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- korean
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received May 12, 2026
Revised June 18, 2026
Accepted July 2, 2026
Available online July 31, 2026
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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비침습 배관 진동 기반 암모니아 회수라인 종료 전이 검출 및 이상 회수 판정
Non-Invasive Pipe Vibration-Based Detection of End-of-Recovery Transition and Abnormal Recovery in Ammonia Recovery Lines
https://doi.org/10.9713/kcer.2026.64.3.105173
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Abstract
고순도 암모니아 회수 공정에서는 시창(sight glass) 관찰에 의존하므로 정상 회수와 종료 직전 기액 혼재 전이를 정량적으로 구분하기 어렵고, 체결부 부분 막힘에 의한 이상 회수도 즉시 식별하기 어렵다. 본 연구는 회수 배관 외벽에 부착한 비침습 디지털 진동 센서와 다단계 규칙 기반 상태기를 이용하여 회수 상태를 실시간 판정한다. 회수 개시 과도응답으로 세션별 기준 진폭을 설정한 뒤, 정규화 블록 통계량과 포락선 기반 활성 특성, 장시간 감쇠 추세를 결합하여 종료 전이와 이상 회수를 검출한다. 총 60세션(운전 상태별 각 20세션)의 후향 평가에서 정상 회수와 이상 회수 판 정 일치율 100%, 종료 전이 검출률 85%(17/20), 전체 판정 일치율 95%(57/60)를 보였다. 제안 시스템은 작업자의 시창 모니터링 부담을 줄이고 회수 종료 판단의 일관성을 높인다.
In high-purity ammonia recovery, sight glass inspection makes it difficult to distinguish normal recovery from the liquid–gas transition near end-of-recovery, and intermittent partial blockage at vent fittings cannot be identified visually. This study proposes a real-time monitoring method using a non-invasive digital vibration sensor on the outer pipe wall and a multi-stage rule-based state machine. A session-specific reference amplitude is set from the onset transient; normalized block statistics, envelope-based activity, and long-term decay tracking then detect end-of-recovery transition and abnormal recovery. A retrospective evaluation on 60 sessions (20 per state) showed 100% agreement for normal and abnormal recovery, 85% (17/20) transition detection, and 95% (57/60) overall agreement. The system reduces operator dependence on sight glass monitoring and improves end-of-recovery judgment consistency.
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