bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Modeling for Rupture Mitigation Valve
Authors: Woll Alex , Liu Guanlan , Zhang Zhuoran 
Published: December 2024

Abstract
Per PHMSAs recent additions to 192.935 (c), if an operator determines that a Rupture Mitigation Valve (RMV) would be an efficient means of adding protection to their High Consequence Areas (HCAs) then they must install them. In order to determine if an RMV would be an efficient means of adding HCA protection, a risk analysis approach must be devised which considers the impacts of installing additional RMVs. RMV installation can improve both failure response time and valve spacing, both of which have an impact on the total time that a rupture with ignition can be sustained.
PHMSAs definition of Potential Impact Radius (PIR) in 192.903 is associated with a sustained heat flux for a thirty second timeframe which results in a one percent likelihood of mortality. Because the PHMSA defined PIR has a fixed timeframe of concern and an RMV installation can impact the time that a rupture with ignition is maintained, in order to assess the impacts of an RMV, we cannot rely upon PHMSA PIR definition for this analysis. Instead, a correlation between heat flux over time (heat dosage) and distance with the likelihood of mortality and building ignition need be established to assess the impacts of RMV installation on reducing consequences associated with a sustained rupture with ignition. In developing that correlation, an operator can then evaluate if the installation of an RMV would be an efficient means of protecting their HCAs.
An RMV risk modeling tool was developed to help operators minimize the consequences of a pipeline incident surrounding their HCAs. This presentation will cover the flow modeling approach, heat flux calculations, approximation of the transient state, and consequence modeling included in our RMV approach. This model supports operators in selecting optimal RMV spacing and locations surrounding their HCAs to most effectively protect public safety.
HyTap-In-Service Welding: Tapping and Stoppling of a 66 Bar (950 Psi) Hydrogen Line
Authors: Huising Otto Jan , Stordeur David , Vrolyk Chris 
Published: December 2024

Abstract
HyTap is a collaborative project undertaken by N.V. Nederlandse Gasunie and T.D. Williamson aimed at establishing safe working methods for in-service welding, hot tapping, and stoppling on hydrogen lines. The project involved a comprehensive analysis of materials and work methods to assess the implications of transitioning from natural gas to hydrogen. Based on these analyses, adapted methods were employed to conduct a full-scale experiment on a DN 500 (20″) 7.8 mm (0.307″) pipe. Throughout the operations, the pipe was pressurized with hydrogen at 99.5% purity and pressures ranging from 60 to 66 bar (870 to 957 psi). Equipment compatibility with hydrogen was carefully evaluated, leading to the use of sour service plugging heads for the isolation operation, while all other equipment remained consistent with natural gas conditions. Welding procedures were adjusted to limit the inside wall temperature to 700°C (1292°F). Of paramount importance was the inerting procedure, ensuring the absence of any explosive atmosphere at any stage of the operation, which proved to have the most significant impact. The HyTap experiment, conducted with these adaptations, successfully demonstrated that, with modifications to existing natural gas procedures, hydrogen lines can be welded, hot tapped, and isolated while in service.
Usage of Isotropic Hardening Model Enhanced by Power Law in Simulation of Pipe Material
Authors: Behseta Khosrow , Agbo Sylvester , Adeeb Samer 
Published: December 2024

Abstract
Analysis of in-service pressure vessels, pressurized components, and buried pipelines made from carbon steels with high tensile properties and a narrow range between their ultimate stress and yield stress are subject to crack-like flaws. Analysis of these structures requires consideration of various mechanical loads and environmental factors such as temperature, internal pressure, soil pressure, and ground movement. These structures often are subject to design features that can generate or enlarge stress concentrations or deformation over time, potentially resulting in failure. Current standards and assessment procedures offer methodologies for evaluating structural integrity and addressing flaws. The finite element method (FEM) is a powerful tool used for analyzing these structures, but its capability to predict fracture onset, especially in ductile and brittle materials, remains limited. To overcome these limitations, the extended finite element method (XFEM) has been developed, allowing for more accurate modelling of crack propagation and interaction with the surrounding material. However, widespread adoption is hindered by challenges in obtaining experimentally verified material properties tailored for XFEM analysis. This study aims to enhance traditional plasticity models and employ XFEM coupled with the cohesive zone model (CZM) to accurately simulate fracture criteria, providing a promising approach for evaluating flaw behaviour in pipeline steel specimens with different geometric configurations.
Force-displacement data were obtained from previous experimental and numerical investigations of pipeline materials using X65 material with round and flat specimens. We integrated various specimen geometries into the Abaqus program with dimensions identical to the published data. Symmetry was utilized whenever appropriate to reduce computational time.
Our results indicate that the isotropic hardening model can replicate the force-displacement behaviour of a variety of specimens with various groove radii, up to a particular strain limit, which is dependent on the specimen geometry and stress state. Beyond this limit, the simulations deviate from experimental observations. However, by enhancing the material data through the power law, a higher limit can be obtained. Furthermore, we demonstrate that through the established plasticity platform and by using XFEM-CZM, the necking section can be modelled correctly without the need for damage mechanics models, providing a verification base for the XFEM-CZM methodology.
Determination of Safe Excavation Pressure During Hoe Ramming Activities
Authors: Saunders Joe , Jobin Mathew , SanJuan Riverol Elvis , Chune Ron , Zhang Ken , Milligan Ryan 
Published: December 2024

Abstract
Hoe-ramming is a form of rock excavation, often used when an excavation area is too large for a jackhammer, or where blasting is not possible. A percussion hammer fitted to an excavator is used to break up and remove rock from the area surrounding a pipeline. During integrity excavations, hoe ramming is typically used to remove rock around in-service natural gas pipelines to allow for access to complete integrity assessment activities. To determine safe operating pressure of the pipeline, currently, a conservative approach is used that usually yields significant pressure reduction that may be unnecessarily required. Reductions in operating pressure to achieve safe excavation pressure can have impacts to typical pipeline operation. Opportunity exists to gain a greater understanding of the loading on the pipeline to allow for a more accurate safe excavation pressure recommendation.
In this study, a full-scale testing and dynamic finite element analysis (FEA) were used to quantify the effects of hoe ramming activities to natural gas pipelines. Testing was completed in December 2022 at the Pipeline Research Council International’s (PRCI) Technology Development Center in Houston, Texas. The test included NPS 24 × 6.35mm NWT, Grade 359 test pipe. Some of the variables controlled during the testing were proximity of hoe ramming to the test pipe, internal pressure (hydro) of test pipe, and configuration of the granite block and test pipe. Two configurations of granite blocks were subject to hoe ramming while in contact with the pipe were tested: including, I) Test pipe on top of two granite blocks, and ii) test pipe on side of one granite block. Accelerometers and strain gauges were instrumented on both granite blocks and the test pipe. Temperature, test pipe acceleration, test pipe strain, and internal pressure data were collected while the granite blocks were subject to hoe ramming. The data was processed and analyzed to quantify the effects to the test pipe during hoe ramming effects on the pipe.
When reviewing stress levels because of hoe ramming, both testing and the dynamic FEA supported that there was no detrimental effect to the test pipe. Further dependencies such as proximity of hoe ramming to the pipeline and internal pressure were also investigated. The test results were used to validate the FEA model of the tested configurations, which can subsequently expand to parametrical models to cover additional scenarios and understand the influence of critical variables. Outcomes of this study include development a pressure-equivalent method to determine safe operating pressure during hoe ramming activities. Details of the testing and assessment are summarized in this paper.
Methods Selection for Natural Gas Pipeline Patrolling Operation Using Technical and Economic Decision
Authors: Tansiriyakul Chavalit , Yanpirat Patcharaporn , Supapa Sansanee , Yindee Chatri 
Published: December 2024

Abstract
Natural gas is a source of clean energy and is the main fuel used for generating electricity in Thailand. Natural gas is transported from production to end users through pipeline systems. Therefore, pipeline patrolling and inspection is extremely important for maintaining pipeline integrity. This paper describes a case study incorporating a proposed framework for making decisions concerning selection of patrolling methods in terms of technical and capital investment criteria. For technical context, multiple criteria decision analysis (combining Voting Analytic Hierarchy Process (VAHP) and Fuzzy Analytic Hierarchy Process (FAHP)) is implemented to determine importance weights of selected expert and performance criteria respectively. For ranking patrolling methods, Fuzzy Technique of Order Preference Similarity to the Ideal Solution (FTOPSIS) is employed. In the context of capital investment evaluation of the patrolling methods, the equivalence uniform annual worth is used for ranking. A sensitivity analysis is also conducted by varying the importance weights of the technical performance criteria. Regarding technical and capital investment contexts, the results reveal that increasing patrolling frequency through outsourcing is the most suitable method for performing natural gas pipeline patrolling.
Best Practices in Geohazard Data Management
Authors: Theriault Bailey , Johnson Clayton , Brooks Justin , Taylor Justin , Mckenzie-Johnson Alex 
Published: December 2024

Abstract
In 2023, the Interstate Natural Gas Association of America Foundation (INGAA) sponsored a Joint Industry Project to develop and publish two documents: 1) Framework for Geohazard Management and 2) Recommended Practice for Pipeline Integrity Management of Landslide Hazards. Both papers identify data management as a key component to a successful pipeline Geohazards Management Program (GMP). This paper expands upon the INGAA publications to provide a more comprehensive overview of geohazard data management best practices, examples of the importance of data management in a GMP, and additional guidance on key components and methods.
The importance of data management in the success of a GMP can often be overlooked or undervalued. Data are central to the outcomes of a GMP in that they provide the basis for nearly all decision-making. Data help Operators determine where threats are located, the nature of the threats, the types of responses that are warranted, and whether additional data are needed. Unnecessary risks and uncertainty develop where data are not properly or consistently compiled or filed, resulting in potential for misunderstanding and mismanagement of threats, over- or under-responding to threats, or costly or unnecessary work.
Data pertaining to geohazard management are generated and collected throughout the life cycle of a pipeline, from a variety of sources and in a variety of formats, which can present challenges to development and maintenance of comprehensive repositories for relevant data. However, it is imperative that critical data are not only maintained, but also organized such that they can be rapidly accessed and analyzed when needed, for either emergent local concerns or for a system-wide review and assessment.
A variety of approaches and platforms are available and used by Operators to manage data. Platform and technology choices are typically driven by available resources such as internal and external hardware, software, and personnel. There are numerous configurations that can accomplish critical key objectives, including: publicly available software to proprietary solutions; local to cloud-based data storage; and small to large internal and/or external teams of data managers, data generators, and data users. This paper does not seek to provide a specific roadmap to data management. Instead, it seeks to layout the core components of data management that are essential for a GMP and to define the objectives of each.
An example of an ideal baseline data management system will be presented, based on real-world Operator systems.
Knowledge-Informed Semi-Supervised Robust Modeling for Mixed Oil Length Prediction in Multi-Product Pipelines Based on Student’s-T Mixture Model
Authors: Yuan Ziyun , Chen Lei , Liu Gang , Li Zukui , Zhang Yuhan 
Published: December 2024

Abstract
Multi-product pipelines usually transport various petroleum products in certain batch, leading to inevitable mixed oil segment. Current soft sensor modeling methods for predicting mixed oil length face challenges characterized by nonlinearity, scarcity of labeled samples and interference from outliers. We propose a semi-supervised robust soft sensor called the KISSVBSMR. It employs the Semi-supervised Variational Bayesian Student’s-t mixture regression to empower the usage of unlabeled data, and it strategically selects the key variable based on physical significance to enhance mode identification. Additionally, by transforming the relationship between input and output variables derived from theoretical equation and incorporating a Bayesian approach, the KISSVBSMR can explore the prior knowledge regarding the regression coefficient and efficiently against outliers. Evaluation through numerical and real-life industrial datasets reveals the shortcomings of traditional methods and the superiority of KISSVBSMR in model performance. This novel approach improves the predictive accuracy of mixed oil lengths, optimizing multi-product pipeline operations and advancing knowledge-data hybrid modeling techniques.
A Neural Network-Enhanced Finite Difference Approach for Strain Demand Prediction of Inelastic Pipes Subjected to Ground Displacement
Authors: Ji Beilei , Zheng Qian , Mei Qipei , Yoosef-Ghodsi Nader , Adeeb Samer 
Published: December 2024

Abstract
Permanent ground displacement induced by geohazards poses a significant threat to the integrity of pipelines due to the potential for excessive strain. Accurately predicting strain demand is critical for guiding the design of new pipelines and assessing the risks associated with existing ones crossing geohazard zones. Previously, a finite difference approach for strain demand prediction in pipes subjected to permanent ground displacement was developed by the authors. It has been proven to be a simple and valuable technique for practical use in the pipeline industry, compared with conservative empirical formulas and time-consuming finite element modeling. However, the existing method relies on explicit expressions for axial force and bending moment, derived under the assumption of a bilinear stress-strain curve for the material, which restricts its applicability when dealing with more complex constitutive models that require numerical integration. To remedy this situation, a novel approach that models constitutive law using deep neural networks is proposed, serving as an alternative means for capturing stress-strain relationship. This novel approach is integrated into the finite difference scheme to overcome the constraints of the original method, thereby enhancing its applicability. A comprehensive case study was conducted to evaluate the effectiveness of the proposed neural network-enhanced finite difference approach in comparison to the original method. Results from this study demonstrated that the proposed method can achieve comparable accuracy to the original finite difference method when dealing with small ground displacements. The finding indicates the potential advantages of the proposed method in efficiently handling more complex constitutive relations, which will be explored in future work.
Construction Pipe Settlement: Causes and Mitigations
Authors: Phillips Ryan , Martens Michael , Thompson Shawn , Barrett John 
Published: December 2024

Abstract
With pipelines, as with any new construction some settlement of the foundation is expected. An allowance is made during design for pipe settlement associated with trenching and backfilling construction activities. However, additional unexplained settlements can occur and become problematic, potentially overstressing a pipeline. These issues are exaggerated at pipeline facilities. An evaluation of current construction guidelines and practices along with additional case study and literature reviews was utilized to determine potential factors contributing to this settlement. Several factors were identified, revolving primarily around trench movements and backfilling quality. Various factors can contribute to trench movements, including elastic and consolidation movements of the surrounding soil due to overburden removal, trench wall movements due to frost heave or water entry while the trench is open. The pattern and magnitude of these movements will depend on soil types, trench support and trench geometry. Winter construction can create frost heaving in both the trench base and walls, followed by settlements during thawing. While consolidation of trench soils due to the increased loading from the new pipeline is considered, the combined result of this swelling is often not accounted for in design. Beyond this, the backfill and its handling can alter the settlement response during and immediately post construction. Controlled granular backfills, compacted with near optimum water contents may provide the highest level of predictability post construction, but will still settle under water wetting. Controlling backfill quality, material, water contents and compaction assurance, and trench swelling prior to pipeline construction, may provide the best path to controlled, predictable and quantifiable pipeline construction settlements. More economic pipeline designs will result if pipeline movements due to external effects can be reduced.
Assessing Likelihood of Failure for Unpigged Pipelines Using Supervised Machine Learning
Authors: Xiang Wei , Tayyab Hassan , Lu Dongliang , Dooley Colin , Lucut Sergiu 
Published: December 2024

Abstract
System-wide risk assessment is an integral process for quantitatively evaluating the risk levels of pipeline segments. This involves utilizing the best available information, including inline inspection (ILI) data, survey data, pipeline attributes, historical mitigation information and environmental properties pertinent to the integrity threats. The assessed risk provides the basis for planning mitigation activities, and ensuring that the risk level is maintained at an acceptable level. Estimation of likelihood of failure (LOF) is a crucial component of the risk assessment, which is subsequently combined with the consequence of that failure to assess risk. In the absence of ILI data, the LOF for unpigged pipelines were often estimated using conventional statistical models. These models delineate the relationship between historical failure rates and the factors influencing the occurrence or prevention of integrity threats. While such traditional regression models offer the advantage of interpretability, they are constrained in capturing the intricate nonlinear relationship between the LOF and pertinent variables. Additionally, they lack the flexibility to characterize the complex interactions among multiple predictor variables.
To improve the accuracy of LOF assessment for unpigged pipeline segments exposed to external corrosion, this paper introduces a multi-layer model that was developed using supervised machine learning techniques and structural reliability method. The model consists of three layers. In the initial layer, a series of machine learning models are employed to predict key statistics that characterize the corrosion severity of the pipeline segments. These machine learning models were trained and validated using pigged pipeline segments, with ILI data considered as the ground truth for the corrosion severity. The predictor variables utilized by the model include pipeline attributes, factors contributing to corrosion, factors resisting corrosion, and survey data (e.g., DCVG). In the second layer, the previously predicted corrosion states serve as the input for probabilistic corrosion assessment models, facilitating the evaluation of the probability of leak (POL) and probability of burst failure (POF) through Monte Carlo simulation. In the final layer, the assessed POL and POF are aggregated into the LOF.
This paper describes the framework of the multi-layer model, outlines the data requirement for model development, and illustrates the process of model calibration and validation. Furthermore, practical considerations in the model development are discussed, ensuring the general alignment between model predictions and engineering judgements. The enhanced LOF accuracy will enable the operators to make more cost-effective mitigation decisions for unpigged pipelines based on the results of risk assessments.
Opportunities and Challenges in Repurposing Canadian Natural Gas Transmission Pipelines for Hydrogen Service – A Regulatory, Standards, and Technical Exploration
Authors: Dinata Randy , Finneran Shane 
Published: December 2024

Abstract
In decarbonizing Canada’s energy systems, utilizing hydrogen as a low-carbon fuel is growing in interest, and repurposing in-service natural gas transmission pipelines to transport large volumes of hydrogen can be a key solution.
Existing intraprovincial natural gas transmission pipelines, such as those in Alberta, can be potential candidates for conversion into hydrogen service in Canada due to factors like steel grades used, pipe hoop stresses, and year of construction. However, all pipelines intended for conversion into hydrogen service must undergo an engineering assessment as outlined in the CSA Z662 to ensure the integrity of the pipeline system and safe operations. While challenges for such conversion exist, discussed in this study, such as high pipe hoop stresses in long-distance interprovincial natural gas transmission pipelines, high specified minimum yield strength, and unavailability of required pipe data, when mitigated, the engineering assessment may determine the pipelines are fit-for-service for hydrogen. This applies not only to line pipe but also to other existing components within the pipeline system that will be exposed to hydrogen, such as gas compressors, meters, fittings, valves, and flanges.