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Record W4390470885 · doi:10.3390/ma17010226

Possible Options for Utilization of EU Biomass Waste: Pyrolysis Char, Calorific Value and Ash Content

2023· article· en· W4390470885 on OpenAlexaff
Ewa M. Iwanek, Urszula Nietrzeba, M. Pietras, Aleksandra Marciniak, Gustaw Głuski, Jakub Hupka, Miłosz Szymajda, Jakub Kamiński, Cezary Szerewicz, Aleksandra Goździk, Donald W. Kirk

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Toronto
FundersPolitechnika Warszawska
KeywordsBiocharBiomass (ecology)Heat of combustionWaste managementEnvironmental scienceCombustionCharRaw materialPyrolysisTorrefactionPulp and paper industryChemistryAgronomyEngineering

Abstract

fetched live from OpenAlex

The application of biomass as a co-feed in coal power plants and in standalone biomass power plants, as well as in char production for soil remediation, is a currently important issue. This paper reports on the investigation of biochar formation from agricultural waste crops that are used for soil upgrading, but which do not meet the standards of EU crops, as well as largescale food processing waste. These were compared to test results from basket willow, which is commonly used for energy generation. Food industry waste is often produced in cities on a large scale and is generally easier to process due to lack of other stream components. The key parameters, namely, the content of volatiles, energy content of the formed biochar and the composition of the ash, were determined for a number of herbaceous materials locally available in the European Union. All of them can be used as a cheap source of biochar. A novel procedure of capturing volatiles and hence minimizing the PAH content in the biochar, as well as enabling the recovery of energy from the volatiles is presented. Knowledge of the composition and form of elements in ash is very important for designing ash management systems if co-combustion is implemented. The aim of this study was to determine if the types of biomass are better suited for biochar production or energy generation.

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 score0.001
Threshold uncertainty score0.349

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 head0.050
GPT teacher head0.251
Teacher spread0.201 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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