Novel Sequential Batch Electro-Fenton System for Total Kjeldahl Nitrogen Removal: Solution for Highly Polluted Industrial Wastewater
Bibliographic record
Abstract
A novel electro-Fenton sequential batch reactor (EF-SBR) was developed in this study which permits to overcome main drawbacks of Fenton process, sludge production. The primary objective of this research is providing a solution for treatment of industrial wastewater containing specifically high amount of Total Kjeldahl Nitrogen (TKN). For sustainable reason, a more detailed objective of this study is simultaneous removal of ammonia, total nitrogen, and organic nitrogen. Thereby, investigations in four phases were conducted to achieve the objectives. The predominant mechanism of this study is electro Fenton oxidation. In Phase 1, fundamental operating parameters were investigated to achieve optimal design for small scale batch system. Throughout phase 2, the medium scale electrokinetic reactor was developed in which the optimal technological parameters were adjusted to scale up process. A multi compartment large scale EK reactor was designed and tested in Phase 3 to optimize the energy consumption. The results of Phase 3 showed above 99% and 99.6% of ammonia and TKN removal by using potent oxidizing agent in an appropriate time interval which leads to an economical retention time. Throughout Phase 4, the EF-SBR (Electro-Fenton Sequential Batch Reactor) was designed to address a industrial situation. The highlights of Phase 4 were reducing retention time of the EF-SBR while obtaining above 99% removal efficiencies for ammonia, TKN, total nitrogen, and organic nitrogen. Conducted research demonstrated the feasibility of proposed method, as well as fractal analysis to find the pathway to construe the transient variations in the target concentrations while analyzing the samples in an adequate number of points over an extended exposure period. The proposed design is sustainable since limits supplying additional chemicals and optimizes energy use. The technology is ready for a full-scale application.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".