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Record W2282612517

Catalytic Production of Furfural by the Subcritical Hydrothermal Gasification of Flax Straw

2013· dissertation· en· W2282612517 on OpenAlexfundno aff
Laila Jaafari

Bibliographic record

VenueoURspace (University of Regina) · 2013
Typedissertation
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsnot available
FundersUniversity of Regina
KeywordsFurfuralStrawCatalysisHydrothermal circulationProduction (economics)Pulp and paper industryChemistryWaste managementEnvironmental scienceChemical engineeringOrganic chemistryEngineeringInorganic chemistryEconomics
DOInot available

Abstract

fetched live from OpenAlex

Developing new sources of energy that can mitigate greenhouse gas (GHG) emissions has generated a strong research interest in the past two decades. Renewable sources of energy have become strong candidates for replacing the conventional resources in order to ameliorate the high level of pollution caused by the use of conventional fossil fuels. Biomass is a type of renewable resource that is considered to be carbon neutral when used in producing fuels and chemicals. Flax straw is an example of biomass that accumulates in Canada in high amounts. It is difficult to dispose of because it does not decompose easily as a result of its tough fibrous nature. However, it can be used through a hydrothermal gasification process to produce gaseous fuels as well as some important liquid products. Hydrothermal gasification process was used in this research because it can deal with wet biomass without the necessity of the drying step. Furfural is an important chemical that has many industrial applications, and as such, was considered to be the major desired product through the hydrothermal gasification of flax straw using a solid acid catalyst. This study focused on the catalytic subcritical hydrothermal gasification of flax straw. The study was performed using a 600 mL autoclave batch reactor using flax straw with a fixed weight (10 g) in all the experimental runs. Three types of solid acid catalysts were explored in this study: γ–alumina, H-ZSM-5, and silica-alumina. Experimental parameters such as temperature (200-325 oC), pressure (0-60 bar), residence time (0-120 min) and weight of solid acid catalysts (0.5-1.5 g) were varied in order to obtain the optimum conditions and to select the best catalyst for producing furfural. The yields of both gas and phenol were also monitored in the study. The yield of gas was quantified using an online gas chromatograph (GC). The gas products included hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2) and trace amounts of methane (CH4) and ethane (C2H6). The yields of furfural and phenol were measured by gas chromatograph/mass spectrometer (GC/MS). The results showed that the production of furfural was affected by all the experimental parameters (temperature, pressure, residence time and weight of the solid acid catalysts). The highest yield of furfural was obtained using γ-alumina with 0.1 g as the optimum weight of catalyst per g of flax straw. The ranking of the three catalysts based on furfural production was: γ–alumina > HZSM- 5 > silica-alumina. This had a direct correlation with the ratio of Lewis to Brϕnsted acid sites which decreased similar to the ranking of the performance of the catalysts. A kinetic study of the catalytic subcritical hydrothermal gasification of flax straw using 1 g of γ–alumina was also performed. Kinetic data were obtained using 10 g of flax straw, autogenous pressure, temperatures in the range of 225-325 oC, and residence time in the range of 0-120 min. The data were analysed using an empirical power law rate model. The carbon conversion was calculated using the ultimate analysis, which gave the highest conversion of 66% at 325 °C compared to the conversion of 40% obtained for a previous non-catalytic study. The final kinetic model was: -rA = 􀯗􀯑􀮺 􀯗􀯧 = 7.038 * 10-2 e -9463.5/R*T (1 – XA)2. The activation energy achieved in this study was lower than the activation energy of 27,969.6 J/mol obtained by the non-catalytic study thus showing the importance of the catalyst in lowering the energy barrier. The predicted rates from the model showed good agreement with the experimental rates with an average absolute deviation of 8.6%.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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