Pipeline Network Research Articles

Leak monitoring and positioning of pressurized water pipeline network

Due to the presence of numerous branches in the pressurized water pipeline network (PWPN), leak-caused negative pressure wave (NPW) has non-unique propagation path, so leak detection and positioning for PWPN is highly challenging. This paper proposes a leak monitoring and positioning method for PWPN based on the time delay matching of NPW propagation. Via introducing the NPW velocity equation, the PWPN is divided into short segments with equal NPW propagation duration, and then the shortest NPW propagation path from each discrete point, namely hypothetical leak point, to each deployed pressure transmitter, is efficiently numerically searched and then used to calculate the NPW arrival time and establish a contrastive time delay library (CTDL) in advance. A set of signal processing algorithm are developed to capture the NPW arrival time and compare these measured values with the pre-established CTDL to accurately position leak point in the PWPN. The effectiveness of this method is verified via leak localization experiments on a field PWPN with an area of 12 × 12 km and 1478 branches. The proposed method can achieve leak positioning with errors less than 100 m throughout the entire pipeline network when only six pressure transmitters are effective.

Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines

Recent increases in buried pipeline damage accidents due to third-party interference have significantly heightened attention towards buried pipeline monitoring. Especially, as the sudden damage can lead to large-scale leakage, there is a necessity for preemptive response and maintenance. However, the application of a structural health monitoring approach is difficult, since the extensive network of buried pipelines, stretching over thousands of kilometers, exhibits diverse noise environments and propagation characteristics. As a result, challenges within the buried pipeline system frequently lead to damages being overlooked. In this study, introduces a deep learning-based pipeline damage monitoring algorithm, specifically designed to early detection of accidents caused by third-party interference. This algorithm integrates a CNN-based anomaly detection model, advanced signal processing for data preprocessing, and TDoA-based source localization. The training and test data set are the acquisition under completely independent conditions, which has been experimentally validate for applicability across various environments for buried pipelines. Moreover, both the training and test dataset acquisition were performed using accelerometers on in-service buried pipelines, each with diameters of 1,100 mm, 1,200 mm, and 2,200 mm, extending over lengths ranging from approximately 200 to 500 meters. Despite the independent conditions of the datasets, our study yielded over 95% accuracy in early detection, with the results being in good agreement with the actual excavate locations.

Unsupervised gas pipeline network leakage detection method based on improved graph deviation network

Historical data has shown that the natural gas pipeline network, as a critical urban lifeline system, is susceptible to disasters such as earthquakes resulting in leakage. Graph neural network modeling provides frontier rapid detection solutions for pipeline leakage; however, the challenges of collecting gas network anomaly data for training limit the precision and robustness of current model. This research proposes an unsupervised gas leakage detection and localization method based on a contained preprocessing process, with a graph deviation model that combines a structural learning approach with a graph neural network to model the spatial dependence of the sensors based on an attention mechanism, and variational inference models the posterior distributions of the hyper-parameters to optimize the model and improve the model precision. Meanwhile, in the preprocessing stage, the automatic optimization strategy of Northern Goshawk Optimization (NGO) for the best parameters K and ɑ in the Variable Mode Decomposition (VMD) efficiently extracts the valid signals and ensures that the model gives full play to the detection and localization effectiveness. The gas pipeline network dataset constructed by Pipeline Studio was used for comparing the performance of the model in this study with other frontier models. The results demonstrate that the model in this research has competitive detection Precision (93.31%), Recall (70.82%), and F1-score (0.81), and the posterior distribution of the model parameters strengthens the gas leakage localization precision, which provides a comprehensive solution for subsequent decision-making and reduces the hidden hazard of leakage effectively.

Optimasi Jaringan Pipa Sumur ESP Pada Manifold A dan Manifold Satelite untuk Meningkatkan Laju Aliran Minyak dan Analisis Perubahan Tekanan di Lapangan J

The wells in Field J, using Electrical Submersible Pumps (ESP), have replaced their natural flow operations. To optimize this ESP well and pipeline network, the research focuses on adjusting the ESP pump frequency within the Pipesim simulator, increasing it to enhance oil flow rates. This study employs a quantitative approach, where field data is analyzed and input into a simulation model that replicates real-world conditions. The analysis encompasses changes in flow rates and pressures, comparing the base case to post-optimization simulations. The results indicate a substantial increase in the initial oil flow rate from 578 STB to 720 STB/d, with an increase of 142 STB/d, when the pump frequency is raised to 60 Hz. This frequency adjustment significantly boosts pump discharge pressure, providing the added energy needed to transport fluid to the surface. Keyword: esp; pipeline network; production optimization; production simulation

Modeling and Simulation of Drilling Gas Mixing Process with Various Conditions in Oil and Gas Pipeline Network

This research aims to model and simulate the gas drilling mixing process with various conditions in the inlet gas flow. This mixing process is very crucial because it can affect the phase stability in the storage tank. Based on this, the gas that has been mixed must be ensured that it is in a perfectly mixed condition before entering the storage tank. The simulation method used in this research is Computational Fluid Dynamics (CFD) with ANSYS Fluent software. The data used as input in this simulation includes flow velocity, temperature, pressure and drilling gas composition. The simulation results are expected to show that flow velocity and pressure have a significant effect on the drilling gas mixing process in the pipe flow. In addition, the differences in the composition of the drilling gas also have an impact on the mixing process, where the more diverse the composition of the drilling gas, the more difficult it is to mix the gas homogeneously. However, by using baffle plates as a barrier and agitator in the pipe, the simulation results show that the mixing of drilling gas can be significantly increased.

Application of GPR to Detect Lost Valve Chambers in a Utility Network

The exact geographic location of the fresh water transmission network of Dhaka WASA was not known to the agency. With time and development works, access to many such underground valve chambers were lost. Their recovery was usually done through manual digging and trenching. This was expensive, time- and labour-consuming, often led to traffic jams, and the rate of success was low. This study was conducted to use the non-intrusive method of Ground Penetrating Radar to locate those valves as an alternative. A total of 27 out of the 41 lost nodes in the network was successfully recovered and their precise location was recorded using RTK-GNSS. The article presents the detail procedure of locating the valve chambers of underground pipeline network. It also highlights how the lack of collaboration between agencies are causing water supply and maintenance problems to this day, and how infrastructure development of one agency may erode successes of another. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 139-151

Simultaneous removal of noise and correction of motion warping in neuron calcium imaging using a pipeline structure of self-supervised deep learning models.

Calcium imaging is susceptible to motion distortions and background noises, particularly for monitoring active animals under low-dose laser irradiation, and hence unavoidably hinder the critical analysis of neural functions. Current research efforts tend to focus on either denoising or dewarping and do not provide effective methods for videos distorted by both noises and motion artifacts simultaneously. We found that when a self-supervised denoising model of DeepCAD [Nat. Methods18, 1359 (2021)10.1038/s41592-021-01225-0] is used on the calcium imaging contaminated by noise and motion warping, it can remove the motion artifacts effectively but with regenerated noises. To address this issue, we develop a two-level deep-learning (DL) pipeline to dewarp and denoise the calcium imaging video sequentially. The pipeline consists of two 3D self-supervised DL models that do not require warp-free and high signal-to-noise ratio (SNR) observations for network optimization. Specifically, a high-frequency enhancement block is presented in the denoising network to restore more structure information in the denoising process; a hierarchical perception module and a multi-scale attention module are designed in the dewarping network to tackle distortions of various sizes. Experiments conducted on seven videos from two-photon and confocal imaging systems demonstrate that our two-level DL pipeline can restore high-clarity neuron images distorted by both motion warping and background noises. Compared to typical DeepCAD, our denoising model achieves a significant improvement of approximately 30% in image resolution and up to 28% in signal-to-noise ratio; compared to traditional dewarping and denoising methods, our proposed pipeline network recovers more neurons, enhancing signal fidelity and improving data correlation among frames by 35% and 60% respectively. This work may provide an attractive method for long-term neural activity monitoring in awake animals and also facilitate functional analysis of neural circuits.

Numerical analysis of the effect of hydrogen doping ratio on gas transmission in low-pressure pipeline network

In the context of a global shift towards clean energy, the integration of hydrogen into existing natural gas pipeline networks is essential for facilitating the energy transition. Specifically, low-pressure natural gas pipeline networks are crucial for transporting gas efficiently and with minimal losses from the source to end-users in nearby cities. The physical properties of hydrogen differ significantly from those of natural gas, which implies that introducing hydrogen into the pipeline network will modify operational parameters and impact the safety and efficiency of gas transmission. Therefore, this study concentrates on the efficiency of transporting and scheduling hydrogen-blended natural gas through existing low-pressure direct pipelines. Utilizing the BWRS (Benedict-Webb-Rubin-Starling) equation of state, mathematical models were developed to calculate gas properties and to simulate both the steady and transient states of hydrogen-blended natural gas networks. These models were then validated with TGNET software. This study examines a low-pressure natural gas transmission and distribution network that transports gas directly from a production site in Sichuan to urban areas. It conducts simulations of hydrogen blending and analyzes the impact of various hydrogen blend ratios on the transport and scheduling responses of the network. Research suggests that maintaining the hydrogen blend below 30% ensures the safe operation of low-pressure gas pipelines and prevents the transmission pressure from exceeding 3 MPa. Additionally, under a fixed energy flow output, blending hydrogen intensifies the challenges associated with pressure drops and temperature increases due to high flows in small diameter pipes, and further complicates the management of interruptions resulting from faults. Therefore, it is recommended that the hydrogen blend ratio be maintained at no more than 30%, and that accident handling procedures be established in advance to control pressure variations following a shutdown.

Design optimisation of a variable flow CO2 pipeline – A statistical approach

Pipeline transport has emerged as the most cost-effective method for transporting CO2 onshore. The CO2 flow rate is a key factor driving transport costs, underscoring the need to understand the impact of flow rate variability in CO2 pipeline networks on their design and economics. This paper presents an optimal pipeline design methodology for CO2 pipelines operating under variable flow that considers the probability distribution of CO2 flow rate and the pipeline length. The results imply that pipelines designed for optimal performance under variable flow rates often demand a higher level of overdesign compared to pipelines intended for steady-state conditions. Decision-makers must balance the trade-offs between pipeline oversizing and installing multiple pressure boosting stations, especially applicable to large transportation distances and projects of extended duration. The examination of different approaches to pipeline design reveals that a variable flow pipeline design based on mean flow rate is not recommended, because it is incapable of handling the maximum flow rate. This drawback is overcome by adopting the variable flow stochastic pipeline design presented in this paper.

Обробка даних на основі швидкого перетворення Фур’є в кореляційному течешукачі

Fast Fourier transform (FFT) algorithms are widely used in correlation leak detectors (CD) in the implementation of digital data processing. The combination of the computa-tional efficiency of FFT with modern high-performance mobile computing means pro-vides acceptable data processing time during leak detection. Due to the increase in the general wear and tear of pipeline networks in many countries, the requirements for reliabil-ity and efficiency in detecting and repairing damage are increasing. Meeting these re-quirements means complicating data processing algorithms to more fully take into ac-count the diversity of the acoustic environment. This is the reason for the special interest in FFT as a basic, universal algorithm, on the basis of which it is promising to further develop data processing in CD. The article describes the experience of the authors in the application of FFT in parametric CD, in particular, the use of FFT in spectral evaluation and digital filtering of signals, calculations of digital filters, calculation and further pro-cessing of correlation functions is considered.

Withdrawn: Transient modeling of natural gas in pipeline networks by two non-iterative explicit and implicit finite volume methods

This paper describes two non-iterative explicit and implicit finite volume methods (FVM) to calculate the unsteady characteristics of natural gas in distribution systems. The proposed finite volume model could be used to solve the gas network as a whole for each time step. The main advantage of this approach is that it can analyze gas distribution networks with less computational cost. In addition, contrary to the conventional iterative algorithms, it does not consist of any divergence problems (Elman et al., 1996). To achieve this goal, the explicit FVM is derived from the governing equations. Then, the linearization process is performed, and the numerical solution of the linear equations is developed by using the implicit FVM. Three classic test cases are exemplified to check the performance of the proposed methods. The results confirm that both explicit and implicit methods, even in complicated distribution networks, can predict the elements of unsteady natural gas flows. Furthermore, when the governing equations are linearized and the implicit FVM is applied, satisfactory results with less computational cost, compared to the explicit one, are produced.

Shapley value cost allocation model for multimodal freight transport carriers

The downstream petroleum products distribution is beset with significant challenges due to ageing pipeline infrastructure, pipeline vandalism and other logistical constraints. These challenges have given rise to soaring pump prices of premium motor spirit (PMS), product shortages and unavailability across some locations in Nigeria. Thus, deploying alternative transport modes for PMS distribution is explored to improve product distribution efficiency. The decision to combine inland waterway transport (instead of pipeline network) and road transport modes would activate the intrinsic advantages inherent in the multimodal transport system. However, the efficiency outcome of using multi-modes may be eroded if the multimodal transport operators compete (instead of collaborating) in service provisions. This research investigated cost efficiency in cooperative collaboration among multimodal transport carriers. We proposed collaboration among six multimodal transport operators. The aim of encouraging such a large-scale coalition (S) is the expectation that costs emanating from their joint operation would be reduced. We applied the Shapley value cost allocation method to distribute the costs of operation and profit to the collaborators. After the analysis, we observed that the unit cost for coalition S1 was reduced by N17.16 (5.10 %) million naira. Similarly, we observed respective reductions in unit costs for coalitions S2, …, S10. We observed a reduction in cost by N107.84 million naira, which represents a 6.15 % reduction in total unit cost for the multimodal transportation carriers. Thus, the observed cost efficiency represents savings due to distribution chain efficiency if the multimodal transport carriers collaborate to improve product availability. Working as a coalition would offset PMS pump price variation attributable to distribution chain inefficiency.

Organization оf Electrochemical Protection оf Steel Underground Pipelines Against Corrosionin the Gas Distribution Industry of the Republic of Belarus

Steel underground gas pipelines occupy a significant share of the total length of gas distribution pipelines, and therefore their maintenance in proper condition is a constant and very urgent task. Due to its global nature, the main influencing factor affecting the technical condition of any steel underground pipelines, including gas pipelines, is corrosion, primarily soil corrosion. To protect against it on pipeline networks, along with insulating coatings, electrochemical protection (ECP) is applied, that is, following the definition of STO [Standard of Organization] 17330282.27.060.001–2008, protection of metal against corrosion in an electrolytic environment, carried out by establishing a protective potential on it or eliminating the anodic potential shift from the stationary potential. In the gas distribution industry of the country, methods and means of electrochemical protection have been applied since the beginning of gasification and the construction of the first gas pipelines. All gas supplying organizations of the State Production Association for Fuel and Gasification “Beltopgaz” (six regional and Minsk city), which operate gas distribution facilities, have specialized corrosion protection services. These services provide maintenance of available ECP equipment, conduct electrical measurements on gas pipelines and corrosion studies of soils, and have certified laboratories. This paper is devoted to the analysis of domestic experience in organizing electrochemical protection of steel underground gas distribution pipelines, searching for promising directions for its improvement and increasing efficiency through the implementation of a unified industry technical policy, automation and telemechanization of ECP equipment in the general context of digital transformation, optimization of the timing and volume of maintenance.

Planning cargo flows via a main pipeline network in reliance on ensuring specified oil quality in terms of sulfur content

Planning cargo flows via a main pipeline network in reliance on ensuring specified oil quality in terms of sulfur content

Industrial Park low-carbon energy system planning framework: Heat pump based energy conjugation between industry and buildings

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability.

Research on the Method of Determining Rainfall Thresholds for Waterlogging Risk in Subway Stations

With the frequency of extreme rainfall increasing, the risk of waterlogging is significantly exacerbated in subway systems. It is imperative to first identify the rainfall threshold for waterlogging risk for subway stations in order to develop effective waterlogging prevention and control plans. This study focuses on Line 11 of the Beijing Subway, using InfoWorks ICM to construct a model of the research area and simulate waterlogging at various subway stations under different rainfall scenarios. The results indicate that there is a risk of waterlogging at Jinanqiao station, Moshikou station, and Beixinan station on Line 11. The accumulated water may enter the subway station through exits A, B, C, and D of Jinanqiao Station. The inlet sequence of Jinanqiao Station always follows A(B), C, and D, and the difference in waterlogging time for each outlet does not exceed 10 min. We derived the rainfall threshold formula for waterlogging risk at Jinanqiao subway station. Among the three influencing factors of topographic features, step height, and drainage capacity of the pipeline network, step height has a significant effect on increasing the rainfall threshold for waterlogging risk. The conclusions obtained can provide reference for the refined management of waterlogging risks in subway stations.

A Generalized Equivalence Method for the Calculation of Low-Frequency EMI on Pipeline Networks Considering Polarization Effect

A Generalized Equivalence Method for the Calculation of Low-Frequency EMI on Pipeline Networks Considering Polarization Effect

Enhanced antifouling and antibacterial performances of novel UV-curable polysiloxane/microcapsules/Ag composite coatings for marine applications.

Marine biological fouling is a widespread phenomenon encountered by various oceanic ships and naval vessels, resulting in enormous economic losses. Herein, novel 4,5-dichloro-2-octyl-isothiazolone@sodium alginate/chitosan microcapsules (DCOIT@ALG/CS) were prepared through composite gel method using DCOIT as core materials, ALG and CS as shells, and CaCl2 as the cross-linking agent. The formed microcapsules (MCs) with Ag nanoparticles (AgNPs) were then filled in UV-curable polysiloxane (UV-PDMS), followed by UV irradiation to yield UV-PDMS/microcapsules/AgNPs (UV-PDMS/MCs/Ag) composite coatings. The constructed micro-nano dual-scale surface using the MCs and AgNPs improved the antifouling and antibacterial properties of UV-PDMS/MCs/Ag coatings. The as-obtained UV-PDMS/MCs/Ag coatings exhibited a static contact angle of about 160°, shear strength of 2.24 MPa, tensile strength of 3.32 MPa and elongation at break of 212%. The synergistic bacteriostatic effects of DCOIT and AgNPs in UV-PDMS/MCs/Ag coatings resulted in a bactericidal rate of 200 μg ml-1 towards Escherichia coli and Staphylococcus aureus with saturation at 100% within 10 min. In sum, the proposed composite coatings look promising for future marine transportation, pipeline networks and undersea facilities.

Prediction of slug length distribution in horizontal large-diameter gas/liquid pipeline systems

Large-diameter (ID ≥ 0.1524 m) pipeline network systems are widely used in the food industry for grain transportation, in the chemical and nuclear industries for steam/water thermal distribution systems, in the oil and gas industry for crude oil transportation and exploitation, and in urban water supply and drainage systems. Due to the favorable operational and geometrical conditions in surface gas/liquid pipelines, slug flow often prevails, dominating the horizontal and slightly inclined flow pattern map. Formed liquid slugs at large-diameter pipeline entrances and dip can grow, accelerate, and overtake other slugs, resulting in high-momentum and long slugs ranging in length from a few meters to a few hundred meters. The discrepancy of current mechanistic models to predict slug length in large diameter pipes, and the scarcity of large diameter slug length data motivated this work to propose a combined mechanistic and empirical approach to predict full slug length distribution in large diameter surface pipeline systems. A first-principle mechanistic mean slug length model is improved by optimizing its closure relationships using a large-diameter lab and field slug length database. Furthermore, an empirical large-diameter slug length standard deviation dimensionless model is proposed and statistically validated. The proposed mean and standard deviation slug length models are combined to construct a probability distribution and maximum slug length models. Validation of the proposed model revealed an absolute average error of 27% and 31% for the mean and maximum slug lengths, respectively, vastly outperforming all existing models.

Transmission Pipeline Planning from Kregan Water Treatment Plant to Watu Gadjah Tank by PERUMDA PDAM Sleman Using EPANET 2.2

Clean water is vital as it directly impacts health and influences various aspects of life, including social, economic, and cultural activities. To address the clean water supply requirements in the area, PERUMDA PDAM Sleman, Yogyakarta, is currently planning for a transmission network linking the Kregan WTP to the Watu Gadjah Tank. The pipeline planning is carried out to ensure efficient and optimal network performance. The pipeline planning use both primary and secondary data. The primary data was obtained from surveys, while the secondary data was obtained though previous research and planning, regulation, and from the other sources. The pipeline network analysis carried out using EPANET 2.2. software. EPTANET was selected because of its cost-effectiveness and robust capabilities in modeling drinking water networks, with the advantage of being re-programmable. The results of the analysis are then adjusted to the planning standards that applicable in Indonesia. Risk analysis was carried out based on experience and expert opinion. The results of the planning showed that the maximum pressure on the transmission network was 84.21m on Pendowo Road. The velocity between 1.03 m/s and 1.63 m/ss with the highest headloss was 10.33 m/km. Based on the results of the planning, the entire parameter meets the standard of the applicable planning criteria. The results of the analysis indicate that the technical specifications of the pipe used must be able to withstand a pressure of 92,63m. There was a potential risk that network performance may be disrupted both in terms of network performance, implementation of development, and operational stages. The re-examination and the development of operational standard procedures is needed to ensure that planning has been in line with the expectations, can be implemented, and can be operated efficiently.