IFTCP: An Integrated Method for Petroleum Waste Management under Uncertainty
Bibliographic record
Abstract
Petroleum waste management has been of much concern in recent years since pollution from petroleum industries may lead to various impacts and risks to environmental systems. In this study, an interval fuzzy two-stage chance-constrained linear programming (IFTCP) method is developed for planning petroleum waste management systems. The IFTCP improves upon the existing optimization methods by allowing uncertainties presented in terms of intervals, fuzzy sets, and probability distributions to be effectively incorporated within the optimization framework. Moreover, it can support the analysis of policy scenarios that are associated with economic penalties when the promised targets are violated. The developed method is then applied to a case of long-term petroleum waste management planning. Interval solutions, which are associated with different levels of constraint-violation risk and system satisfaction degree, have been obtained by solving two submodels based on an interactive algorithm. They can be used to generate decision alternatives and support an in-depth analysis of the tradeoff between system cost and system-failure risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".