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Record W3211445141 · doi:10.18280/mmep.080514

Modelling and Environmental Aspects of Direct or Indirect Dimethyl Ether Synthesis Using Digestate as Feedstock

2021· article· en· W3211445141 on OpenAlexvenueno aff
Aristide Giuliano, Enrico Catizzone

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsDigestateBiogasDimethyl etherAnaerobic digestionRaw materialWaste managementBiofuelEnvironmental sciencePulp and paper industryProcess engineeringChemistryMethanolEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The conversion of waste and residues towards high added value products has receiving a growing attention, as a reliable strategy to improve sustainability of emergent processes. Anaerobic digestion converts organic waste into biogas and digestate. While biogas may be used for energetic purpose, digestate has limited uses and with a low profitability. In this paper, dimethyl ether (DME) is adopted as target product which may be produced from digestate-derived syngas. Process simulation is carried out for both direct and indirect synthesis of DME and environmental aspects are assessed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.832

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 head0.031
GPT teacher head0.236
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations1
Published2021
Admission routes1
Has abstractyes

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