Kinetic and parametric studies of refinery effluent treatment in electrochemical reactor
Bibliographic record
Abstract
Treatment of refinery effluent using an electrocoagulation reactor assisted with a natural coagulant, from Acacia tortilis, was investigated under controlled operating conditions. The influence of process variables – namely, type of electrode (copper (Cu), steel and aluminium (Al)), effluent pH (3·5–11·5), influent chemical oxygen (O2) demand (COD) of the effluent (605–2420 mg/l), coagulant dosage (1·0–6·0 g/l), voltage (15–45 V) and current (1·5–2·5 A) – on the COD removal efficiency was investigated. Among the different electrodes tested, the aluminium electrode performed well and could remove 74·2% at an equilibrium time of 90 min. Formation of the aluminium hydroxide (Al(OH)3) complex was identified as the working mechanism. The optimal conditions for better COD removal efficiency were identified as pH 5·5, coagulant dose of 4·0 g/l, voltage of 45 V and current of 2·5 A. The empirical relationship between the coagulant dose and the COD removal percentage was found to be exponential in nature. A pseudo-second-order kinetic model was found to represent the experimental data very well (coefficient of determination > 0·900) and the kinetic constant (k2) was estimated as 0·20 × 10−3(g/mg)/min at an initial effluent COD of 2420 mg/l.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".