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Record W1274527041

Anaerobic Digestion CHP Solutions for the Urban and Rural Environments

2015· dissertation· en· W1274527041 on OpenAlexaboutno aff
Nathan Curry

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsWaste managementAnaerobic digestionEnvironmental scienceGreenhouse gasEnvironmental engineeringPopulationWaste-to-energyEngineeringIncinerationMethane
DOInot available

Abstract

fetched live from OpenAlex

Urban waste generation and disposal remains a major global issue. As the world’s population grows past the 7 billion mark and more people move to urban areas, the amount of waste generated will grow accordingly. The most promising solutions to this problem are waste to energy technologies in the form of biological treatment of organics through anaerobic digestion and thermal decomposition via plasma arc gasification. These two technologies can be used in the urban environment separately or complimentarily to reduce the volume of the waste being processed while also generating heat and power (CHP) and reducing transportation costs and greenhouse gas emissions. In this research, the feasibility of heating a small-scale anaerobic digester using an air source heat pump and solar heat gains from a greenhouse located on the roof of an urban building in Montreal, Canada, is investigated during the coldest month of the year. Small-scale implementation of anaerobic digestion systems for backup and emergency power is also investigated for both the urban and rural environments as a solution for increased grid blackouts caused by more frequent and more severe storms. Derating curves are determined for generators operating under extreme unbalanced load conditions in both the urban and rural environments. The benefits and disadvantages of induction and synchronous generator systems are presented for small to large-scale systems (20kW to 2000kW).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.259
Teacher spread0.232 · 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.

Study designObservational
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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