MétaCan
Menu
Back to cohort

Prospects of Rice Husk Gasification for Power Generation in Bangladesh

2013· article· en· W2060868084 on OpenAlexvenueno aff
Barun K. Das, Nirendra Nath Mustafi, S.M. Najmul Hoque

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersCentral Leather Research InstituteIndian Council of Social Science Research
KeywordsHuskAgricultureFossil fuelEnvironmental scienceBiomass (ecology)Rice strawAgricultural wasteWaste managementElectricity generationEnvironmental engineeringEngineeringAgronomyPower (physics)StrawGeographyBiology

Abstract

fetched live from OpenAlex

Electricity is the basic requirement to promote socio-economic development. In recent years, Bangladesh is facing severe power crisis all over the country, but the rural areas are the most vulnerable. This acute electricity crisis along with the conventional fuel crunch is affecting every sector of the country and economy is being crippled. To reduce the dependency on fossil fuels, rice husk which are widely abundant agricultural waste from rice industry could play a vital role in this regards. Bangladesh is an agricultural country and produces huge quantity of rice every year. In the year 2011, the total rice production was around 50.63 million tones. Husk is the waste biomass produce during the rice processing, on average it accounts around 20% of the rice produced on weight basis (10.12 million tones). The potential of power generation from rice husk by gasification is around 310 MWe in Bangladesh. The power plant to be installed near the large rice mills `cluster areas’ in Dinajpur, Bogra, Naogaon, Chapainawabganj and Ishwardi with the surplus rice husk. This paper consolidates information from various studies on the availability of rice husk, its characterization and estimates possible power potential that can be realized.

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 score0.298
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Technology Innovations in Renewable EnergySame topicEnergy and Environment ImpactsFrench-language works237,207