Potentiality of petrochemical wastewater as substrate in microbial fuel cell
Bibliographic record
Abstract
Abstract The petrochemical wastewater (PCW) from acrylic acid plant possesses very high chemical oxygen demand (COD) due to presence of acrylic acid along with other organic acids. The treatment of PCW by conventional methods is energy intensive. The treatment of PCW with concurrent power generation by employing microbial fuel cell (MFC) could be a potential alternative solving the problem of energy and environment. The goal of the present paper is to evaluate the viability of treating the wastewater using anaerobic sludge as biocatalyst in a dual- chamber MFC for simultaneous power generation and wastewater treatment. This study demonstrates that anaerobic sludge (AS) could work as a biocatalyst producing maximum power density of 0.75 W/m3at current density and open circuit voltage (OCV) of 412 mA/m2 and 0.45 V respectively using PCW with an initial COD of 45,000 mg/L. The COD removal efficiency and the columbic efficiency (CE) were found 40% and 13.11%, respectively. The mechanism of electron transfer in the anode was analyzed by cyclic voltammetry (CV) and the resistances across the electrode/biofilm/solution interface were investigated by employing impedance spectroscopy (EIS). The current work proves the capability of the MFC for the treatment of acrylic acid plant PCW using anaerobic sludge (AS) as biocatalyst.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".