Improving oil classification quality from oil spill fingerprint beyond six sigma approach. Issue 1 (15th July 2017)

• Record Type: Journal Article
• Authors: Juahir, Hafizan; Ismail, Azimah; Mohamed, Saiful Bahri; Toriman, Mohd Ekhwan; Kassim, Azlina Md.; Zain, Sharifuddin Md.; Ahmad, Wan Kamaruzaman Wan; Wah, Wong Kok; Zali, Munirah Abdul; Retnam, Ananthy; Taib, Mohd. Zaki Mohd.; Mokhtar, Mazlin
• Is Part Of: Marine pollution bulletin. Volume 120:Issue 1/2(2017)
• Page Start: 322
• Page End: 332
• Publication Date: 2017-07-15
• Subjects: Quality engineering -- Six-sigma -- Oil classification -- Fingerprinting -- Hydrocarbon; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2017.04.032
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat > Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Highlights: Adoption of six-sigma approach in oil classification Four oil types discriminated via six-sigma approach Oil spill fingerprinting from GC-FID and GC–MS