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Keywords = networks of gas distribution pipelines

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32 pages, 10028 KiB  
Article
Natural Gas Heating in Serbian and Czech Towns: The Role of Urban Topologies and Building Typologies
by Dejan Brkić, Zoran Stajić and Dragana Temeljkovski Novaković
Urban Sci. 2025, 9(7), 284; https://doi.org/10.3390/urbansci9070284 - 21 Jul 2025
Viewed by 418
Abstract
This article presents an analysis on natural gas heating in residential areas, focusing on two primary systems: (1) local heating, where piped gas is delivered directly to individual dwellings equipped with autonomous gas boilers, and (2) district heating, where gas or an alternative [...] Read more.
This article presents an analysis on natural gas heating in residential areas, focusing on two primary systems: (1) local heating, where piped gas is delivered directly to individual dwellings equipped with autonomous gas boilers, and (2) district heating, where gas or an alternative fuel powers a central heating plant, and the generated heat is distributed to buildings via a thermal network. The choice between these systems should first consider safety and environmental factors, followed by the urban characteristics of the settlement. In particular, building typology—such as size, function, and spatial configuration—and urban topology, referring to the relative positioning of buildings, play a crucial role. For example, very tall buildings often exclude the use of piped gas due to safety concerns, whereas in other cases, economic efficiency becomes the determining factor. To support decision-making, a comparative cost analysis is conducted, assessing the required infrastructure for both systems, including pipelines, boilers, and associated components. The study identifies representative residential building types in selected urban areas of Serbia and Czechia that are suitable for either heating approach. Additionally, the article examines the broader energy context in both countries, with emphasis on recent developments in the natural gas sector and their implications for urban heating strategies. Full article
(This article belongs to the Special Issue Urban Building Energy Analysis)
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19 pages, 667 KiB  
Review
A Review of Optimization Methods for Pipeline Monitoring Systems: Applications and Challenges for CO2 Transport
by Teke Xu, Sergey Martynov and Haroun Mahgerefteh
Energies 2025, 18(14), 3591; https://doi.org/10.3390/en18143591 - 8 Jul 2025
Viewed by 389
Abstract
Carbon Capture and Storage (CCS) is a key technology for reducing anthropogenic greenhouse gas emissions, in which pipelines play a vital role in transporting CO2 captured from industrial emitters to geological storage sites. To aid the efficient and safe operation of the [...] Read more.
Carbon Capture and Storage (CCS) is a key technology for reducing anthropogenic greenhouse gas emissions, in which pipelines play a vital role in transporting CO2 captured from industrial emitters to geological storage sites. To aid the efficient and safe operation of the CO2 transport infrastructure, robust, accurate, and reliable solutions for monitoring pipelines transporting industrial CO2 streams are urgently needed. This literature review study summarizes the monitoring objectives and identifies the problems and relevant mathematical algorithms developed for optimization of monitoring systems for pipeline transportation of water, oil, and natural gas, which can be relevant to the future CO2 pipelines and pipeline networks for CCS. The impacts of the physical properties of CO2 and complex designs and operation scenarios of CO2 transport on the pipeline monitoring systems design are discussed. It is shown that the most relevant to liquid- and dense-phase CO2 transport are the sensor placement optimization methods developed in the context of detecting leaks and flow anomalies for water distribution systems and pipelines transporting oil and petroleum liquids. The monitoring solutions relevant to flow assurance and monitoring impurities in CO2 pipelines are also identified. Optimizing the CO2 pipeline monitoring systems against several objectives, including the accuracy of measurements, the number and type of sensors, and the safety and environmental risks, is discussed. Full article
(This article belongs to the Topic Oil and Gas Pipeline Network for Industrial Applications)
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21 pages, 4215 KiB  
Article
Real-Time Classification of Distributed Fiber Optic Monitoring Signals Using a 1D-CNN-SVM Framework for Pipeline Safety
by Rui Sima, Baikang Zhu, Fubin Wang, Yi Wang, Zhiyuan Zhang, Cuicui Li, Ziwen Wu and Bingyuan Hong
Processes 2025, 13(6), 1825; https://doi.org/10.3390/pr13061825 - 9 Jun 2025
Viewed by 545
Abstract
The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic [...] Read more.
The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic monitoring signals, leveraging a hybrid framework that combines the feature learning capacity of a one-dimensional convolutional neural network (1D-CNN) with the classification robustness of a support vector machine (SVM). The proposed method effectively distinguishes various pipeline-related events—such as minor leakage, theft attempts, and human movement—by automatically extracting their vibration patterns. Notably, it addresses the common shortcomings of softmax-based classifiers in small-sample scenarios. When tested on a real-world dataset collected via the DAS3000 system from Hangzhou Optosensing Co., Ltd., the model achieved a high classification accuracy of 99.92% across six event types, with an average inference latency of just 0.819 milliseconds per signal. These results demonstrate its strong potential for rapid anomaly detection in pipeline systems. Beyond technical performance, the method offers three practical benefits: it integrates well with current monitoring infrastructures, significantly reduces manual inspection workloads, and provides early warnings for potential pipeline threats. Overall, this work lays the groundwork for a scalable, machine learning-enhanced solution aimed at ensuring the operational safety of critical energy assets. Full article
(This article belongs to the Section Process Control and Monitoring)
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18 pages, 3582 KiB  
Article
A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
by Daqing Wang, Huirong Huang, Bin Wang, Shaowei Tian, Ping Liang and Weichao Yu
Appl. Sci. 2025, 15(8), 4464; https://doi.org/10.3390/app15084464 - 18 Apr 2025
Viewed by 438
Abstract
Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs [...] Read more.
Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs have received less attention, and existing risk assessment methodologies for GDSs may have limitations in providing accurate and reliable accident probability predictions and fault diagnoses, especially under data uncertainty. This paper introduces a novel dynamic accident probability assessment framework tailored for GDS under data uncertainty. By integrating Bayesian network (BN) modeling with fuzzy expert judgments, frequentist estimation, and Bayesian updating, the framework offers a comprehensive approach. It encompasses accident modeling, root event (RE) probability estimation, undesired event (UE) predictive analysis, probability adaptation, and accident diagnosis analysis. A case study demonstrates the framework’s reliability and effectiveness, revealing that the occurrence probability of major hazards like vapor cloud explosions and long-duration jet fires diminishes significantly with effective safety barriers. Crucially, the framework acknowledges the dynamic nature of risk by incorporating observed failure incidents or near-misses into the assessment, promptly adjusting risk indicators like UE probabilities and RE criticality. This underscores the importance for decision-makers to maintain a heightened awareness of these dynamics, enabling swift adjustments to maintenance strategies and resource allocation prioritization. By mitigating assessment uncertainty and enhancing precision in maintenance strategies, the framework represents a significant advancement in GDS safety management, ultimately striving to elevate safety and reliability standards, mitigate natural gas distribution risks, and safeguard public safety and the environment. Full article
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16 pages, 9321 KiB  
Article
Improved Deep Convolutional Generative Adversarial Network for Data Augmentation of Gas Polyethylene Pipeline Defect Images
by Zihan Zhang, Yang Wang, Nan Lin and Shengtao Ren
Appl. Sci. 2025, 15(8), 4293; https://doi.org/10.3390/app15084293 - 13 Apr 2025
Viewed by 432
Abstract
Gas polyethylene (PE) pipes have an become essential component of the urban gas pipeline network due to their long service life and corrosion resistance. To prevent safety incidents, regular monitoring of gas pipelines is crucial. Traditional inspection methods face significant challenges, including low [...] Read more.
Gas polyethylene (PE) pipes have an become essential component of the urban gas pipeline network due to their long service life and corrosion resistance. To prevent safety incidents, regular monitoring of gas pipelines is crucial. Traditional inspection methods face significant challenges, including low efficiency, high costs, and limited applicability. Machine vision-based inspection methods have emerged as a key solution to these issues. Despite this, the method also encounters the problem of scarcity of defect samples and uneven data distribution in gas pipeline defect detection. For this reason, an improved Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. By integrating the Minibatch Discrimination (MD), Spectral Normalization (SN), Self-Attention Mechanism (SAM) and Two-Timescale Update Rule (TTUR), the proposed approach overcomes the original DCGAN’s limitations, including mode collapse, low resolution of generated images, and unstable training, the data augmentation of defective images inside the pipeline is realized. Experimental results demonstrate the superiority of the improved algorithm in terms of image generation quality and diversity, while the ablation study validates the positive impact of the improvement in each part. Additionally, the relationship between the number of augmented images and classification accuracy, showing that classifier performance improved across all scenarios when generated defect images were included. The findings indicate that the images produced by the improved model significantly enhance defect detection accuracy and hold considerable potential for practical application. Full article
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22 pages, 32472 KiB  
Article
Multi-Scale Feature Fusion GANomaly with Dilated Neighborhood Attention for Oil and Gas Pipeline Sound Anomaly Detection
by Yizhuo Zhang, Zhengfeng Sun, Shen Shi and Huiling Yu
Information 2025, 16(4), 279; https://doi.org/10.3390/info16040279 - 30 Mar 2025
Viewed by 618
Abstract
Anomaly detection in oil and gas pipelines based on acoustic signals currently faces challenges, including limited anomalous samples, varying audio data distributions across different operating conditions, and interference from background noise. These challenges lead to reduced accuracy and efficiency in pipeline anomaly detection. [...] Read more.
Anomaly detection in oil and gas pipelines based on acoustic signals currently faces challenges, including limited anomalous samples, varying audio data distributions across different operating conditions, and interference from background noise. These challenges lead to reduced accuracy and efficiency in pipeline anomaly detection. The primary challenge in reconstruction-based pipeline audio anomaly detection is to prevent the loss of critical information and ensure the high-quality reconstruction of feature maps. This paper proposes a pipeline anomaly detection method termed Multi-scale Feature Fusion GANomaly with Dilated Neighborhood Attention. Firstly, to mitigate information loss during network deepening, a Multi-scale Feature Fusion module is proposed to merge the encoded and decoded feature maps at different dimensions, enhancing low-level detail and high-level semantic information. Secondly, a Dilated Neighborhood Attention module is introduced to assign varying weights to neighborhoods at various dilation rates, extracting channel interactions and spatial relationships between the current pixel and its neighborhoods. Finally, to enhance the quality of the reconstructed spectrum, a loss function based on the Structure Similarity Index Measure is designed, considering both pixel-level and structural differences to maintain the structural characteristics of the reconstructed spectrum. MFDNA-GANomaly achieved 92.06% AUC, 93.96% Accuracy, and 0.955 F1-score on the test set, demonstrating that the proposed method can effectively enhance pipeline anomaly detection performance. Additionally, MFDNA-GANomaly exhibited competitive performance on the ToyTrain and Bearing subsets of the development dataset in the DCASE Challenge 2023 Task 2, confirming the generalization capability of the model. Full article
(This article belongs to the Section Artificial Intelligence)
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15 pages, 3623 KiB  
Article
Preliminary Exploration on Short-Term Prediction of Local Geomagnetically Induced Currents Using Hybrid Neural Networks
by Yihao Fang, Jin Liu, Haiyang Jiang and Wenhao Chen
Processes 2025, 13(1), 76; https://doi.org/10.3390/pr13010076 - 1 Jan 2025
Viewed by 1003
Abstract
During extreme space weather events, transient geomagnetic disturbances initiated by solar eruptive activities can induce geomagnetically induced currents (GICs), which have severe impacts on power grid systems and oil/gas pipelines. Observations indicate that GICs in power grids are characterized by large fluctuation amplitudes, [...] Read more.
During extreme space weather events, transient geomagnetic disturbances initiated by solar eruptive activities can induce geomagnetically induced currents (GICs), which have severe impacts on power grid systems and oil/gas pipelines. Observations indicate that GICs in power grids are characterized by large fluctuation amplitudes, broad frequency ranges, and significant randomness. Their behavior is influenced by several factors, including the sources of space weather disturbance, Earth’s electrical conductivity distribution, the structural integrity and performance of power grid equipment, and so on. This paper presents a hybrid prediction using actual GIC data from power grids and deep learning techniques. We employ various technical methods, including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, to investigate short-term prediction methods for local grid GICs. The study uses GIC monitoring samples from 8 November 2004 for model training and testing. The results are evaluated using the coefficient of determination R2, root mean square error (RMSE), and mean absolute error (MAE). Preliminary research suggests that the combined CEEMDAN–CNN–LSTM–attention model significantly improves prediction accuracy and reduces the time delay in GIC prediction during geomagnetic storms compared to using LSTM neural networks alone. Full article
(This article belongs to the Section Energy Systems)
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14 pages, 290 KiB  
Article
Bayesian Assessment of Corrosion-Related Failures in Steel Pipelines
by Fabrizio Ruggeri, Enrico Cagno, Franco Caron, Mauro Mancini and Antonio Pievatolo
Entropy 2024, 26(12), 1111; https://doi.org/10.3390/e26121111 - 19 Dec 2024
Viewed by 815
Abstract
The probability of gas escapes from steel pipelines due to different types of corrosion is studied with real failure data from an urban gas distribution network. Both the design and maintenance of the network are considered, identifying and estimating (in a Bayesian framework) [...] Read more.
The probability of gas escapes from steel pipelines due to different types of corrosion is studied with real failure data from an urban gas distribution network. Both the design and maintenance of the network are considered, identifying and estimating (in a Bayesian framework) an elementary multinomial model in the first case, and a more sophisticated non-homogeneous Poisson process in the second case. Special attention is paid to the elicitation of the experts’ opinions. We conclude that the corrosion process behaves quite differently depending on the type of corrosion, and that, in most cases, cathodically protected pipes should be installed. Full article
(This article belongs to the Special Issue Bayesianism)
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24 pages, 10669 KiB  
Article
Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations
by Muhammad Waqas and Mohsin Jamil
Electronics 2024, 13(16), 3235; https://doi.org/10.3390/electronics13163235 - 15 Aug 2024
Cited by 8 | Viewed by 9120
Abstract
A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having [...] Read more.
A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations. Full article
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20 pages, 9389 KiB  
Article
Research on Gas Drainage Pipeline Leakage Detection and Localization Based on the Pressure Gradient Method
by Huijie Zhang, Maoliang Shen, Zhonggang Huo, Yibin Zhang, Longyong Shu and Yang Li
Processes 2024, 12(8), 1590; https://doi.org/10.3390/pr12081590 - 29 Jul 2024
Cited by 5 | Viewed by 1757
Abstract
Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. [...] Read more.
Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. Firstly, the basic law of gas flow in the drainage pipeline was analyzed, and a pipeline network resistance correction formula was deduced based on the pressure gradient method. Then, a drainage pipeline model was established based on the realizable k-ε turbulence model, and the pressure and flow velocity distribution during pipeline leakage under different leakage degrees, leakage locations, and pipeline negative pressures were simulated and analyzed, thus verifying the feasibility of the pipeline leakage detection and localization method. It is concluded that the positioning errors of pipeline leakage points under different leakage degrees, different leakage positions, and different pipeline negative pressures were 0.88~1.08%, 0.88~1.49%, and 0.68~0.88%, respectively. Finally, field tests were conducted in the highly located drainage roadway 8421 of the Fifth Mine of Yangquan Coal Industry Group to verify the accuracy of the proposed pipeline leakage detection and localization method, and the relative error was about 8.2%. The results show that with increased pipeline leakage hole diameters, elevated pipeline negative pressures, and closer leakage positions to the pipeline center, the relative localization error was smaller, the localization accuracy was higher, and the stability was greater. The research results could lay the foundation for the fault diagnosis and localization of coal mine gas drainage pipeline networks and provide technical support for safe and efficient coal mine gas drainage. Full article
(This article belongs to the Special Issue Intelligent Safety Monitoring and Prevention Process in Coal Mines)
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19 pages, 11239 KiB  
Article
Assessing Hydrogen Embrittlement in Pipeline Steels for Natural Gas-Hydrogen Blends: Implications for Existing Infrastructure
by Hesamedin Ghadiani, Zoheir Farhat, Tahrim Alam and Md. Aminul Islam
Solids 2024, 5(3), 375-393; https://doi.org/10.3390/solids5030025 - 16 Jul 2024
Cited by 10 | Viewed by 4551
Abstract
Governments worldwide are actively committed to achieving their carbon emission reduction targets, and one avenue under exploration is harnessing the potential of hydrogen. Blending hydrogen with natural gas is emerging as a promising strategy to reduce carbon emissions, as it burns cleanly without [...] Read more.
Governments worldwide are actively committed to achieving their carbon emission reduction targets, and one avenue under exploration is harnessing the potential of hydrogen. Blending hydrogen with natural gas is emerging as a promising strategy to reduce carbon emissions, as it burns cleanly without emitting carbon dioxide. This blending could significantly contribute to emissions reduction in both residential and commercial settings. However, a critical challenge associated with this approach is the potential for Hydrogen Embrittlement (HE), a phenomenon wherein the mechanical properties of pipe steels degrade due to the infiltration of hydrogen atoms into the metal lattice structure. This can result in sudden and sever failures when the steel is subjected to mechanical stress. To effectively implement hydrogen-natural gas blending, it is imperative to gain a comprehensive understanding of how hydrogen affects the integrity of pipe steel. This necessitates the development of robust experimental methodologies capable of monitoring the presence and impact of hydrogen within the microstructures of steel. Key techniques employed for this assessment include microscopic observation, hydrogen permeation tests, and tensile and fatigue testing. In this study, samples from two distinct types of pipeline steels used in the natural gas distribution network underwent rigorous examination. The findings from this research indicate that charged samples exhibit a discernible decline in fatigue and tensile properties. This deterioration is attributed to embrittlement and reduced ductility stemming from the infiltration of hydrogen into the steel matrix. The extent of degradation in fatigue properties is correlated not only to the hydrogen content but also to the hydrogen permeability and diffusion rate influenced by steel’s microstructural features, with higher charging current densities indicating a more significant presence of hydrogen in the natural gas pipeline blend. Full article
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32 pages, 8446 KiB  
Article
Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland
by Aleksei Vakhnin, Ivan Ryzhikov, Christina Brester, Harri Niska and Mikko Kolehmainen
Energies 2024, 17(12), 2840; https://doi.org/10.3390/en17122840 - 9 Jun 2024
Cited by 2 | Viewed by 1550
Abstract
Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak [...] Read more.
Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Efficient Buildings)
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23 pages, 4332 KiB  
Article
Transitioning to a Hydrogen Economy: Exploring the Viability of Adapting Natural Gas Pipelines for Hydrogen Transport through a Case Study on Compression vs. Looping
by Abubakar Jibrin Abbas, Salisu Kwalami Haruna, Martin Burby, Idoko Job John and Kabir Hassan Yar’Adua
Gases 2024, 4(2), 74-96; https://doi.org/10.3390/gases4020005 - 30 Apr 2024
Viewed by 2682
Abstract
The growing importance of hydrogen as an energy carrier in a future decarbonised energy system has led to a surge in its production plans. However, the development of infrastructure for hydrogen delivery, particularly in the hard-to-abate sectors, remains a significant challenge. While constructing [...] Read more.
The growing importance of hydrogen as an energy carrier in a future decarbonised energy system has led to a surge in its production plans. However, the development of infrastructure for hydrogen delivery, particularly in the hard-to-abate sectors, remains a significant challenge. While constructing new pipelines entails substantial investment, repurposing existing pipelines offers a cost-effective approach to jump-starting hydrogen networks. Many European countries and, more recently, other regions are exploring the possibility of utilising their current pipeline infrastructure for hydrogen transport. Despite the recent efforts to enhance the understanding of pipeline compatibility and integrity for hydrogen transportation, including issues such as embrittlement, blend ratios, safety concerns, compressor optimisation, and corrosion in distribution networks, there has been limited or no focus on pipeline expansion options to address the low-energy density of hydrogen blends and associated costs. This study, therefore, aims to explore expansion options for existing natural gas high-pressure pipelines through additional compression or looping. It seeks to analyse the corresponding cost implications to achieve an affordable and sustainable hydrogen economy by investigating the utilisation of existing natural gas pipeline infrastructure for hydrogen transportation as a cost-saving measure. It explores two expansion strategies, namely pipeline looping (also known as pipeline reinforcement) and compression, for repurposing a segment of a 342 km × 36 inch existing pipeline, from the Escravos–Lagos gas pipeline system (ELPS) in Nigeria, for hydrogen transport. Employing the Promax® process simulator tool, the study assesses compliance with the API RP 14E and ASME B31.12 standards for hydrogen and hydrogen–methane blends. Both expansion strategies demonstrate acceptable velocity and pressure drop characteristics for hydrogen blends of up to 40%. Additionally, the increase in hydrogen content leads to heightened compression power requirements until approximately 80% hydrogen in the blends for compression and a corresponding extension in looping length until around 80% hydrogen in the blend for looping. Moreover, the compression option is more economically viable for all investigated proportions of hydrogen blends for the PS1–PS5 segment of the Escravos–Lagos gas pipeline case study. The percentage price differentials between the two expansion strategies reach as high as 495% for a 20% hydrogen proportion in the blend. This study offers valuable insights into the technical and economic implications of repurposing existing natural gas infrastructure for hydrogen transportation. Full article
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25 pages, 19930 KiB  
Article
Design Improvement Using Topology Optimization for the Structural Frame Design of a 40 Ft LNG ISO Container Tank
by Tuswan Tuswan, Muhammad Andrian, Wilma Amiruddin, Teguh Muttaqie, Dian Purnama Sari, Ahmad Bisri, Yuniati Yuniati, Meitha Soetarjo, Muhammad Ridwan Utina and Rudias Harmadi
Designs 2024, 8(2), 21; https://doi.org/10.3390/designs8020021 - 21 Feb 2024
Cited by 2 | Viewed by 3299
Abstract
LNG ISO tank containers are a solution for bulk liquefied natural gas (LNG) delivery to the outer islands of Indonesia that are not connected to the gas pipeline network. The design of an ISO tank frame must consider two critical parameters, strength/rigidity and [...] Read more.
LNG ISO tank containers are a solution for bulk liquefied natural gas (LNG) delivery to the outer islands of Indonesia that are not connected to the gas pipeline network. The design of an ISO tank frame must consider two critical parameters, strength/rigidity and weight saving, which affect the operational performance of the distribution process. The current investigation aims to numerically optimize the design of the structural frame of a 40 ft LNG ISO tank for a mini LNG carrier operation using a topology optimization framework. Two design solutions are used in the topology optimization framework: reducing the strain energy and mass retained. Mass retained was selected as the objective function to be minimized, which was assumed to be 60–80%. The proposed frame design is tested using three operational loading scenarios, including racking, lifting, and stacking tests based on the ISO 1496 standard. The convergence mesh tests were initially evaluated to obtain the appropriate mesh density in the finite element analysis (FEA). The simulation findings show that the topology optimization method of the frame design resulted in an improved design, with an increase in the strength-to-weight saving ratio. A promising result from the optimization scenario demonstrates weight savings of about 18.4–37.3%, with experienced stress below the limit criteria. It is found that decreasing mass retained causes a significant stress increase in the structural frame and ISO corner castings, especially in the stacking load. The critical recommendation in the frame design of the LNG ISO tank can be improved by eliminating the saddle support and bottom frame and increasing the thickness of the vertical frame. Full article
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22 pages, 5502 KiB  
Article
Optimization Design of the Elbow Inlet Channel of a Pipeline Pump Based on the SCSO-BP Neural Network
by Libin Zhang, Yin Luo, Zhenhua Shen, Daoxing Ye and Zihan Li
Water 2024, 16(1), 74; https://doi.org/10.3390/w16010074 - 24 Dec 2023
Cited by 7 | Viewed by 1662
Abstract
A vertical pipeline pump is a type of single-stage, single-suction centrifugal pump with a curved elbow input. The inhomogeneous flow of the impeller inlet coexists with the unique elbow inlet channel, making it simple to generate the inlet vortical secondary flow. This paper [...] Read more.
A vertical pipeline pump is a type of single-stage, single-suction centrifugal pump with a curved elbow input. The inhomogeneous flow of the impeller inlet coexists with the unique elbow inlet channel, making it simple to generate the inlet vortical secondary flow. This paper aimed to optimize elbow inlet channel performance using a backpropagation (BP) neural network enhanced by the Sand Cat Swarm algorithm. The elbow flow channel’s midline and cross section shapes were fitted with a spline curve, and the parametric model of the curve was then constructed. Nine initial variables were filtered down to four optimization variables using the partial factor two-level (P2) and Plackett-Burman (P-B) experimental designs and multivariate analysis of variance. The sample space was generated by 50 groups of experiment samples, and the Sand Cat Swarm algorithm to optimize the BP (SCSO-BP) neural network and the approximation model of four variables were built. A genetic algorithm (GA) was applied to determine the optimal parameters among the approximate models in the sample space, and the ideal parameter combination of the elbow inlet channel was achieved. The findings demonstrated a strong agreement between the experimental and numerical simulation results. With reduced error fluctuation in inaccuracy and a more consistent fluctuation range, the approximate prediction model based on the optimized Sand Cat Swarm algorithm performed better. The optimized inlet model minimized the impact loss on the inlet wall, improved the velocity distribution uniformity of the inlet impeller, increased the pump efficiency by about 5% and the head by about 7.48% near the design flow, and broadened the efficient region of the pump. Full article
(This article belongs to the Special Issue Design and Optimization of Fluid Machinery)
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