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Record W4409742527 · doi:10.1002/cjce.25702

Innovative approaches in adsorbent production through pyrolysis of waste biomass: Effective parameters, modifications, and techno‐economic analysis

2025· article· en· W4409742527 on OpenAlexvenueno aff
Behnam Rezvani

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBiocharPyrolysisBiomass (ecology)Environmental scienceWaste managementPollutionActivated carbonAdsorptionEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract The growing demand for sustainable waste management solutions and protection of natural resources from pollution has increased interest in effective adsorbents. This study explores recent advancements in producing biochar and activated carbon derived from various waste biomass, including novel pyrolysis methods, such as microwave, flash, and vacuum pyrolysis. Moreover, innovative upgrading processes, such as ultrasound, electrochemical, and plasma modification, were assessed. This article also examines the role of chemical and physical modification in developing optimal pore structures and surface characteristics that improve pollutant removal efficiency. Key parameters that influence the effectiveness of pyrolysis were analyzed, such as temperature, heating rate, residence time, and reactor type. Furthermore, a techno‐economic analysis is presented, investigating the feasibility and cost‐effectiveness of biochar production. This comprehensive overview serves as a foundation for future research to advance the field of waste biomass‐derived adsorbents, with potential implications for pollution control and resource recovery.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.011
GPT teacher head0.194
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

Citations6
Published2025
Admission routes1
Has abstractyes

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