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Supercritical CO2 Pipeline Leakage Localization Detection Based on the Negative Pressure Wave Method and Cross-Correlation Analysis

負圧波法と相互相関解析に基づく超臨界CO2パイプライン漏洩位置検出 (AI 翻訳)

Bing Chen, Hong Feng, Chunli Tang, Wenjiao Qi, Hongliang Xiao, Xiangzeng Wang, Jian Bi, A. Oloruntoba

Processes📚 査読済 / ジャーナル2026-02-03#CCUSOrigin: CN
DOI: 10.3390/pr14030536
原典: https://doi.org/10.3390/pr14030536

🤖 gxceed AI 要約

日本語

本研究は、CCUSにおける超臨界CO2パイプラインの漏洩検知手法を提案する。負圧波法と相互相関解析を組み合わせ、小漏洩時の信号識別困難性を克服。延長油田の実パイプラインモデルで検証し、5%以上の漏洩で相対誤差±5.40%以内の精度を達成。

English

This study proposes a leakage detection method for supercritical CO2 pipelines using negative pressure wave and cross-correlation analysis, addressing small leak detection challenges. Validated with Yanchang oilfield model, it achieves relative error within ±5.40% for leaks exceeding 5%.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本でもCCUSパイプライン構想が進む中、本手法は事業者や規制当局が漏洩検知基準を検討する際の参考になる。ただし、日本の地質・運用条件への適合検証は別途必要。

In the global GX context

As CCUS deployment expands globally, this paper provides a practical leak detection method for supercritical CO2 pipelines, relevant for projects under development in the US, EU, and China. It addresses a key operational risk for CCS infrastructure.

👥 読者別の含意

🔬研究者:Offers a validated cross-correlation approach for CO2 pipeline leak localization, extending negative pressure wave methods to supercritical conditions.

🏢実務担当者:Useful for CCUS project engineers designing leak detection systems for long-distance CO2 pipelines.

🏛政策担当者:Supports development of safety standards for CO2 transport infrastructure, but further field validation is needed.

📄 Abstract(原文)

Supercritical CO2 pipeline transportation is a critical component of the carbon capture, utilization and storage (CCUS) industry chain, where long distance operation introduces inherent risks of accidental leakage. During the leakage process of supercritical CO2 pipelines, throttling pressure reduction and the Joule–Thomson effect generate distinct negative pressure wave characteristics. The magnitude of the leakage directly impacts localization effectiveness, particularly under small leakage conditions where negative pressure wave signals are less pronounced, so the leakage is difficult to effectively detect. To solve this problem, the mutual correlation function model for pipeline leakage was developed by using the mutual correlation analysis method, and it was verified by the dense-phase CO2 leakage data from Trondheim University of Technology. Based on the TGNET software, the actual pipeline model of the Yanchang oilfield is established, and the captured leakage signal is imported into MATLAB for differential pressure conversion, using the verified cross-correlation function model of the differential pressure signal to calculate the time difference between the arrival of the negative pressure wave at the two ends of the pipeline. Finally, the actual leakage location was determined. The simulation results indicate that the leakage detection method based on mutual correlation analysis of negative pressure wave signals exhibits varying localization performance under different leakage rates. By enhancing negative pressure wave characteristics and utilizing mutual correlation analysis, this method effectively addresses the challenges of indistinct negative pressure wave features and difficult localization during small leakage conditions. When leakage exceeds 5%, the relative error is controlled within ±5.40%, meeting the preliminary localization requirements for rapid identification and regional determination in engineering applications. Through the application of actual engineering cases, it is shown that this method has high accuracy in pipeline leakage detection. These findings provide theoretical and methodological support for supercritical CO2 pipeline leakage detection in the CCUS projects currently under construction.

🔗 Provenance — このレコードを発見したソース

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