eprintid: 8156
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/81/56
datestamp: 2023-11-09 16:20:01
lastmod: 2023-11-09 16:20:01
status_changed: 2023-11-09 16:11:56
type: article
metadata_visibility: show
creators_name: Musthafa, N.H.B.
creators_name: Hooi, Y.K.
creators_name: Hassan, M.F.
creators_name: Shariff, A.M.
creators_name: Khalid, K.S.
title: Automatic statistical inventory reconciliation for leak detection of petrochemical storage
ispublished: pub
note: cited By 3
abstract: This study evaluates software-based business workflow improvement using computerized statistical technique for management of gasoline retailer stations. Petrochemical leakage causes potential losses to the economy, life and environmental. Early detection can minimize the impact. A common technique to detect leakage of fuel tanks in gasoline stations is using systematic record and statistical analysis of the liquid level. However, the routine check using dipstick by station operator and manual analysis of inventory using inventory sheet and calculator are lacking efficiency. Computerized automation can result in continuous analysis and early detection. This study develops and evaluates a computerized system to that performs Statistical Inventory Reconciliation (SIR) on inventory data and presents the calculation of leak possibility, probability and rate. The result shows promising reliability and effectiveness. This study integrates workflow, algorithm, data structure and output visualization in a single framework to minimize human factor and to improve the process. The proposed system framework is non-intrusive to existing operation and can be used as a reference for development of expert systems in liquid inventory management and leak detection. © 2017 American Scientific Publishers. All rights reserved.
date: 2017
publisher: American Scientific Publishers
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040866896&doi=10.1166%2fasl.2017.10151&partnerID=40&md5=619dd5ba69a283ba024dab34b5e72dee
id_number: 10.1166/asl.2017.10151
full_text_status: none
publication: Advanced Science Letters
volume: 23
number: 11
pagerange: 10777-10781
refereed: TRUE
issn: 19366612
citation:   Musthafa, N.H.B. and Hooi, Y.K. and Hassan, M.F. and Shariff, A.M. and Khalid, K.S.  (2017) Automatic statistical inventory reconciliation for leak detection of petrochemical storage.  Advanced Science Letters, 23 (11).  pp. 10777-10781.  ISSN 19366612