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Record W2531538551 · doi:10.4172/2090-4541.s1.002

Screening of a wide range of pre-treatments for improving the algal biomass solubilization and biogas potential

2015· article· en· W2531538551 on OpenAlexvenueno aff
Serge R. Guiot

Bibliographic record

VenueNPARC · 2015
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsnot available
Fundersnot available
KeywordsBiogasBiomass (ecology)SolubilizationRange (aeronautics)Biochemical engineeringBiofuelEnvironmental scienceBiotechnologyPulp and paper industryEngineeringBiologyWaste managementEcologyBiochemistry

Abstract

fetched live from OpenAlex

The microalgal biomass conversion into methane as a biofuel offers the best energetic balance among the different biomass–to-biofuel scenarios for microalgae containing less than 40% lipids. The anaerobic degradation limitation of algae at around 50% emphasizes the need for pre-treatment to obtain higher methane production from algae. This study was performed using Scenedesmus sp. AMDD, green microalgae, as a model strain. Over 20 series of different pretreatments were evaluated, alone, or in sequence. The enzymatic pretreatments were performed with pectate-lyase and cellulase at incubation time from 2 to 24 hours. Chemicals pretreatments were done with H2SO4, NaOH or H2O2, at 0.2N and 2N and 2 to 24 h of reaction time. Thermal treatments were completed in an oven or a pressure vessel at 121 – 180°C or using a microwave (175 – 300°C). The enzymatic hydrolysis of Scenedesmus sp. AMDD followed with a three hours incubation in NaOH 0.2N resulted into a 75% solubilization. Similar results were found with incubation in 0.2N NaOH followed with short thermal treatment. Caustic and thermal pretreatments improved the methane production by around 12% compared with the anaerobic digestion of untreated algal biomass, at 335 ± 28 ml CH4 STP/g volatile solid (VS) added. The results from the enzymatic pretreatment were less encouraging with improvement of 2-7% of the methane production only. However, a combination of enzymatic with a thermal treatment successfully solubilized up to 75% of Scenedesmus sp. AMDD biomass. The resulting methane production, although up to 15% higher than for the control biomass, did not fully correlate with the increased dissolved organic matter. In anaerobic digesters continuously fed with solubilized biomass after combined enzymatic and thermal pre-treatment, the CH4 yield was improved by up to 35% in some operational conditions, while the degradation rate was faster, allowing for lower retention time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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