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Record W2950576938 · doi:10.1002/cjce.23575

Treatment of the real boiler cleaning wastewater in an anaerobic fluidized bed microbial fuel cell: Organic matter degradation, bioelectrochemistry, and kinetics

2019· article· en· W2950576938 on OpenAlexvenueno aff
Jiating Wang, Xinmin Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobial fuel cellChemistryChemical oxygen demandPulp and paper industryWastewaterFerrousCitric acidWaste managementNuclear chemistryAnodeOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

A single chambered air cathode anaerobic fluidized bed microbial fuel cell (AFB‐MFC) was simultaneously used to dispose of the real boiler cleaning wastewater (BCW) containing a high concentration of citric acid and to generate renewable energy. At the temperature of 40 °C and flow rate of 5.22 mL/s, the removal efficiency and power density were improved in AFB‐MFC with the abiotic cathode of the Pt/C modified carbon cloth. With the real boiler cleaning wastewater fed as substrate, the maximal removal efficiency of chemical oxygen demand (COD) in AFB‐MFC was up to 90 %, which was higher than the corresponding value of 84 % obtained in the anaerobic biological fluidized bed reactor (ABFBR). The maximum voltage and the maximum power density were 424.6 mV and 18.68 mW/m 2 , respectively, when the external resistance was 5000 Ω. Furthermore, the Haldane inhibition model was well fitted with experiment data (R 2 = 0.97–0.98) in AFB‐MFC/ABFBR. The inhibition of citric acid degradation in ABFBR was stronger than that of AFB‐MFC. The bioelectrochemical system of AFB‐MFC not only improved the charge transfer and but also accelerated the reaction rate of citric acid. The reduction of the ferric ion and oxidation of ferrous ion in AFB‐MFC played important roles in the degradation reaction of critic acid.

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 score0.007
Threshold uncertainty score0.292

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 head0.004
GPT teacher head0.156
Teacher spread0.153 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicMicrobial Fuel Cells and BioremediationFrench-language works237,207