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Record W3162956058

Investigation of Ash Deposition During Co-Firing Biomass/Peat with Coal in a Pilot-Scale Fluidized-Bed Reactor

2011· article· en· W3162956058 on OpenAlexaboutno aff
Yuanyuan Shao

Bibliographic record

VenueScholarship@Western (Western University) · 2011
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPeatEnvironmental scienceBiomass (ecology)Fluidized bedWaste managementDeposition (geology)CoalPilot plantFluidized bed combustionEnvironmental engineeringGeologyEngineeringEcology
DOInot available

Abstract

fetched live from OpenAlex

Biomass, a promising alternative to fossil fuels, has been applied widely for energy generation by co-firing technology in recent year particularly in the EU countries. In this thesis, a key issue of biomass co-firing technology - ash deposition in combustion, co-combustion and gasification, was comprehensively investigated in a pilot-scale bubbling fluidized bed reactor. A custom-designed, air-cooled probe was installed in the freeboard zone of the reactor to simulate the heat-transfer surface and collect ash deposits from the process. A local lignite coal, a woody biomass (white pine), and a Canadian peat were involved in the tests. The main varying operating parameters investigated in this study included: blending ratio, air/fuel ratio, moisture content and sulphur addition for the combustion/combustion tests; equivalence ratio, bed materials and fuel types for the gasification tests.\nA new parameter, "relative deposition rate" (RDA) was proposed in this study to evaluate the relative deposition tendencies of biomass fuels and biomass-coal mixed fuels against the coal as the base fuel for co-firing. As expected, co-firing of the lignite and the wood pellets (with a much lower ash-content than the lignite) resulted in a decreased superficial rate of ash deposition. However, co-firing of woody biomass and lignite coal did not significantly increase the ash deposition tendency in terms of the values of RDA, and more interestingly, co-firing of the fuel blend of 50% lignite-50% white pine pellets produced a lower RDA. Co-combustion of three-fuel blend at 20%lignite-40%peat-40%pine resulted in the lowest deposition rate and the least deposition tendency among all the combustion tests with various mixed fuels or individual fuels.\nAnother new and interesting discovery of this study was that fluidized-bed combustion of an individual fuel or a fuel blend with a higher moisture content produced not only a more uniform temperature profile along the fluidized-bed column but also a reduced ash deposition rate. A higher chlorine concentration in the feed would generally result in a higher tendency of ash deposition. Adding sulfur into the fuel of coal or peat could effectively decrease the chloride deposition in the ash deposits via sulphation. The sulphur addition could also reduce the ash deposition rate for the combustion of lignite, while it slightly increased the ash deposition rate for the peat fuel.\nIn air-blown gasification of a woody biomass and a Canadian peat, the experimental results demonstrated that among the four bed materials (olivine, limestone, iron ore, and dolomite), the use of olivine resulted in the lowest ash deposition rate. The superb performance of olivine in retarding ash deposition could be accounted for by its outstanding thermal stability and mechanical strength. The other three bed materials, in particular limestone, were fragile during the fluidized bed gasification, and the fractured fines from the bed materials were found to deposit along with the fuel-ash on the heat transfer surface, leading to higher ash deposition rates.\nFinally, mathematical models parameterized with interactions between fuel chlorine, alkali and ash particles were developed to analyze the ash and chlorine deposition behavior based on the experimental data from co-firing peat with lignite coal. The developed equations in this study can not only describe the dependence of the deposition rate and the ash chlorine content on the fraction of peat, but can also determine suitable range of the peat fraction for smooth operations, which would be useful for co-firing other fuel blends.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.243
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2011
Admission routes1
Has abstractyes

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