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Comparison between biodegradable polymers from cassava starch and glycerol as additives to biogas production

2016· article· en· W2508337295 on OpenAlexaboutno aff
Paulo André Cremonez, Armin Feiden, Joel Gustavo Teleken, Samuel Nelson Melegari de Souza, Michael Feroldi, Thompson Weiser Meier, Jhony Tiago Teleken, Jonathan Dieter

Bibliographic record

VenueSemina Ciências Agrárias · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
Fundersnot available
KeywordsGlycerolStarchBiogasPulp and paper industryChemical oxygen demandChemistryAnaerobic digestionWastewaterBiodegradationBiogas productionVolume (thermodynamics)Total dissolved solidsWaste managementFood scienceEnvironmental scienceOrganic chemistryEnvironmental engineeringMethane

Abstract

fetched live from OpenAlex

In this study, we compared cassava starch-based biodegradable polymers (PBMs) and glycerol (G) as additives used to increase biogas production from the co-digestion of swine wastewater (ARS). We chose to work with an inoculum comprising 40% (v/v) of the total volume of the reactor; this inoculum was obtained from a Canadian model digester for treating swine waste. In the anaerobic digestion process, batch reactors were used on a laboratory scale with a total volume of approximately 4 L and a working volume of 3.2 L. Three treatments were conducted to compare the efficiency of solid removal, the chemical oxygen demand (COD), and the production of biogas. The first treatment contained only swine waste; the second included the addition of glycerol at 1, 3, and 5% (w/v); and the third treatment included the addition of 1, 3, and 5% (w/v) of PBM residue in relation to the swine wastewater. From the results, it can be concluded that higher yields were obtained for the treatment with 3% PBM and 1% glycerol. Most treatments showed high removal rates of total solids and total volatile solids. Reductions lower than 70% were obtained only for treatments with PBM and glycerol at a ratio of 5%.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.999

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)0.0020.000

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 head0.037
GPT teacher head0.287
Teacher spread0.250 · 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.

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

Citations5
Published2016
Admission routes1
Has abstractyes

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