Impact of Resin and Fatty Acids on Full-Scale Anaerobic Treatment of Pulp and Paper Mill Effluents
Why this work is in the frame
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Bibliographic record
Abstract
Wastewater generated during pulp and paper production usually contains large amounts of organic matter that may be converted to biogas through anaerobic treatment. These effluents can also contain a variety of inhibitors such as resin acids and long-chain fatty acids (RFAs) among other compounds. A multivariate data analysis was conducted with performance and water quality data from an anaerobic reactor at a pulp and paper mill, which provides unprecedented evidence for the long-term inhibitory effects of RFAs during full-scale operation. Although RFAs in the reactor influent did not cause serious process disturbance, they significantly contributed to the variability in the percentage removal of soluble chemical oxygen demand (percent sCOD removal) (∼45% to ∼75%), as well as the variability in volatile fatty acid concentration in the effluent (2 to >20 meq/L). Furthermore, a linear correlation was found between RFA concentrations in the reactor sludge bed and the percent sCOD removal. An effective strategy to remove RFAs before anaerobic treatment is upfront removal of particles within the primary clarifier. Based on an exploratory model, a graphical method is introduced that allows for quick assessment of the extent to which resin acids are sorbed to particles, and associated potential for upfront settling. When the pH of high-solids wastewater is below 7, the majority of resin acids will be associated with particles and a considerable fraction of these can therefore be removed by means of settling or floatation. Finally, a novel liquid chromatography–mass spectrometry method that rapidly quantifies major RFA constituents was developed. In the full-scale reactor, dehydroabietic acid (DHA) represented the bulk (∼92%) of the resin acids within the granular sludge. DHA in sludge warrants special attention and should be monitored closely.
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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 | 0.001 |
| 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 it