메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
이두진 (한국수자원공사 K-water 연구원) 김주환 (한국수자원공사 K-water 연구원) 김도환 (한국수자원공사 K-water 연구원) 김경필 (한국수자원공사 K-water 연구원)
저널정보
대한상하수도학회 상하수도학회지 상하수도학회지 제24권 제5호
발행연도
2010.1
수록면
495 - 508 (14page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
The aim of this study is to develop a quantified water loss Index to evaluate and manage leakage scientifically for the reduction of non-revenue water in water distribution systems. For the purpose, unavoidable background leakage suggested from UK water industry and IWA, and allowable water leakage in accord with the concept of allowable water loss are proposed by analyzing the inflow into two study water districts and the short-term water use of each customer in the districts. The study distribution areas are selected among the metered districts with good maintenance of leakage after improvement activities in Nonsan, medium sized city in Korea. Estimation models of allowable leakage are developed by metering and analyzing the minimum night flow at residential and commercial areas in the city. In the results of the investigation, it is estimated that background night flow in residential area was larger than that of commercial area where the types of business shows small water use characteristics. Meanwhile, night flow and background water loss on internal plumbing systems show great differences for each district which is influenced much by the water use characteristics and facilities scale. Based on metering water use data in various districts, leakage management criteria can be established under the consideration of domestic conditions in Korea by analyzing separated real water use and background leakage and it is possible to apply into presentation of optimal leakage level and reasonable time for working activities for leakage reduction.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0