A Comparative Review and Multi-criteria Analysis of Petroleum Refinery Wastewater Treatment Technologies
Bibliographic record
Abstract
The global economy’s continued dependence on fossil fuels is associated with a multitude of environmental concerns, including the production of hazardous wastes in petroleum refineries. Large quantities of petroleum refinery wastewater (PRWW) are produced daily, requiring the development of appropriate treatment methods. Activated sludge biological treatment is commonly used to treat PRWW, however this treatment method has a high sludge production, high operational time and may not be optimally suited for the variable loading conditions of refineries. Multi-criteria analysis is a tool capable of evaluating different wastewater treatment technologies through the weighted consideration of multiple environmental and economic factors. The following methods of treating PRWW were reviewed and evaluated using a multi-criteria analysis (MCA): biodegradation, advanced oxidation processes, electrocoagulation and microbial fuel cell technology. The MCA considered the removal efficiencies, sludge production, cost-benefit, process complexity and operational time of each method and was conducted under six different weighting scenarios. Advanced oxidation processes were preferred by this analysis under all six scenarios, with overall index scores (OIS) ranging from 7.84 to 8.51 out of a possible 10 points. Biodegradation of PRWW obtained was found to have the greatest overall removal efficiencies, however the high operational time and sludge production of this method resulted in a maximum OIS of 7.59. Electrical methods, such as electrocoagulation and microbial fuel cell technology required further improvements in removal efficiencies to be considered as a standalone treatment method. Further research into all methods, particularly microbial fuel cell technology is recommended. DOI: http://dx.doi.org/10.5755/j01.erem.74.4.21428
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".