Pipeline System Research Articles

A two-level MPC method for the operation of a gas pipeline system under demand variation

A gas pipeline system is in an almost continual state of transition due to changing gas demand. A control method is required to keep a gas pipeline operating in an energy-saving status and provide reliable transmission service for the customers. Model predictive control (MPC) predicts the future states of a system, making it suitable for following gas demand changes. However, to avoid computation time challenges associated with formulating the MPC problem as a large-scale MINLP, an online two-level MPC system is proposed, comprising an upper-level economic MPC (EMPC) and a lower-level tracking MPC (TMPC). EMPC adopts energy cost as the objective function and utilizes a simplified nonlinear model to address long-horizon optimization, while TMPC tracks the status optimized in EMPC, to find the optimal output pressure and the on-off status of the compressors and compressor stations. The performance of the method is demonstrated through application to case studies.

Internet of Things Based Oil Pipeline Spill Detection System Using Deep Learning and LAB Colour Algorithm

Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.

Ultimate strength of hybrid repaired offshore pipelines with various slenderness ratio subjected to through-thickness pitting corrosion under axial compressive loading

Offshore pipelines are susceptible to various types of corrosions where pitting corrosion is generally considered the most severe. This study aims to study the effect of ratio of Carbon Fiber Reinforced Polymer to Glass Fiber Reinforced Polymer and wrapping patterns on the repair performance of composite repair system for offshore pipelines subjected to pitting corrosion. Four sets of samples with different slenderness ratios are subjected to axial compression, where the peak loads are recorded as the performance indicator of different repair schemes. Based on the experimental results, the average repair index, which is the ratio of repaired strength to intact strength, increases from 1.06 to 1.40 as the ratio of CFRP to GFRP increases. However, 1G3C repair schemes experience brittle failure while other repair schemes show different extents of ductility upon failure. Longitudinal wrapping pattern achieves the average repair index of 1.19 while Alternate and Horizontal yields the average repair index of 1.18 and 1.15 respectively. Furthermore, Longitudinal wrapping pattern exhibits the highest rigidity followed by Alternate and Horizontal. A higher slenderness ratio can contribute to a lower repair index where the cross-sectional properties have a more prominent effect compared to sample length. The theoretical results calculated based on equations provided in ASME PCC-2 show good agreement with the experimental results as there is only a percentage difference of 5.5% between them.

Influence of hydrogen blending on the operation of natural gas pipeline network considering the compressor power optimization

Blending hydrogen into the existing natural gas pipeline network is regarded as the most potential transportation mode on the utility-scale. However, the impact of hydrogen on the operation performance of the pipeline remained unclear. This paper analyzes the economic and environmental performance of natural gas pipeline under different hydrogen blending ratios and different hydraulic boundary conditions. In order to reduce the operation costs and carbon emissions in each condition, this paper establish a non-linear optimization model to get the new operation plan of the pipeline system. Further, in order to characterize different boundary conditions of the system, four scenarios, namely, the baseline scenario (BS), the setpoint of flow rate scenario (SF), the setpoint of pressure (SP), and the setpoint of heat flow rate (SH) are proposed in this paper. Two studied cases demonstrated that: (1) the original compressor for natural gas can adapt to a hydrogen blending without change the type of compressor. (2) The optimization model has significant potential in reducing the economic cost and carbon emissions of the system with an average decrease of 11.48%. (3) For every 1% of hydrogen added, the annual operating cost of the system is reduced by $0.73 million (3.29%) in SF, 0.017 million (0.08%) in SP, or increased by $1.67 million (7.50%) in SH. Moreover, the annual carbon emission is reduced by 0.38 kt (3.53%) in SF, 0.047 kt (0.44%) in SP, or increased by 0.76 kt (7.14%) in SH. However, when the hydrogen mixing ratio is more than 8%, the contrary trend may occur. (4) The maximum blending ratio at different points of the multi-source pipeline network are physically interdependent. The paper proposed an optimization model for the 2-E analysis of the pipeline and can provide theoretical guidance for the further application of this transportation mode.

Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven modeling techniques have been widely utilized. To accomplish intelligent corrosion prediction and enhance corrosion control, machine learning (ML)-based approaches have been developed. Some published papers related to SCC have discussed ML techniques and their applications, but none of the works has shown the real ability of ML to detect or predict SCC in energy pipelines, though fewer researchers have tested their models to prove them under controlled environments in laboratories, which is completely different from real work environments in the field. Looking at the current research status, the authors believe that there is a need to explore the best technologies and modeling approaches and to identify clear gaps; a critical review is, therefore, required. The objective of this study is to assess the current status of machine learning’s applications in SCC detection, identify current research gaps, and indicate future directions from a scientific research and application point of view. This review will highlight the limitations and challenges of employing machine learning for SCC prediction and also discuss the importance of incorporating domain knowledge and expert inputs to enhance the accuracy and reliability of predictions. Finally, a framework is proposed to demonstrate the process of the application of ML to condition assessments of energy pipelines.

Transient-based multiple branch detection in reservoir pipeline valve systems

Efficient management of pipeline assets is crucial for a reliable urban water supply. Unknown side branches in pipeline systems can hinder the prediction of hydraulic transients, owing to unexpected pressure responses. Herein, we developed a method for detecting multiple side branches in a simple reservoir pipeline valve system. A polynomial-based transient characterization scheme was proposed to address nonlinear valve manoeuvres, which substantially improved the transient simulations over conventional orifice-based approximations. A procedure for pressure signal manipulation via the superimposition of pressure signals with and without branches and isolation of the interference between various branched elements, which distorted the pressure response of multiple branches, was introduced. The model performance was evaluated by comparing the interference-isolated signals obtained from the conventional method, proposed scheme, and experimental datasets. Effective phase matching of the pressure responses obtained via the proposed scheme and experimental data indicated the potential of the former for detecting multiple branches.

Simplified approach for sizing surge tank in long-distance pipeline systems

The cross-sectional area of a surge tank (CAST) is a key factor for protecting pipelines from harm by hydraulic transients. However, for long-distance pipeline systems (LDPS), the water level fluctuations in the surge tank and valve closure water hammer normally share a coupling relationship. Traditionally, this coupling relationship renders difficulty in determining the CAST. This study focused on the influence of water level fluctuation and water hammer, and proposed a simplified approach for sizing surge tanks. First, a water hammer formula that considers the coupling effect was derived. Subsequently, formulae for sizing the CAST were deduced. Finally, a case study of an actual LDPS was conducted to verify the accuracy of the presented formulae. In addition, the calculation errors caused by the theoretical assumptions are discussed. The results indicate that the theoretical calculation values and time-domain simulation results match well, thereby demonstrating the great convenience of designing surge tanks.

CipherTrace: automatic detection of ciphers from execution traces to neutralize ransomware

Abstract In 2021, the largest US pipeline system for refined oil products suffered a 6-day shutdown due to a ransomware attack [1]. In 2023, the sensitive systems of the US Marshals Service were attacked by a ransomware [2]. One of the most effective ways to fight ransomware is to extract the secret keys. The challenge of detecting and identifying cryptographic primitives has been around for over a decade. Many tools have been proposed, but the vast majority of them use templates or signatures, and their support for different operating systems and processor architectures is rather limited; neither have there been enough tools capable of extracting the secret keys. In this paper, we present CipherTrace, a generic and automated system to detect and identify the class of cipher algorithms in binary programs, and additionally, locate and extract the secret keys and cryptographic states accessed by the cipher. We focus on product ciphers, and evaluate CipherTrace using four standard cipher algorithms, four different hashing algorithms, and five of the most recent and popular ransomware specimens. Our results show that CipherTrace is capable of fully dissecting Fixed S-Box block ciphers (e.g. AES and Serpent) and can extract the secret keys and other cryptographic artefacts, regardless of the operating system, implementation, or input- or key-size, and without using signatures or templates. We show a significant improvement in performance and functionality compared to the closely related works. CipherTrace helps in fighting ransomware, and aids analysts in their malware analysis and reverse engineering efforts.

Experimental study of the effect of coal dust on the flame dynamics of partially premixed gas-coal dust explosion in a vertical pressure relief pipe-line

To further study the gas-coal dust explosion hazards under complex conditions. Experiments were conducted in pipe-line systems containing explosive pipe-line and pressure relief pipe-line. The effect of coal dust concentration and particle size on the flame dynamics of gas-coal dust explosion was investigated. The flame structure in the explosion pipe-line is divided into two-stages: spherical and round-finger. In the pressure relief pipe-line, the flame structure e varies at working conditions. Flame front position increases with time, showing a class exponential growth trend. The flame front velocity increases in the explosion pipe-line, has a short drop during the entry into the pressure relief pipe-line, and then increases. With the increase of coal dust concentration, flame propagation time has been increasing in the pipe-line system and the explosion pipe-line, with the shortest time at 25 g/m?. Flame propagation time first increases and then decreases in the pressure relief pipe-line, the shortest time at 50 g/m?. The maximum flame front velocity first increases and then decreases. The maximum flame front velocity is maximized at 50 g/m?. With the increase of coal dust particle size, flame propagation time has been increasing in the pipe-line system, and in the explosion pipe-line, flame propagation times have been decreasing in the pressure relief pipe-line. The maximum flame front velocity first increases, then decrease, and increases again. The maximum flame front velocity is minimal at 45 ?m.

Chordal measurement of phase fraction distribution in a static gas-liquid system using collimated gamma-ray densitometer and artificial neural networks

Two-phase flow occurs in various industries, as in the production of oil and gas. A collimated gamma-ray densitometer is applied for the study of a static gas-liquid system that simulates a stratified flow pattern. It stands out for its non-intrusive measurement capacity, its high sensitivity to density variations and its good spatial resolution. Chordal phase fraction distributions are obtained in a tube containing water and air at room conditions, with the water level varied between 25%, 50% and 75%. The results obtained highlight the usefulness of the collimated gamma-ray densitometer to determine phase fraction distributions along the pipe’s cross section. Furthermore, this study suggests the use of an artificial neural network (ANN) model for predicting holdup in pipeline systems using a dataset of 110 experimental data points. The ANN model considers factors such as absorbed intensity, water cut percentage, and dimensionless h/D ratio. The adopted configuration includes the use of the Adam solver, Rectified Linear Unit (ReLU) activation function, a batch size of 3, two hidden layers (60 neurons each), and a learning rate of 0.001. The model achieves good accuracy, with a minimum mean square error (MSE) of 0.3% and a low mean absolute error (MAE) of 0.028.

Influence of elasticity of high-concentration paste on unsteady flow in pipeline transportation

In high-pressure pulsatile pipeline transport of high-concentration viscous paste, severe backflow is generated when there are sudden pressure drops due to the paste's elastic recovery, resulting in strong impact and damage to the pipeline system. The paste demonstrates high viscosity, yield stress, compressibility, and shear elasticity. Nevertheless, the effect of the paste's elastoviscoplasticity properties on unsteady flow during pipeline transport remains uncertain. Consequently, we derived the unsteady flow control equations of the paste that accounts for the elastoviscoplasticity behavior and linear compressibility. We conducted experimental research by building a circulating pipeline transportation system and performed numerical analysis using the finite-volume method (modified RheoFoam solver). The results demonstrate a substantial increase in pressure fluctuations within the pipeline during rapid pressure drops due to the elasticity of the paste. When accounting for elasticity, the maximum backflow velocity increases by 32.4% compared to cases where elasticity is not considered. The backflow velocity during piston pump reversal is influenced by the pressure level and the rate of pressure drop. Pressure fluctuations are more likely to propagate along the pipeline during high-pressure than during low-pressure periods. These findings offer guidance for analyzing unsteady flow characteristics in paste pipeline transport and designing pipeline systems.

A Simple Estimation Scheme for Leak Detection in Pipelines

This paper presents a simple high-gain observer to detect water leak in a pipeline system. The estimation scheme was developed based on the nonlinear model of the pipeline system. A simulation was conducted to illustrate the potential of the proposed method.

Research on standards and standard system of hydrogen pipeline in China

Pipeline hydrogen transport is one of the main technical paths to achieve large-scale storage and transportation of hydrogen energy, and standards are important technical support for pipeline hydrogen transport applications. China’s hydrogen pipeline construction started late, and a relatively complete hydrogen pipeline standard system has been established in the world, but China’s hydrogen pipeline standard system has not been established, the lack of key technical standards and other problems. This paper investigates the current situation of hydrogen transport pipeline standards and the applicability to China. A standard system is preliminarily constructed for hydrogen transport pipelines, including 6 sub-systems and 23 technical standard fields. Some suggestions are put forward for the development of China’s hydrogen pipeline standardization.

Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion

Abstract Aiming at the current challenges of enormous scale, complex structure, difficult control and frequent accidents of city gas high-pressure pipeline network, there are still three aspects of difficulties in the risk monitoring and control of China’s city gas high-pressure pipeline network, namely, rough data, shallow assessment, and lack of power. This paper proposes an intelligent management system for gas pipelines based on C/S model and J2EE enterprise-level framework, in which the failure warning models of gas leakage, Gaussian plume diffusion, and fire and explosion are established. And the Kalman filter algorithm improved by DS evidence theory is used for intelligent fusion of Multi-source data, analyzing and screening the unified adequate information on data types, extracting state characteristics, classifying warning levels, and developing an integrated and visualized pipeline remote diagnosis and warning platform. In the simulation of the intelligent management system of gas pipeline, when the wind speed is 1.5m/s in winter, the ground surface is a safe area within 12.15m of the gas pipeline. When the maximum wind speed is 10m/s, the upper limit distance of the gas leading to fire and explosion is only 2.43m, and the hazardous range of the gas pipeline jet fire is within 12.69m. Relying on the gas high-pressure pipeline network in L city for practical experiments and applications, it provides technical support and decision-making basis for the construction of intelligent pipeline network, comprehensively improves the risk control capability of city gas high-pressure pipeline network, and has reference significance for the risk control of national city gas high-pressure pipeline network.

Unravelling the FOG Deposit Sources Characteristic in the South Jakarta City Wastewater Network

DKI Jakarta is the largest wastewater contributor in Indonesia, where 75% of it comes from domestic activities. Typically, effluent will enter the river via a system of wastewater pipelines. Numerous wastewater networks have been observed to have FOG deposits (FD), particularly in South Jakarta. FD is an accumulation of oil and grease in sewerage systems that results in blockages and gas accumulation, leading to explosions in closed networks. Despite the impact being well known, there are currently very few studies that focus on Indonesia’s FD, particularly FD in Jakarta’s centralized wastewater network. This study aims to analyse the main sources and characteristics of wastewater that forms FD. Samples from two locations were used to compare the conditions of sewerage without FD and those with FD (A and B, respectively). Results showed that Padang restaurants and catering are the main sources of pollutants affecting FD formation. The FOG concentration in sample B was 2x107 times higher than in sample A, while the TSS concentration was 1.8x104 times higher. The dominant free fatty acid (FFA) in sample B was palmitic acid (51%), resulting in a dense precipitate structure and composition.

Effects of flow rate and wastewater concentration on the transformation of nitrogen in sediment-water system of sewage pipelines.

In this work, the transformation law of nitrogen in sediment-water system under different flow rates and wastewater concentrations were investigated in a simulated sewage pipeline system. Results showed that the different flow rates and wastewater concentrations in the pipeline caused differences in microbial community in sediments and nitrogen transformation. When the flow rate increased from 0.05 to 0.2 m/s, the scouring effect was enhanced, resulting in higher concentrations of NH4 + -N and NO3 - -N in the overlying water. At 0.2 m/s, the relative abundance of Clostridium_sensu_stricto_1 in sediments was higher, resulting in a greater conversion of amino acid nitrogen (AAN) to NH4 + -N. Meanwhile, many denitrifying bacteria (Trichococcus, Dechloromonas, norank_f__norank_o__Gaiellales, Thiobacillus) had high relative abundance in the sediments, and the denitrification process was common. When the wastewater concentration was high, the nitrification reaction was great in overlying and interstitial water. Moreover, the ammoniation process was great in the sediments, and the variation flux of AAN was large (remarkably reduced). PRACTITIONER POINTS: AAN transformed to NH4 + -N in sediment under different flow rate and concentration. Scouring was enhanced at 0.2 m/s, increasing nitrogen contents in overlying water. Difference in microbial community led to more AAN conversion to NH4 + -N at 0.2 m/s. The ammoniation process was greater in sediment at a high concentration of sewage. NH4 + -N migrated from overlying water to sediment at a high concentration of sewage.

Positive aspect in the presence of asphalt-smo-loparaffin deposits during oil products transfer through the pipeline network

Until now, the phenomenon of paraffin contamination of oil pipelines is considered an obstacle to the efficient transport of petroleum raw materials, leading to a decrease in the reliability of their operation. The positive effect of asphalt, resin and paraffin deposits during the movement of petroleum raw materials in the oil pipeline system can be assessed by taking into account a number of phenomena. So, first of all, asphalt-resin-paraffin deposits have a relatively low coefficient of thermal conductivity and smooth out irregularities from internal corrosion of the walls of oil pipelines, acting as its corrosion. These deposits, when polished during operation of the oil pipeline system, reduce the surface roughness of the walls, which causes a drop in hydraulic resistance and for this reason leads to an increase in the permeability of oil pipelines and, as a consequence, to a reduction in energy costs. The article provides an assessment of the advantages of having a controlled layer of designated sediments when transporting petroleum raw materials through the main oil pipeline network. One of these advantages is the formation of a controlled layer of deposits, as a corrosion inhibitor, on the surface of oil pipelines in contact with oil raw materials, as well as as their thermal insulation with a roughness-reducing effect.

Detection of Partial and Extended Blockages: A Case Study of Edible Oil Pipeline System

Detection of Partial and Extended Blockages: A Case Study of Edible Oil Pipeline System

Pipeline sharing: Boosting multi-product pipeline transport biofuels in the shift to low-carbon energy

Under the dual pressures of oil depletion and low carbon transition, biofuels are considered effective alternatives for fuel oil due to their renewable nature and environmental friendliness. However, oil demand remains high, which indicates that biofuels and fuel oil will coexist for a long time to come. While pipelines offer an efficient means of transporting liquid fuels, the initial investment required is substantial. Therefore, this paper proposes a comprehensive framework that focuses on sharing already existing multi-product pipelines with biofuels, thus evaluating the pipeline remaining potential to transport biofuels. The core component of this framework is the evaluation method for determining the multi-product pipeline remaining capacity. We coupled fuel oil consignment contracts into the remaining capacity evaluation method and developed a model for evaluating the multi-product pipeline transport biofuels potential, aiming to maximize biofuels pipeline transport volume. The objective of this model is to optimize the transportation volume of biofuels through pipelines. Various constraints are taken into account, such as product batch movement, prohibited sequences, operational constraints, delivery time frames, and inventory levels. Moreover, this study evaluates and discusses the benefits of biofuels transportation capacity using eight real-world multi-product pipeline systems without impacting fuel oil consignment contracts. The findings indicate that pipelines are capable of transporting 49,849 m3 of biofuels per week, increasing revenue by 1.78 million CNY. Simultaneously, the low energy consumption for pipeline transportation can lead to a weekly reduction of 34.6 tons in carbon emissions. The evaluation model anticipatively provides the ability to respond to future market developments.

Experimental investigation on corrosion resistance and tribological properties of steel coated with glass-reinforced polymer

This work presents a comprehensive electrochemical corrosion analysis conducted on steel coated with glass-reinforced polymer (GRP), low alloy steel and stainless steel, focusing on their performance in a simulated aggressive environment (3 % NaCl). The corrosion rates were established and the corrosion mechanisms governing each material's behaviour were elucidated. By comparing the electrochemical behaviour of these materials, we aim to evaluate the efficacy of GRP coatings in preventing corrosion compared to conventional metallic materials used for pipeline systems. Furthermore, in addition to the corrosion analysis, the coefficient of friction determination between the GRP-coated steel and 25CrMo4 (EN 10083-3) steel was conducted to assess their tribological compatibility. Understanding the frictional behaviour between these materials is essential for optimising pipeline performance, minimising wear and ensuring operational efficiency in dynamic environments encountered in the petroleum and gas industry. The study includes, also, an evaluation of cumulative linear wear to quantify the material loss. This comprehensive approach provides a complete understanding of the interplay between corrosion resistance and mechanical durability.