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Record W2745767050 · doi:10.1007/s41207-017-0031-z

Feasibility of OFMSW co-digestion with sewage sludge for increasing biogas production at wastewater treatment plants

2017· article· en· W2745767050 on OpenAlexaff
Annika Björn, Sepehr Shakeri Yekta, Ryan Ziels, Karl Gustafsson, Bo Svensson, Anna Karlsson

Bibliographic record

VenueEuro-Mediterranean Journal for Environmental Integration · 2017
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of British Columbia
FundersEnergimyndigheten
KeywordsBiogasAnaerobic digestionWaste managementSewage sludgeHydraulic retention timePulp and paper industryEnvironmental scienceMunicipal solid wasteSewage treatmentBioenergyMethaneMethanogenesisBiogas productionBiodegradable wasteWastewaterChemistryBiofuelEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Sweden has the ambition to increase its annual biogas production from the current level of 1.9 to 15 TWh by 2030. The unused capacity of existing anaerobic digesters at wastewater treatment plants is among the options to accomplish this goal. This study investigated the feasibility of utilizing the organic fraction of municipal solid waste (OFMSW) as a co-substrate, with primary and waste-activated sewage sludge (PWASS) for production of biogas, corresponding to 3:1 ratio on volatile solid (VS) basis. The results demonstrated that co-digestion of OFMSW with PWASS at an organic loading rate of 5 gVS l−1 day−1 has the potential to increase the biogas production approximately four times. The daily biogas production increased from 1.0 ± 0.1 to 3.8 ± 0.3 l biogas l−1 day−1, corresponding to a specific methane production of 420 ± 30 Nml methane gVS−1 during the laboratory experiment. Co-digestion of OFMSW with PWASS showed a 50:50 distribution of hydrogenotrophic and aceticlastic methanogens in the digester and enhanced the turnover kinetics of intermediate products (acetate, propionate, and oleate). Practical limitations potentially include the need for sludge dewatering to maintain a sufficient hydraulic retention time (17 days in this study), as well as additional energy consumption for mixing due to an increased sludge apparent viscosity (from 1.8 ± 0.1 to 45 ± 4.8 mPa*s in this study) at elevated OFMSW-loading rates.

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 score0.028
Threshold uncertainty score0.727

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.278
Teacher spread0.229 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

Explore more

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