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Record W2089582060 · doi:10.1021/es062756p

Effect of Methanol on pH and Stability of Inorganic Biofilters Treating Dimethyl Sulfide

2007· article· en· W2089582060 on OpenAlexaff
Yuefeng Zhang, Steven N. Liss, D. Grant Allen

Bibliographic record

VenueEnvironmental Science & Technology · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsBiofilterChemistryDimethyl sulfideSulfurSulfuric acidMethanolNitrificationEnvironmental chemistrySulfideInorganic chemistryPulp and paper industryNitrogenOrganic chemistry

Abstract

fetched live from OpenAlex

The biofiltration of dimethyl sulfide (DMS) and other reduced sulfur compounds (RSC) results in acidification of biofilters due to the accumulation of the sulfuric acid in packing material. This may lead to a decrease in biofilter performance due to a drop in pH. Results obtained from continuous experiments using three bench-scale biofilters packed with inorganic material mixed with limestone show that methanol (MeOH) alleviates the pH drop and enhances the stability of biofilter performance and DMS removal. The pH drop in the biofilters treating DMS with MeOH is 4 fold slower than that in the control biofiler treating DMS only. For the biofilters with MeOH addition, the pH of the biofilters drops more gradually (0.044 pH units per day) when compared to the MeOH suspension periods when MeOH is not added (0.23 pH units per day). MeOH addition consumes oxygen and results in a lower conversion ratio of sulfide to sulfuric acid due to the formation of elemental sulfur, reducing acidification in the biofilters. Nitrification was found to be actively taking place in the control biofilter treating DMS without MeOH addition, contributing to the significant pH drop in the reactor. It is proposed that MeOH prevents acid production from nitrification likely by limiting oxygen and nutrients to nitrifying bacteria in the MeOH-fed biofilters.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.099
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.005
GPT teacher head0.232
Teacher spread0.226 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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