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Record W2896894107 · doi:10.3390/en11102794

Hydrothermal Carbonization of Peat Moss and Herbaceous Biomass (Miscanthus): A Potential Route for Bioenergy

2018· article· en· W2896894107 on OpenAlexafffundabout
Poritosh Roy, Animesh Dutta, Jim Gallant

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Guelph
FundersMinistry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsMiscanthusPeatBiomass (ecology)BioenergyEnvironmental scienceHydrothermal carbonizationPhalaris arundinaceaMossCoalCarbonizationWaste managementAgronomyBiofuelWetlandChemistryBotanyEcologyEngineering

Abstract

fetched live from OpenAlex

Peat moss and miscanthus were hydrothermally carbonized (HTC) either individually or co-processed in a different ratio to produce hydrochar. The hydrochar and pelletized hydrochar were then characterized to determine if hydrochar can be used as an alternative to coal to produce bioenergy from existing coal-fired power plants in Ontario that have already been shut down. The properties of carbonized biomass (either hydrochar or pellets) reveal that fuel grade hydrochar can be produced from peat moss or from the blend of peat moss and miscanthus (agricultural biomass/energy crops). Hydrochar either produced from peat moss or from the blend of peat moss and miscanthus was observed to be hydrophobic and porous compared to raw peat moss or raw miscanthus. The combustion indices of carbonized biomass confirmed that it can be combusted or co-combusted to produce bioenergy and can avoid slagging, fouling, and agglomeration problems of the bioenergy industry. The results of this study revealed that HTC is a promising option for producing solid biofuel from undervalued biomass, especially from high moisture biomass. Co-processing of peat moss with rural biomass, a relatively novel idea which can be a potential solution to heat and power for the rural communities/agri-industry that are not connected with national grids and alleviate their waste management problems. In addition, the hydrochar can also be used to run some of the existing coal-fired power plants that have already been shut down in Ontario without interrupting investment and employment.

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 score0.008
Threshold uncertainty score0.476

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations32
Published2018
Admission routes3
Has abstractyes

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