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Record W2313298844 · doi:10.3390/wsf-4-a008

Recovery of Sewer Waste Heat vs. Heat Pumps Using Borehole Geothermal Energy Storage for a Small Community Water Heating System: Comparison and Feasibility Analysis

2014· article· en· W2313298844 on OpenAlexafffund
Shahryar Garmsiri, Seama Kouhi, Marc A. Rosen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeothermal energyEnvironmental scienceWaste heatWaste managementHeat exchangerGeothermal heatingEnergy consumptionHeat pumpGeothermal gradientBoreholeRenewable heatGreenhouse gasEnvironmental engineeringEnergy recoveryWaste heat recovery unitThermal energy storageNatural gasHeating systemHeat recovery ventilationEngineeringHybrid heatEnergy (signal processing)GeologyMechanical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

The consumption of hot water represents a significant portion of national energy consumption and contributes to concerns associated with global climate change. Utilizing heat recovered from the sewer, or the stored heat by utilizing heat pumps with a borehole geothermal energy storage system, are simple and effective ways of heating water for domestic purposes. Reclaiming heat from the waste warm water that is discharged to the sewer or stored heat in a borehole geothermal energy storage system can help reduce natural gas energy consumption as well as the associated energy costs and greenhouse gas emissions. In this paper, sewer waste heat recovery is compared with heat pumps using geothermal energy storage systems for a small community shared water heating system including commercial and institutional buildings. It is found that the sewer heat exchanger method is relatively economical as it has the smallest rate of return on investment for the selected community size. The findings also demonstrate a reduction occurs in natural gas consumption and fewer CO2 gas emissions are emitted to the atmosphere. The results are intended to allow energy technology suppliers to work with communities while accounting appropriately for economic issues and CO2 emissions associated with these energy technologies.

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 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 score0.282
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.040
GPT teacher head0.262
Teacher spread0.222 · 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
Published2014
Admission routes2
Has abstractyes

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