DMAIC Six Sigma Methodology in Petroleum Hydrocarbon Oil Classification
Bibliographic record
Abstract
This research focuses on the use of the DMAIC method (Define, Measure, Analyze, Improve and Control) as a Six Sigma approach in studying oil spill fingerprint of samples recovered from Peninsular Malaysia and Sabah (East Malaysia). The DMAIC approach in this study was used as a way to classify oil types based on data obtained from GC-FID and GC-MS measurements. The cause-effect diagram was used to define the factors leading to the failure of the oil spill fingerprinting based on inaccurate oil type clustering. Discriminant Analysis (DA) was also applied to quantify the root-cause of the failure. An Ishikawa diagram obtained in the analysis phase identifies the potential failure causal. Principal component analysis (PCA) was applied and was successful in discriminating four clusters of oil types, namely diesel, heavy fuel oil (HFO), mixture oil lube and fuel oil (MOLFO) and waste oil (WO) with a total variance of 85.3%. In the control phase, the use of a Pareto chart indicated 100% cumulative percentage of oil type clustering with a 95% confidence level. The DMAIC approach to be effective in solving oil spill fingerprinting problems and results in quality improvement in the clustering of oil spills into the different hydrocarbon types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".