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The feasibility of shortening the pretreatment time for improvement of the biogas production rate from rice straw with three chemical agents

2019· article· en· W2942575235 on OpenAlexaff
Jing Du, Yuting Qian, Yonglan Xi, Hongmei Jin, Xiangping Kong, Ning Zhu, LV Xi-wu, Yingpeng Zhang, Xiaomei Ye

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsStrawBiogasRice strawDistilled waterChemistryMethaneAnaerobic digestionBiogas productionYield (engineering)BioenergyPulp and paper industryNuclear chemistryAnimal scienceAgronomyMaterials scienceWaste managementBiofuelBiotechnologyBiologyChromatographyOrganic chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Solid-state chemical pretreatment at room temperature was adopted to evaluate the methane production potential of rice straw. Dried rice straw was homogenized with distilled water at a 1:3 ratio and pretreated for 6 h, 24 h, 72 h, 120 h, and 168 h. The effects of the pretreatment time with three chemicals (H2O2, Ca(OH)2, and NH3·H2O) on the methane yield were investigated. The results showed that the anaerobic digestion performance was improved for solid-state chemical pretreatment of room temperature-treated rice straw. The biogas yields from the treatments with H2O2 for 168 h, Ca(OH)2 for 72 h, and NH3·H2O for 120 h were 386.6 mL/g VS, 413.5 mL/g VS, and 390.5 mL/g VS, which were 35.8%, 45.2%, and 37.2% higher than that from the untreated rice straw, respectively. Therefore, it is feasible to shorten the pretreatment time of rice straw for these three chemical solutions.

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 score0.034
Threshold uncertainty score0.181

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 head0.013
GPT teacher head0.208
Teacher spread0.194 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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