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Applicability of ground source heat pumps as a bioremediation-enhancing technology for monoaromatic hydrocarbon contaminants

2021· article· en· W3135777782 on OpenAlexafffundabout
Iman Roohidehkordi, M. Król

Bibliographic record

VenueThe Science of The Total Environment · 2021
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeothermal gradientEnvironmental remediationEnvironmental scienceHeat pumpEnvironmental engineeringGeothermal energyBoreholePetroleum engineeringWaste managementRenewable energyGroundwaterGeothermal heatingContaminationHeat exchangerGeotechnical engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Geothermal or ground source heat pumps (GSHPs) are among the highest growing renewable energy technologies used for heating and cooling of buildings. However, despite being a well-established technology, their geo-environmental effects such as impact of the heat on the biosphere is still not thoroughly understood. This study uses FEFLOW software, to simulate heat and mass transport of a vertical closed-loop GSHP system. Transient flow and heat transport results for a multiple borehole system are presented which indicate long-term effects on subsurface temperature. Moreover, the impact of temperature change in a contaminated granular porous subsurface during remediation applications is examined. In particular, as subsurface temperatures are elevated due to geothermal heating, sorption will decrease and biodegradation rates will increase. These effects are examined in the context of contaminant transport, to evaluate the possibility of utilizing geothermal heating as a remediation strategy. The results revealed that temperature changes caused by GSHP operation can significantly enhance biodegradation of hydrocarbon contaminants. For instance, elevated subsurface temperature resulted in 97% reduction in benzene total mass, after one year of GSHP operation for a typical office building in Toronto.

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 score0.176
Threshold uncertainty score0.346

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 studies0.0000.001
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations12
Published2021
Admission routes3
Has abstractyes

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