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Record W2953373790 · doi:10.82308/35531

Biomass combustion and gasification for greenhouse carbon dioxide enrichment

2015· article· en· W2953373790 on OpenAlexfundno aff
Yves Roy

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesBioFuelNet CanadaMinistry of Agriculture - SaskatchewanMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsFlue gasSyngasWaste managementWood gas generatorCombustionBiomass (ecology)Environmental scienceCarbon dioxideNOxCombustorPulp and paper industryEnvironmental engineeringChemistryCoalEngineering

Abstract

fetched live from OpenAlex

The increasing popularity of biomass systems for greenhouse heating provides a new source of CO2 for greenhouse producers. The main objective of this research project was to determine the feasibility of using the CO2 present in the flue gas produced during direct biomass combustion or syngas combustion of gasified biomass for greenhouse CO2 enrichment. A flue gas purification system was designed, constructed and installed on the chimney of a 35.17 kW wood pellet furnace (SBI Caddy Alterna) and on the calorific unit chimney of a 10 kW wood pellet pilot-scale downdraft gasifier (GEK Level 4, Model V3.1.0). The calorific unit system was designed and constructed to attach on the gasifier burner to calculate the heat produced during syngas combustion and to force the flue gas into a chimney to ease its analysis and purification. The purification system consists of a rigid box air filter (MERV rating 14, 0.3 μm pores) followed by heating elements and catalytic converters. The purification system attached to the wood pellet furnace was able to reduce CO concentrations from 1100 ppm to less than 1 ppm, NOx from 70 to 5.5 ppm, SO2 from 19 ppm to less than 1 ppm and trap particulates down to 0.3 μm with an efficiency greater than 95% as well as producing CO2 at a rate of 10.89 kg of CO2 hr-1.The purification system attached to the biomass gasifier was able to reduce CO concentration from 198.3 to 24.2 ppm, NOx from 128.1 to 9.8 ppm, NO from 122 to 9.3 ppm, NO2 from 2.4 to 1.1 ppm and SO2 from 10.4 to 1.7 ppm and remove particulates down to 0.3 μm with an efficiency greater than 95% as well as producing CO2 at a rate of 2.42 kg of CO2 hr-1. For direct combustion, the results are satisfactory since they ensure human and plant safety after dilution into the ambient air of the greenhouse. For syngas combustion, noxious components concentrations after dilution into the ambient air of the greenhouse are significantly below the limit exposure to ensure human and plant safety for CO, NOx and NO but are only slightly under the limit exposure for NO2 and SO2. Continuous flue gas analysis shows considerable instability in the flue gas composition which has a significant impact on noxious gases concentrations in the purified flue gas. Until these fluctuations are controlled and attenuated, purified flue gas from gasification cannot safely be used for greenhouse CO2 enrichment without endangering human and plant safety. Experiments show that direct combustion exhaust gas recuperation through the purification system reduces greenhouse heating costs by 18.8% which represents a savings of 14.7 $ per week for a tunnel greenhouse of 165.8 m2. The purification system substantially reduces greenhouse heating costs even for small operations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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