The Impact Of Process Parameters On The Amount Of Micropollutant In The Generated End Products During Sewage Sludge Pyrolysis
Bibliographic record
Abstract
The Impact Of Process Parameters On The Amount Of Micropollutant In The Generated End Products During Sewage Sludge PyrolysisAbstractThe use of biochar from sewage sludge as a soil amendment requires ensuring a minimal and safe pollutant load. However, sewage sludge can contain various micropollutants such as PFAS, polyaromatic hydrocarbons (PAHs) and dioxins. Data is lacking to guarantee the purity of biochar, hence institutions such as the European Commission banned its application as a soil amendment in agriculture. This study aims to determine pyrolysis process parameters that ensure the production of safe biochar according to available regulations. Pilot-scale tests demonstrated that increasing the pyrolysis temperature and adding a carrier gas during pyrolysis and cooling stages reduce the amount of micro-pollutants in biochar. Additionally, biochemical methane potential tests of the aqueous pyrolysis liquid showed its suitability as a co-substrate for anaerobic digestion. Overall, the findings of this research highlight that pyrolysis can serve as a suitable process for transforming sewage sludge into valuable end-products.Pyrolysis of sewage sludge can provide valuable end-products such as biochar, aqueous pyrolysis liquid, oil and gas phases. However, the use of biochar obtained from sewage sludge in agriculture is currently banned by the European Commission due to the uncertainty that it is free of pollutants. This work studies the optimal process parameters to reduce micro-pollutants in biochar using a pilot-scale pyrolysis plant. Moreover, the potential of APL as substrate for biogas production is assessed.SpeakerSchlederer, FelizitasPresentation time14:30:0014:50:00Session time13:30:0015:00:00SessionBehavior of Emerging Contaminants in Thermal Treatment ProcessesSession locationRoom S503 - Level 5TopicIntermediate Level, Microconstituents and Contaminants of Emerging Concern (Non-PFAS), PFASTopicIntermediate Level, Microconstituents and Contaminants of Emerging Concern (Non-PFAS), PFASAuthor(s)Schlederer, FelizitasAuthor(s)F. Schlederer 1; F. Schlederer 1 ; E. Martín-Hernández 2; C. Vaneeckhaute 3;Author affiliation(s)BioEngine Research Team on Green Process Engineering and Biorefineries, Department of Chemical Engineering, Université Laval, Québec, QC, Canada. CentrEau Water Research Center, 1065 Avenue de la Médecine, Québec, QC, Canada 1; BioEngine Research Team on Green Process Engineering and Biorefineries, Department of Chemical Engineering, Université Laval, Québec, QC, Canada. CentrEau Water Research Center, 1065 Avenue de la Médecine, Québec, QC, Canada 1 ; BioEngine Research Team on Green Process Engineering and Biorefineries, Department of Chemical Engineering, Université Laval, Québec, QC, Canada. CentrEau Water Research Center, 1065 Avenue de la Médecine, Québec, QC, Canada 2; BioEngine Research Team on Green Process Engineering and Biorefineries, Department of Chemical Engineering, Université Laval, Québec, QC, Canada. CentrEau Water Research Center, 1065 Avenue de la Médecine, Québec, QC, Canada 3;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159083Volume / Issue Content sourceWEFTECCopyright2023Word count20
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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".