Removal of phenolics from aqueous pyrolysis condensate by activated biochar
Bibliographic record
Abstract
Abstract Aqueous pyrolysis condensate (APC) is rich in acetic acid and has been utilized as feedstock for anaerobic digestion to produce biogas. However, various phenolic compounds dissolved in the APC act as inhibitors, negatively affecting the anaerobic digestion. In this work, we have investigated the feasibility of employing pyrolytic biochar as an adsorbent for the selective removal of phenolics from APC. Biochars derived from the pyrolysis of soft wood, rice husks, and sewage sludge and their respective activated forms have been tested as potential adsorbents for the removal of pure phenol from water solutions. The experimental results showed that, among the adsorbents studied, activated soft wood (ASW) biochar has the highest adsorption efficiency and capacity for phenol removal from water. The kinetic and isotherm studies showed that the phenol adsorption data with ASW biochar may be well described by pseudo second‐order equation and Freundlich model. Batch experiments were carried out to investigate the effects of pH, adsorbent loading, contact time, and temperature on phenolics adsorption onto ASW from APC. At optimal adsorption conditions (pH of 6.0, contact time of 30 min, adsorbent loading of 9.61 , and temperature of 25°C), an adsorption efficiency of 96.9% ± 1.8% and a capacity of 100.78 ± 2.7 mg · g −1 were achieved. Finally, the adsorption efficiency and capacity of ASW for phenolics removal from APC was successfully compared with those of commercial activated carbon, showing comparable results, which indicated the suitability of ASW as an environmentally friendly adsorbent.
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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".