Optimization method of supercritical water treatment of oily sludge based on double constraints of treatment efficiency and energy consumption
Bibliographic record
Abstract
The high energy consumption of supercritical water oxidation (SCWO) technology is a major constraint on its industrial application. Existing studies have predominantly focused on treatment efficiency, lacking energy consumption evaluation methods and optimization of operational parameters based on energy usage. This study experimentally investigates the reaction sensitivity and interactions of temperature, oxidation coefficient, time, and pressure in the SCWO of oily sludge. The results show that temperature has the strongest reaction sensitivity for treatment efficiency, while the effect of pressure can be neglected. Enhancing another operating parameter within any given range of one operating parameter will promote the reaction. Additionally, a dual-constraint reaction prediction model, coupling treatment efficiency with energy consumption, was developed. Results show that temperature not only determines the endothermic heating of the reaction but also influences exothermic oxidation and thermal recovery through its effect on treatment efficiency. The optimal operating parameters for maximum COD removal efficiency (CRE) and minimum energy consumption were found to be T = 766 K, OC= 2.59, and t = 318 s, resulting in a CRE of 99.41 % and a theoretical energy consumption ( Q th ) of 85.99 kJ. These findings provide critical insights for the industrial application of SCWO technology in treating oily sludge.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".