Combined Electrocoagulation and Chemical Coagulation in Treating Brewery Wastewater
Bibliographic record
Abstract
Significant over-strength discharge fees are often imposed on breweries for the disposal of high-strength effluent to sanitary sewers. In this research work, the removal performances of electrocoagulation (EC) compared with operating electrocoagulation and chemical coagulation in sequence (EC-CC) or vice-versa (CC-EC) was examined to determine the capability of treatment in reducing the strength of the wastewater. Optimal operating parameters regarding electrolysis time, initial pH, and applied power were determined in conjunction with nutrient removal performance, electrode consumption and energy usage. Combined EC-CC treatment has been demonstrated to be economically feasible for brewery wastewater applications from an energy consumption perspective due to the efficiency of nutrient removal and the reduction of sewer discharge costs. Treatment by EC-CC at 5 W for 20 min using aluminum electrodes resulted in enhanced and consistent removal efficiencies of 26%, 74%, 76%, and 85% for chemical oxygen demand (COD), reactive phosphorous (RP), total phosphorous (TP) and total suspended solids (TSS), respectively. Energy consumption was the main contributor to operating cost. By considering potential recovered over-strength discharge fees (ODF), EC-CC treatment is economically feasible and beneficial in a brewery wastewater application. The results demonstrated the effectiveness of the CC-EC process to remove phosphorous, organics and solids from brewery wastewater at lower power supply, so that the recovered ODF cost for CC-EC at 5 W-EC is 23% higher than at 10 W-EC.
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) | 0.000 | 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".