the optimum location for their placement. An optimization procedure is proposed in this work to find the optimum locations of energy recovery (ER) and leakage reduction (LR). The optimization method is based on the Rao algorithm, written in Python, coupled to an EPANET-implemented hydraulic model. With optimal ER of 543.46, 648.24 and 154.58 kWh/day and LR of 61.1, 13.6 and 6.8%, three benchmark networks – the 5-node, the 25-node and the 46-node network – serve as the basis for validating the proposed approach which converges to the best solution in only 2,000, 4,000 and 10,000 function evaluations. For large networks, solutions with two ideally located devices gave higher ER than three imperfectly located devices. This methodology is verified to give optimal ER locations and can be applied to any pipe network regardless of size and location. This work made it easier to analyse the potential for ER in WDNs and promotes the use of sustainable leakage management methods.