MétaCan
Menu
Back to cohort
Record W3034463942 · doi:10.1680/jenes.20.00007

Experimental analysis of municipal solid waste blended with wood in a downdraft gasifier

2020· article· en· W3034463942 on OpenAlexvenueno aff
Ayyadurai Saravanakumar, T. B. Reed, M. Sudha

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2020
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsWood gas generatorWaste managementtar (computing)Municipal solid wasteCharEnvironmental sciencePelletsProducer gasProcess engineeringEnergy recoveryThermal energyThermal efficiencyFuel gasEngineeringCombustionMaterials scienceEnergy (signal processing)PyrolysisComputer science

Abstract

fetched live from OpenAlex

This paper presents the voyage of an innovative design and development and performance study of the multi-condition acceptability and flexible operation of a tar-free municipal solid waste (MSW)–refuse derived fuel gasifier. The innovation of the technology and its development are highly emphasised for converting waste into energy through gasification. The designed and fabricated gasifier (100 kg/h capacity) is a downdraft gasifier that is square in shape and operates slightly above atmospheric pressure to generate tar-free producer gas. The design operates with pellets and fluffy MSW. The generated producer gas was tested for both thermal applications. This designed gasifier can be scaled up and implemented to suit the requirements of industrial energy demand, which has wide-ranging applications. For optimum performance, a 25 kg amount of char is needed to gasify MSW completely. The energy output from the mass and energy balance is 92.5% optimal for effective gas generation. Energy generation from the designed gasifier and process optimisation for thermal energy from gas with enhanced efficiency abets in drastically reducing the cost of factory’s energy consumption. This also helps achieve reduced environmental emissions with good returns on investments.

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 score0.006
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.191
Teacher spread0.184 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207