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Record W7006456744

Torrefaction and Steam Explosion of Selected Biomass for Biofuel Production

2024· dissertation· en· W7006456744 on OpenAlexfundno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaBioFuelNet Canada
KeywordsTorrefactionSteam explosionPelletsSawdustPelletBiomass (ecology)StrawRaw material
DOInot available

Abstract

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Lignocellulosic biomass has demonstrated great potential as feedstock for pellet production, notwithstanding the fact that the industrial production of pellet is faced with some economic challenges. Pretreatment research has been focused on identifying, evaluating, developing, and demonstrating promising approaches that enhance the physiochemical and mechanical properties of biomass. Assessment of biomass pretreatment processes depends on a parameter called the severity factor, which is defined as the combined effect of temperature and residence time. This study summarizes pretreatment processing methods, namely, steam explosion and torrefaction of selected biomass feedstocks (sawdust and oat straw), processing characteristics, and product properties. The study also discusses the economics of densifying biomass, outstanding challenges, the potential for industrial applications of biomass-pretreated products, and global trends in biomass utilization. Steam explosion pretreatment of sawdust and oat straw was conducted under 3 stages: mild, medium, and severe conditions. The effects of temperature, time, and moisture content on the physiochemical and mechanical properties of pellets formed were examined. The p-values of the regression models for all the response variables (dimensional stability, tensile strength, and pellet density) studied were significant (p < 0.05), except for the pellet density of steam-pretreated oat straw pellets. The interaction of these three factors did not significantly affect the response variables of oat straw pellets. Microstructural examination of the pellets from steam-pretreated biomass revealed that the material contained particles that were more closely bonded and featured a cemented surface with fewer pores when compared to particles from untreated oat straw and sawdust. Microwave-assisted torrefaction was conducted on white spruce sawdust (WSS) at temperatures of 200 °C, 250 °C, and 300 °C and retention times of 5 min, 7 min, and 9 min in an inert environment. Torrefaction pretreatment is a mild form of pyrolysis that has the potential to produce high-quality raw material for making biofuel that serves as a replacement for coal in the bioenergy industry. The torrefaction process produces a solid carbon, commonly known as biochar, and condensable (torrefaction liquid (TL)) and non-condensable gases. In this study, torrefaction characteristics were investigated to observe its effects on the thermal and physiochemical properties of the pellets produced. During the torrefaction process, a significant mass loss associated with the decomposition of hemicellulose was observed. The hemicellulose content drastically reduced to approximately 1.8% from 19.25% and the cellulose content was reduced by approximately 10%, while the lignin gained approximately 35% as the severity increased. This led to an improvement in the higher heating value (HHV), hydrophobicity, bulk, particle density, pellet dimensional stability, and pellet density. However, the pellet tensile strength decreased as the torrefaction severity increased. Therefore, to enhance the tensile strength of the pellets, the introduction of a binder was necessary. Torrefaction liquid and sawdust were used as additives at different proportions during pelletization. The addition of binders (torrefaction liquid and sawdust) to the pellet formulation increased the tensile strength of the torrefied WSS by approximately 50%. The OH groups in the biomass break down to a limited degree due to dehydration. This hinders the formation of H bonds, thereby increasing the chances that the pretreated biomass will become hydrophobic. The Scanning Electron Microscope (SEM) graphs showed that the torrefied WSS pellets demonstrated more firmly glued surfaces with fewer pores spaces when set side by side with the raw pellets. The thermogravimetric analysis conducted showed that the torrefaction of WSS slightly reduced its thermal stability. Technoeconomic analysis of six case scenarios (wood pellet production from raw sawdust in a pellet plant (I), straw pellet production from raw oat straw in a pellet plant (II), integrated torrefied treated sawdust pelletization plant utilizing the torrefaction liquid (TL) as the pellet binder (III), integrated torrefied treated oat straw pelletization plant utilizing the torrefaction liquid (TL) as a pellet binder (IV), integrated steam treated sawdust pellet plant (V), and integrated steam treated oat straw pellet plant (VI)) was carried out to develop a process model for pellet production from sawdust and oat straw that uses torrefaction and steam explosion pretreatments prior to pelletization. The breakdown of equipment purchase cost showed that the torrefaction reactor is the most expensive unit with about 51% of the purchase cost. Scenarios 1, 2, 5, and 6 generated positive net present value at baseline model. Facility-dependent and feedstock costs were the major significant contributors to the pellet production cost. The minimum selling price of pellets obtained from Scenarios 1-6 were $113.4/t, $118.7/t, $283.4/t $298.7/t, $200.5/t, and $208.4/t, respectively. The profitability of pellet production as determined by the net present value (NPV), internal rate of return (IRR), and payback period was found to be sensitive to variations in feedstock cost.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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