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Record W2888982173 · doi:10.3390/en11092286

Hydrothermal Carbonization of Biosolids from Waste Water Treatment Plant

2018· article· en· W2888982173 on OpenAlexafffund
Dhananjay V. Bhatt, Ankita Shrestha, Raj Kumar Dahal, Bishnu Acharya, Prabir Basu, Richard MacEwen

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsHealth PEIUniversity of Prince Edward IslandDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrothermal carbonizationBiosolidsCarbonizationHeat of combustionNitrogenCarbon fibersSewage sludgePhosphorusMoistureHydrothermal circulationWastewaterPulp and paper industrySewage treatmentDewateringChemistryEnvironmental chemistryWaste managementEnvironmental scienceMaterials scienceChemical engineeringEnvironmental engineeringCombustionAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

The high moisture content of biosolid from a wastewater treatment plant limits its use for agriculture and energy applications. This limitation could be obviated by hydrothermal carbonization, which requires less energy compared to other thermochemical treatment processes, and results in stabilized solid hydrochar product. The present study examined this option by hydrothermally treating the biosolid at three temperatures (180, 200 and 220 °C) for 30 min, and at 200 °C for 15, 30 and 60 min. An increase of 50% in the heating value of the biosolid was obtained after this carbonization. A reduction in the nitrogen concentration in hydrochar was noted with an increase in phosphorus concentration, but potassium concentration remained largely unchanged. Additionally, the carbon to nitrogen ratio in the hydrochar product was higher than the biosolid that makes it suitable for agriculture applications. The chemical oxygen demand of the process water was in the range of 83,000 to 96,000 mg/L. The study thus provides insight into high-value products that can be generated by the hydrothermal carbonization of biosolids.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.177
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2018
Admission routes2
Has abstractyes

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