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An Overview of the Downdraft Rice Husk Gasifier Technology for Thermal and Power Applications

2013· article· en· W2095201907 on OpenAlexvenueno aff
Alexis T. Belonio, Joel A. Ramos, Manuel José C. Regalado, Victoriano B. Ocon

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsWood gas generatorWaste managementEnvironmental scienceBoiler (water heating)HuskEngineeringThermal efficiencyProcess engineeringAutomotive engineeringCoalChemistryCombustion

Abstract

fetched live from OpenAlex

An overview of the downdraft rice husk gasifier (DDRHG) for thermal and power applications is herein presented. The different designs of the downdraft rice husk gasifier with reactor diameter ranging from 0.10 meter to 1.20 meter are discussed in detail. Smaller units of the DDRHG were found to have performed well in fixed bed. Larger units of the gasifier, on the other hand, are suited for moving-bed type making possible continuous operation without discharging and recharging the reactor. Present thermal applications of the gasifier includes: domestic cookstove, bakery oven, dryers, rotary kiln, steam boiler, and torrefyer. The DDRHG is also used to run surplus gasoline engines for driving water pump, micro-mill, and electric generator without any modification. The advantages and limitations of the gasifier as well as its environmental and socio-economic benefits over the use of conventional fossil-fueled systems are enumerated. At present, the investment cost for the gasifier ranges from PHP2,000 to 2,500.00 (USD 1 = PHP40.00) per kWt for thermal application and PHP20,000.00 to 30,000 per kWe for power generation. The cost of using the gasifier is much cheaper than that of the conventional fossil fuel and the investment can be recovered in a shorter period.

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 score0.031
Threshold uncertainty score0.339

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 science0.0010.000
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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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