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Adsorption enhanced biological treatment of hydrothermal liquefaction aqueous phase derived from municipal sludge

2024· article· en· W4400412398 on OpenAlexaff
Kemal Aktaş, Huan Liu, İbrahim Alper Başar, Çiğdem Eskicioğlu

Bibliographic record

VenueBioresource Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHydrothermal liquefactionChemistryChemical oxygen demandBiochemical oxygen demandAdsorptionAqueous solutionMesophilePulp and paper industryWastewaterWaste managementEnvironmental chemistryOrganic chemistryBacteriaCatalysis

Abstract

fetched live from OpenAlex

Hydrothermal liquefaction (HTL) is a promising method for municipal sludge valorization through waste minimization and biofuel production. The process wastewater, HTL aqueous, presents a significant challenge for scale-up due to recalcitrant compounds. In this study, granular activated carbon (GAC) was used to remove potential inhibitors from HTL aqueous through adsorption to enhance aerobic and anaerobic biological treatment. GAC removed up to 61 % chemical oxygen demand (COD), 50 % biochemical oxygen demand (BOD) and potential inhibitors, such as total phenolic compounds (87 %) and N-heterocycles (90 % of pyridines) at 100 g/L. Conversely, most volatile fatty acids remained in HTL aqueous. Subsequently, mesophilic and thermophilic specific methane potential increased by up to 97 % and 83 %, respectively. BOD increased by up to 50 %, which enhanced BOD/COD ratio from 81 % to 93 % before and after adsorption. This study established the groundwork for HTL aqueous adsorption, described mechanism for pollutant removal, and provided insights for biological treatment.

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 score0.088
Threshold uncertainty score0.664

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 head0.016
GPT teacher head0.248
Teacher spread0.233 · 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

Citations14
Published2024
Admission routes1
Has abstractyes

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