Assessment of the potential of oily sludge char for removing organics from petroleum refinery wastewater
Bibliographic record
Abstract
Oil sludge (OS), a by-product of petroleum refinery (PR) waste, contains various toxic organic and inorganic compounds. Improper handling of OS poses serious environmental risks, highlighting the need for an effective and sustainable solution to mitigate these hazards and transform OS into a valuable product. This study converted OS into char-(OSC), including both pristine-OSC and ZnCl2-activated-OSC, for the removal of organic pollutants from refinery wastewater (WW) with a high COD concentration (89,233 mg/L). ZnCl2-activated-OSC produced at 400 °C (SB-Zn-400) showed superior adsorption capacity compared to pristine-OSC, due to enhanced oxygen-containing functional groups, crystallinity, thermostability, and superior degradation (OS). The adsorption process demonstrated rapid COD removal, reaching equilibrium within 2 h and achieving a 28 % reduction in COD. The adsorption capacity was found to be 420.5 mg-COD/g-OSC. SB-Zn-400 exhibited heterogeneous surface properties and supported multi-layer adsorption, with hydrogen-bonding and π–π interactions likely adsorption mechanisms. Moreover, the total concentration of organic compounds in PR-WW was >2700 mg/L, and SB-Zn-400 reduced this concentration to 34 mg/L, achieving >98 % removal. Although the adsorption treatment reduced the inorganic parameters of PR-WW, leaching of Mn, Ni, and Zn was observed, likely due to the nature of OS and the ZnCl2-activation process. Thermal regeneration of spent SB-Zn-400 allowed the reuse of OSC, with adsorption efficiency remaining higher than that of pristine-OSC, indicating that SB-Zn-400 has potential to be reused. These findings highlight the effectiveness of OSC in treating PR-WW, supporting a circular economy approach to enhance resource efficiency and minimize the environmental impact of OS from PR industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".