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Record W2898694619 · doi:10.1051/e3sconf/20186100017

A comparative analysis of heat recovery and product stabilization in fluid-bed and ablative pyrolysis systems

2018· article· en· W2898694619 on OpenAlexaff
Murlidhar Gupta, Benjamin King, Fernando Preto, Andrew McFarlan

Bibliographic record

VenueE3S Web of Conferences · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsPyrolysisVapoursCondensationLigninChemical engineeringProcess engineeringChemistryRaw materialCondenser (optics)Pulp and paper industryOrganic chemistryMaterials scienceWaste managementPetroleum engineeringThermodynamics

Abstract

fetched live from OpenAlex

Nearly all existing commercial pyrolysis technologies employ single-step rapid condensation of vapours from 500 oC to 50 oC using sprays of cold bio-oil or liquid hydrocarbon as a quench fluid. This approach produces raw bio-oil, a non-homogenous mixture of hundreds of oxygenated organic compounds including organic acids and water. Single-step quench also results in loss of high quality heat to the ambient. In this work, a novel 3-stage fractional condensation approach has been proposed. The intent is to produce targeted stable products for value added applications as well to enhance the overall efficiency of pyrolysis processes. The first phase of this research involved modelling and simulation of staged condensation of pyrolysis vapours using Pro/2 process software. A comprehensive pyrolysis model with 13 representative compounds was developed and validated. The Pro/2 model is able to simulate complex condensation of lignin and sugar fractions at high temperatures. Multiple cases involving staged condensation in ablative and fluid bed pyrolysis systems were investigated. In each case, there was a trade-off between high-quality heat recovery and early separation of lignin and sugars from organic acids. Results demonstrated that dew point depression adds additional complexity and limits heat recovery. However, judicious selection of condenser temperatures offers opportunity for early isolation of sugars and lignin from acids, thereby improving product stability.

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 score0.054
Threshold uncertainty score0.335

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 head0.018
GPT teacher head0.242
Teacher spread0.223 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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