Prediction Of Leakage Research Articles

Development of the Quantitative Property-Consequence Relationship Model for Prediction of Hydrogen Leakage and Dispersion Using Response Surface Method and Artificial Neural Network Approaches.

Hydrogen, an environmentally friendly and highly regarded future energy source, can form flammable vapor clouds upon leakage, which may transition into explosion. Predicting the dispersion behavior of hydrogen is crucial for preventing such incidents. This study aims to develop a quantitative property-consequence relationship (QPCR) model using the response surface method (RSM) and artificial neural network (ANN) to swiftly and accurately predict dispersion behavior. Initially, 8 variables were defined from source and dispersion models, constructing a data set through 6,561 PHAST simulations. Subsequently, the RSM-BBD (Box-Behnken design) and ANN-BPNN (Backpropagation neural network) models were developed, alongside a hybrid model incorporating BPNN after excluding four low-influence variables based on analysis of variance (ANOVA). All models achieved an R2 value exceeding 0.99. The hybrid model notably reduced computational costs by 97% compared to ANN-BPNN and exhibited lower mean square error (MSE). These results introduce a cost-effective approach for high-accuracy QPCR modeling and highlight the viability of diverse statistical methods.

Real-time leakage predictions of pneumatic controlled actuator during cycle test using machine learning

Real-time leakage predictions of pneumatic controlled actuator during cycle test using machine learning

A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.

Research on Time Series Steel Leakage Prediction Model Based on TS-GA Optimization Decision Tree

Research on Time Series Steel Leakage Prediction Model Based on TS-GA Optimization Decision Tree

Negative and Positive Predictors of Anastomotic Leakage in Colorectal Cancer Patients-The Case of Neutrophil-to-Lymphocyte Ratio.

Colorectal surgery for cancer is associated with a high rate of surgical complications, including anastomotic leakage. The ability to predict the risk of leakage early enough seems to be of high value, since it would facilitate the design of personalized treatment and duration of hospitalization. Although different studies present the neutrophil-to-lymphocyte ratio [NLR] as having a strong predictive value, there is a discrepancy with respect to which postoperative day is the most reliable. We evaluated a series of NLR values, from the day before surgery up to the POD7, in a cohort of 245 colorectal surgery patients in order to clarify the best predictable score for the identification of the risk of anastomotic leakage. There were 28 patients with leaks. ROC curve analysis of NLR on POD1 indicates that a cut-off point ≥ 7.4 exerts a negative prediction for leakage (AUC 0.881, sensitivity 68.7%, specificity 96.4%, PPV 28.4%, and NPV of 99.3%), thus excluding 150 patients from the risk of leakage. Furthermore, the ROC curve analysis of NLR on POD4 indicates that a cut-off point ≥ 6.5 gives a positive prediction of leakage (AUC 0.698, sensitivity 82.1%, specificity 51.6%, PPV 17.6%, and NPV of 95.6%), thus indicating 52 patients as being at high risk of leakage. Finally, NLR failed to identify five leaks out of twenty-eight. These results strongly indicate the ability of NLR on POD1 to predict patients at low risk of developing a leak and then on POD4 to predict the high-risk patients. This makes our study particularly innovative, in that it enables doctors to concentrate on potential high-risk patients from POD1.

An innovative three-dimensional computational fluid dynamics-iterative ensemble Kalman filter model for the prediction of heavy gas leakage and dispersion in enclosed workplaces: Case study for hydrogen sulfide leakage

An innovative three-dimensional computational fluid dynamics-iterative ensemble Kalman filter model for the prediction of heavy gas leakage and dispersion in enclosed workplaces: Case study for hydrogen sulfide leakage

A digital twin model of urban utility tunnels and its application

Background Multiple pipelines in utility tunnels may lead to various accidents and serious social impact. In the era of digitalization, how to better model the operation of a utility tunnel, dynamically predict the accident evolutions, and support corresponding decision-makings are essential issues. Methods In this study, a CFD-based digital twin framework for accidents in utility tunnels is proposed. First, Kalman filtering is applied to correct the parameter drift of sensors used for long-term monitoring. A data interaction system is then developed based on Internet of Things (IOT) and OPC Unified Architecture (OPC UA) to comprehensively manage data transmission within the utility tunnel. Subsequently, a natural gas leakage prediction model is developed to enable the efficient prediction of the spatial and temporal distribution in the case of leakage. Finally, these components are integrated for visualization in a digital twin platform for natural gas leakage in utility tunnels. Additionally, numerical simulations are employed to validate of the proposed method. Results The utility tunnel data transmission system based on IoT and OPC UA proposed in this paper is case-validated. By comparing the simulation results at 10 s, 20 s, 30 s, and 40 s, the model accurately predicts the methane concentration at the leak position after 10 seconds and maintains acceptable accuracy thereafter. The simulation results of different cases are introduced to verify the reliability of the risk indicator proposed in this paper, which increases with the leakage rate. Finally, A process for visualizing numerical simulation is proposed into a digital twin. Conclusions The proposed predictive digital twin technology facilitates the rapid risk assessment of and emergency management of natural gas accidents in utility tunnels. Based on the results of predictive model, a risk indicator is introduced to evaluate the natural gas accidents.

Prediction of Anastomotic Leakage after Right Colectomy Using Ph, Lactate and Glucose from Peritoneal Drain

Prediction of Anastomotic Leakage after Right Colectomy Using Ph, Lactate and Glucose from Peritoneal Drain

Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.

Characteristics analysis of leakage diseases of Beijing underground subway stations based on the field investigation and data statistics

With a continuous increasing of the underground water level of Beijing, many leakage diseases induced by the structural defect have been exposed in the subway tunnels that have been put into operation. The leakage diseases of underground subway stations may severely cause the safety and durability problems of the structure. Moreover, it could affect the passenger experience and seriously threaten the operation safety. Therefore, this paper collected the leakage states of a total of 258 underground subway stations in 16 lines of Beijing by field investigation, which include the number of average leakage points, leakage types and classification, distribution scenes of leakage, the construction methods and operation ages of subway stations and so on. Besides, in order to further investigate the causes of the leakage diseases, three typical cases of Beijing subway stations with severe leakage diseases, which were constructed by different methods, were selected to carry out the statistical analysis of leakage disease. The typical positions of leakage diseases of Beijing subway stations with different construction methods were presented by three-dimensional modelling, and the leakage characteristics of each station were analyzed. Finally, based on the field investigation and data statistics, a data-based Bayesian network leakage prediction model to evaluate the leakage risk of subway stations was constructed. This research can provide an intuitive reference for early warning, monitoring and prevention of leakage diseases of metro subways in operation.

The Application of Geosynthetics in Tailings Storage Facilities: A General Review

This paper is a summary of many of the key findings on the application of geosynthetics in tailings storage facilities. Topics include the compressibility and permeability of tailings, the equations predicting leakage through circular and non-circular geomembrane holes, the effect of the subgrade permeability, and the effect of a lateral drainage system within tailings on leakage predictions. Two commonly encountered engineering problems relating to the piping through circular geomembrane holes and the opening width of non-circular defective geomembrane seams are given to demonstrate the potential application of leakage prediction equations. Meanwhile, issues related to the subgrade imperfection and the long-term performance of both high-density polyethylene (HDPE) and bituminous geomembranes in tailings storage applications are addressed. The research highlights that an appropriate HDPE geomembrane liner can be expected to perform very well for an extremely long time, limiting leakage and contaminant migration from the facility into the surrounding environment if the liner is well constructed on a suitable subgrade.

The Abundance of Marine Plastic Debris in Indonesia’s Ocean

Abstract Plastic waste is a global problem expected to increase with population growth. An estimated 275 million metric tons per year of plastic waste are produced globally. Indonesia has the fourth largest population in the world and makes the most plastic waste. Indonesia’s government plans to reduce plastic waste by 70% by 2025. This scientific paper aims to show the amount of plastic production in Indonesia. The linear regression approach of the national marine plastic leakage trend from 2018 to 2022, it can be predicted that plastic waste leakage in 2025 will reach 0.228 million tons. The prediction of plastic waste leakage in 2025 states that Indonesia has gained 62.92%, which means that Indonesia has yet to achieve its goals by Presidential Regulation No. 83/2018. However, the leakage of marine plastic waste is predicted to have a value that will reach 0 by 2029, so it can be expected that Indonesia will become a country free of marine plastic waste in 2045. In addition, there is still a need for action to overcome plastic waste that has entered the marine environment and cooperation with countries to make Indonesia’s seas accessible to plastic waste.

Diagnostic biomarkers of early anastomotic leakage prediction after colorectal surgery

Diagnostic biomarkers of early anastomotic leakage prediction after colorectal surgery

Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study

BackgroundAnastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data.MethodsWe retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation.ResultsAmong 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02).ConclusionIn this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

Early drain removal after hepatectomy based on bile leakage prediction using drainage fluid volume and direct bilirubin level.

This study aimed to determine the value of the drainage fluid volume and direct bilirubin level for predicting significant bile leakage (BL) after hepatectomy and establish novel criteria for early drain removal. Data from 351 patients who underwent hepatic resection at Gunma University in Japan between October 2018 and March 2022 were retrospectively analyzed. Clinical characteristics and surgical outcomes of patients with and without significant BL were compared. Criteria for early drain removal were determined and verified. Bile leakage occurred in 27 (7.1%) patients; 8 (2.3%) had grade A leakage and 19 (5.4%) had grade B leakage. The optimal cut-off value for the drainage fluid direct bilirubin level on postoperative day (POD) 2 was 0.16mg/dL, which had the highest area under the curve and negative predictive value (NPV). Patients with BL had significantly larger drainage volumes on POD 2. The best cut-off value was 125mL because it had the greatest NPV. Patients in both the primary and validation (n=90) cohorts with bilirubin levels less than 0.16mg/dL and drainage volumes less than 125mL did not experience leakage. A drainage fluid volume less than 125mL and direct bilirubin level less than 0.16mg/dL on POD 2 are criteria for safe early drain removal after hepatectomy.

Evaluation of ANN model for pipe status assessment in drinking water management

ABSTRACT Non-revenue water due to pipe leakages presents a significant global challenge, impacting both the economy and environmental sustainability. The current approach to pipe management for water utilities in Sweden is mainly reactive; leaks are repaired when detected, sometimes with large costs if the leakage is extensive and critical. With this study, we want to focus on proactive pipe network management by using an Artificial Neural Network (ANN) model to estimate the probability of leakage in water pipes. The ANN model was trained on leaks that occurred over 10 years. A comparison with leaks reported after the training shows that the model succeeds in identifying groups of pipes with a higher leakage frequency. Evaluation of both new and historical leaks in four different water pipe networks in Sweden showed that a higher prediction value from the ANN model was linked to a higher occurrence of leakage. This indicates that the ANN model succeeds in identifying some of the combinations of attributes that lead to leakage. An evaluation of the input attributes in the ANN model found that the most important attributes for leakage prediction were pipe material, pipe age, adjacent problems on the pipe stretch, pipe length and pipe dimension.

Study on the sealing performance of high-pressure flanges by a magnetic measurement method of bolt axial force

Under high-pressure conditions, flange sealing often fails due to inadequate bolt pre-tension, leading to unstable pressurization or leakage. Assessing the integrity of high-pressure flange sealing presents challenges, constrained by operational conditions, complex equipment integration, and associated costs. This study introduces a method to evaluate flange sealing using magnetic measurements of bolt axial forces. A novel device developed using the magnetoelastic method efficiently and conveniently measures these forces under various internal pressures. The results show a linear relationship between magnetic signals and bolt axial forces at nominal pressures, whereas excessively high pressures cause anomalous changes in magnetic signals, indicative of bolt plastic deformation. Finite element simulation analyzed the changes in bolt axial force and gasket contact stress due to internal pressures, leading to an empirical formula that correlates these variables under varying pressures. The study also explored the impact of uneven pre-tensioning and gasket material on sealing contact stress. A sealing contact surface model, established based on actual roughness profile parameters, enabled the simulation and formulation of an exponential relationship between contact stress and sealing clearance. This foundation facilitated the creation of a leakage prediction model that incorporates bolt axial force parameters, thus enabling a quantitative assessment of flange sealing performance. This research provides a practical approach to evaluating the sealing performance of operational high-pressure flanges.

Prediction of tunnel localized water leakage influences on adjacent lateral pile responses in saturated clay

Prediction of tunnel localized water leakage influences on adjacent lateral pile responses in saturated clay

Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

The use of electrical stimulation devices to manage bladder incontinence relies on the application of continuous inhibitory stimulation. However, continuous stimulation can result in tissue fatigue and increased delivered charge. Here, we employ a real-time algorithm to provide a short-time prediction of urine leakage using the high-resolution power spectrum of the bladder pressure during the presence of non-voiding contractions (NVC) in normal and overactive bladder (OAB) cats. The proposed method is threshold-free and does not require pre-training. The analysis revealed that there is a significant difference between voiding contraction (VC) and NVC pressures as well as band powers (0.5–5 Hz) during both normal and OAB conditions. Also, most of the first leakage points occurred after the maximum VC pressure, while all of them were observed subsequent to the maximum VC spectral power. Kalman-Fuzzy method predicted urine leakage on average 2.2 s and 1.6 s before its occurrence and an average of 2.0 s and 1.1 s after the contraction started with success rates of 94.2% and 100% in normal and OAB cats, respectively. This work presents a promising approach for developing a neuroprosthesis device, with on-demand stimulation to control bladder incontinence.

Neural Network-Based Prediction of NH3 Leakage from SCR Systems for Diesel Engines

Neural Network-Based Prediction of NH3 Leakage from SCR Systems for Diesel Engines