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Record W3199184857 · doi:10.4236/jsbs.2021.113010

A Cost Analysis of Mobile and Stationary Pellet Mills for Mitigating Wildfire Costs

2021· article· en· W3199184857 on OpenAlexaff
Ryan Jacobson, Shahab Sokhansanj, Dominik Röeser, Jason Hansen, R. Bhushan Gopaluni, Xiaotao Bi

Bibliographic record

VenueJournal of Sustainable Bioenergy Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPelletPelletsPelletizingEnvironmental scienceBiomass (ecology)Raw materialBioenergyPulp and paper industryWaste managementAgricultural engineeringBiofuelEngineeringAgronomyChemistryMaterials science

Abstract

fetched live from OpenAlex

A comparison of traditional and mobile wood pellet mills found that mobile systems had higher production costs. Wildfire suppression costs have consistently exceeded British Columbia’s budget set for such activities. Pelletization of excess wood for bioenergy applications has been proposed as a possible method of reducing the overall costs of fighting wildfires. In this study, a traditional pellet mill produces wood pellets from new, marginal feedstocks for $182.24 ± 24.47 and a mobile pellet production system produces wood pellets for $402.71 ± 24.18 . The traditional pellet mill produces 90,000 with harvest residues being collected in the forest, transported to the pellet mill, dried, chipped, pelletized and then stored. The mobile system collects harvest residues from the forest, transports them to the forest landing where the trailer-mounted mobile pellet system is established and is then ground, pelletized and dried if needed. The mobile system uses a novel high moisture pelletization system and harvest residues to heat the biomass dryer used in the system. The mobile pellet system requires 22 systems to produce 90,000 and each system should relocate 9 times in a year to minimize production costs related to feedstock quality and scarcity. These mobile pellet systems can allow increased forest management in forest areas at high risk for wildfires and reduce the cost of suppressing wildfires in treated areas.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.237
Teacher spread0.228 · 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 designSimulation or modeling
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

Citations5
Published2021
Admission routes1
Has abstractyes

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