Investigation of Ash Deposition During Co-Firing Biomass/Peat with Coal in a Pilot-Scale Fluidized-Bed Reactor
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".