Organic pollutant removal from ice cream production wastewater via hybrid electrode connected electrochemical processes
Bibliographic record
Abstract
Abstract The ice cream production industry consumes a large amount of water, generating considerable volumes of wastewater. Therefore, effective and sustainable treatment methods for ice cream production plant wastewater (ICPPW) are essential. Electrochemical processes have emerged as promising alternatives to conventional treatment methods, offering high pollutant removal efficiency within a single reactor. However, research on hybrid electrochemical treatments for ICPPW remains limited. This study aimed to evaluate the effectiveness of hybrid electrochemical processes using simultaneous electrode configurations for the removal of organic pollutants, specifically chemical oxygen demand (COD) and total organic carbon (TOC). The biodegradability of these pollutants was further assessed using average oxidation state (AOS), carbon oxidation state (COS), and TOC/COD ratio indicators. Various electrode combinations were tested, including BDD/SS/SS, Fe/SS/TiRuO 2 , Al/SS/TiRuO 2 , Al/SS/Al, Fe/SS/Fe, TiRuO 2 /SS/graphite, Fe/SS/graphite, and Al/SS/graphite. Among these, the BDD/SS/SS configuration achieved the highest COD and TOC removal efficiencies of 95% and 87%, respectively. The optimal removal for all setups occurred within 40 min of operation time. At a current density of 41.66 A/m 2 , energy consumption ranged from 3.611 to 7.639 kWh/m 3 , current efficiency varied between 0.051 and 0.083, and electrical energy efficiency was between 2.841 and 18.96 kWh/m 3 . These findings highlight that the BDD/SS/SS electrode combination not only provides superior pollutant removal but also does so with low energy input, making it a viable and efficient method for the treatment and potential reuse of wastewater from ice cream production.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".