Assessment and optimization of several upgrading techniques to improve the quality of pyrolytic bio-oil from black spruce biomass
Bibliographic record
Abstract
In places like Quebec, with a long winter, the use of fossil fuels for heating applications, besides emitting large amounts of GHGs, has also brought economic problems to greenhouse owners because of its high cost over long periods of time.It is therefore imperative to develop a biofuel, such as pyrolytic oil, that can be used in heating boilers at a lower environmental and economic cost.A standard (ASTM D7544) has been developed to regulate the quality of pyrolytic oil or bio-oil to be used in stationary combustion systems.One of the main parameters established is the water content in the oil, since this can influence other properties of the bio-oil such as viscosity, calorific value, density, and acidity.The aim of this doctoral research project was to produce a pyrolytic oil capable of being used in heating applications and to offer the first step towards the chemical extraction process, by analyzing and evaluating the chemical composition of the feedstock, the operating conditions under which the pyrolysis is performed, and the type of condensation system used to collect the bio-oil.A semi-pilot vertical auger pyrolysis reactor designed by IRDA and CRIQ was selected as the technology to produce bio-oil, using black spruce as feedstock given its high availability in Quebec.As a first stage, the effect of the operational variables of the fast pyrolysis on the water content in the oil as well as its yield was studied.Four variables were selected from literature review and preliminary experiments.As a result, the optimal conditions obtained allowed the production of a bio-oil with one of the lowest water contents (16.8 wt.%) and dissolved solids content (0.1 wt.%) published for auger reactors.The second stage was the combination of the microwave-assisted hydrothermal (MHT) pretreatment with the optimal operational variables of fast pyrolysis obtained previously, aiming to evaluate and optimize the effect of this treatment on the quality of the hydrochar and, consequently, on the quality of the bio-oil.The optimization of this pre-treatment was targeted to minimize both hemicellulose and ash content responses.As a result, the pretreated biomass or hydrochar at optimal conditions showed a significant decrease in the ash content by 58% and 12.5% in the content of hemicellulose.By pyrolyzing this hydrochar, a higher total bio-oil yield was obtained, which increased by 24% and a reduction of 35% in moisture content (10.5 wt.%) was achieved.V cendres.En conséquence, la biomasse ou l'hydrochar prétraité dans des conditions optimales a montré une diminution significative de la teneur en cendres de 58% et de 12,5% de la teneur en hémicellulose.La pyrolyse de cet hydrochar a permis d'obtenir un rendement total en biohuile plus élevé, qui a augmenté de 24 %, et une réduction de 35 % de la teneur en humidité (10,5 % en poids).Enfin, la troisième étape de cette recherche a consisté en la conception et l'évaluation d'un système de condensation fractionnée.Deux et trois étapes de condensation ont été évaluées avec un profil de température décroissant pour évaluer l'efficacité de la condensation en fonction du nombre d'étapes et des températures utilisées.Les résultats ont montré une augmentation de l'efficacité de condensation de 35% en ajoutant le troisième étage de condensation.Dans l'ensemble, on a obtenu des modèles d'optimisation statistique qui prédisent les valeurs des variables les plus critiques de la biomasse et des conditions d'exploitation pyrolytique qui affectent la qualité de la biohuile, et qui peuvent être reproduits pour différents types de biomasse.Ce modèle offre également des méthodes efficaces pour la séparation de la biohuile en différentes fractions, qui serviraient de première étape dans l'extraction de produits chimiques d'intérêt.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".