Recent Advances and Future Perspectives in Carbon Capture, Transportation, Utilization, and Storage (CCTUS) Technologies: A Comprehensive Review

Fuel, Год журнала: 2023, Номер 351, С. 128913 - 128913

Опубликована: Июнь 10, 2023

Язык: Английский

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Opportunities, challenges and the way ahead for carbon capture, utilization and sequestration (CCUS) by the hydrocarbon industry: Towards a sustainable future

Energy Reports, Год журнала: 2022, Номер 8, С. 15595 - 15616

Опубликована: Ноя. 1, 2022

While carbon capture and sequestration (CCS) has garnered many headlines in the recent past, its wider applicability on an industrial scale is yet to be explored due a plethora of challenges, primarily involving short-term financial attractiveness CCS projects. However, represents crucial technology for hydrocarbon industry extend utility this age where environmental, social governance (ESG) criteria applied by institutes restrict access capital industry. opportunities are varied require more detailed understanding various processes them successful their desired operational parameters. Alternatively, captured dioxide (CO2) may utilized enhanced oil recovery (EOR) or methane production, which referred as capture, utilization storage (CCUS). Hence, primary purpose review introduce research audience process CCUS providing brief introduction potential. The article discusses multiple technologies studies Technology Readiness Level (TRL) values each these order give better outlook about being employed contemporary world. Also, viability subsurface mobilization applications would adverse conditions. Finally, highlight implementation proposed methods transition towards CO2 gas been elaborated upon. Apart from reviewing literature related technologies, authors have upon economic aspects technologies. There also studied impact different industries like chemical industry, cement etc. thus, paper intended act guide policy makers future course action with regards reducing footprint oil, till equitable sources energy replace it.

Язык: Английский

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Ultrasonic guided wave techniques and applications in pipeline defect detection: A review

International Journal of Pressure Vessels and Piping, Год журнала: 2023, Номер 206, С. 105033 - 105033

Опубликована: Июль 22, 2023

Язык: Английский

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Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Sensors, Год журнала: 2023, Номер 23(6), С. 3226 - 3226

Опубликована: Март 17, 2023

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result severe consequences, such as wasted resources, risks to community health, distribution downtime, economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes machine learning-based platform for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, kurtosis, skewness, mean value, square, root square (RMS), peak standard deviation, entropy, frequency spectrum features, were extracted from signal features train learning models. adaptive threshold-based sliding window approach was used retain properties both bursts continuous-type emissions. First, we collected three datasets 11 time domain 14 one-second each data category. measurements their associated statistics transformed into feature vectors. Subsequently, these utilized training evaluating supervised models detect leaks. Several widely known classifiers, neural networks, decision trees, random forests, k-nearest neighbors, evaluated four regarding water leakages at different pressures pinhole sizes. We achieved an exceptional overall classification accuracy 99%, providing reliable effective results that are suitable implementation proposed platform.

Язык: Английский

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Real-time pipeline leak detection and localization using an attention-based LSTM approach

Process Safety and Environmental Protection, Год журнала: 2023, Номер 174, С. 460 - 472

Опубликована: Апрель 12, 2023

Язык: Английский

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Experimental study on leakage temperature field of hydrogen blending into natural gas buried pipeline

Applied Energy, Год журнала: 2024, Номер 359, С. 122656 - 122656

Опубликована: Янв. 18, 2024

Язык: Английский

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Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet

Sensors, Год журнала: 2025, Номер 25(4), С. 1112 - 1112

Опубликована: Фев. 12, 2025

Effective leak detection and size identification are essential for maintaining the operational safety, integrity, longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, excessive computational costs, which limits their feasibility real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline approach, integrating Empirical Wavelet Transform (EWT) adaptive frequency decomposition with customized one-dimensional DenseNet architecture achieve precise classification. The methodology begins EWT-based signal segmentation, isolates meaningful bands enhance leak-related feature extraction. To further improve quality, thresholding denoising techniques applied, filtering out low-amplitude while preserving critical diagnostic information. denoised signals processed using DenseNet-based deep learning model, combines convolutional layers densely connected propagation extract fine-grained temporal dependencies, ensuring accurate classification presence severity. Experimental validation was conducted on real-world AE data collected under controlled non-leak conditions at varying pressure levels. proposed model achieved an exceptional accuracy 99.76%, demonstrating its ability reliably differentiate between normal operation multiple severities. method effectively reduces costs robust performance across diverse operating environments.

Язык: Английский

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Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2023, Номер 17(1)

Опубликована: Янв. 17, 2023

This paper proposes a leak detection and size identification technique in fluid pipelines based on new leak-sensitive feature called the vulnerability index (VI) 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal operating conditions of pipeline. However, multiple sources hits, such as pressure joints, interference noises, flange vibrations, leaks pipeline, make less sensitive toward To address this issue, are first extracted from (AE) signal using sliding window with an adaptive threshold. Since distribution changes according to pipeline working conditions, newly developed multiscale Mann–Whitney test (MMU-Test) is applied obtain feature, which shows pipeline's susceptibility conditions. Finally, provided input 1-D-CNN for identification, whose experimental results show higher accuracy compared reference state-of-the-art methods under variable

Язык: Английский

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Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Expert Systems with Applications, Год журнала: 2023, Номер 231, С. 120542 - 120542

Опубликована: Май 31, 2023

Язык: Английский

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Experimental and numerical studies on hydrogen leakage and dispersion evolution characteristics in space with large aspect ratios

Journal of Cleaner Production, Год журнала: 2024, Номер 438, С. 140467 - 140467

Опубликована: Янв. 1, 2024

Язык: Английский

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