Investigation of Ash Deposition During Co-Firing Biomass/Peat with Coal in a Pilot-Scale Fluidized-Bed Reactor
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».