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Record W4414524194 · doi:10.1002/cjce.70095

Enhancing energy recovery from waste through torrefaction: A study on municipal solid waste ( <scp>MSW</scp> ) fractions under <scp> N <sub>2</sub> </scp> and <scp> CO <sub>2</sub> </scp> atmospheres

2025· article· en· W4414524194 on OpenAlexafffundvenue
Fatemeh Salami, Naomi B. Klinghoffer

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsWestern University
FundersWestern University
KeywordsEnergy recoveryTorrefactionFlue gasMunicipal solid wasteHeat of combustionPyrolysisSawdustCarbon dioxideCarbonizationWaste-to-energy

Abstract

fetched live from OpenAlex

Abstract This study investigates the impact of torrefaction on municipal solid waste (MSW) fractions, focusing on energy recovery, calorific value enhancement, mass yield reduction, and energy densification under both nitrogen (N 2 ) and carbon dioxide (CO 2 ) atmospheres. As waste production increases globally, driven by population growth and industrialization, there is growing interest in waste‐to‐energy conversions to address both energy demand and waste management concerns. Torrefaction, a thermochemical pretreatment, enhances the properties of solid waste to make them more suitable for energy recovery processes like pyrolysis and gasification. This study demonstrated that torrefaction effectively addresses the low energy content of MSW, achieving an energy densification ratio up to 1.73. The process showed high energy efficiency, with energy recovery ranging from 69.3% to 99.15%, while different waste fractions exhibited varied behaviours during torrefaction. Lemon peels exhibited the highest energy densification while paper cups achieved the highest energy recovery but minimal energy densification. Wood waste fractions, such as white spruce sawdust and forest residues, demonstrated balanced performance with high energy recovery and moderate energy densification, making them ideal candidates for prioritizing high energy recovery applications. The results show that the use of CO 2 , representing flue gas, enhances volatile release and improves energy densification in some fractions, particularly forest residues, compared to N 2 , while also promoting better carbon retention at higher temperatures. Overall, this study highlights the importance of waste stream selection, torrefaction atmosphere, and temperature optimization to improve the efficiency of MSW torrefaction, offering insights for the use of flue gas torrefaction in waste‐to‐energy processes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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.

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

Citations2
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicMunicipal Solid Waste ManagementFrench-language works237,207