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Techno-Economic Assessment of Integrating Thermal Energy Storage with Diesel Exhaust Waste Heat Recovery System for Remote Mines in Cold Climates

2020· preprint· en· W4285702077 on OpenAlexaffabout
Durjoy Baidya, Marco Antonio Rodrigues de Brito, and Ghoreishi-Madiseh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWaste managementWaste heatEnvironmental scienceWaste heat recovery unitDiesel exhaustThermal energy storageEnergy recoveryExhaust gas recirculationExhaust gasDiesel fuelEngineeringEnergy (signal processing)Mechanical engineeringHeat exchanger

Abstract

fetched live from OpenAlex

Detachment from the national gridline makes the remote mines in the cold climate regions of Canada solely dependent on diesel generators for power generation purposes. Notwithstanding, more than 30% of the consumed diesel by these generators is frittered away as heat through the exhaust. To endure the long harsh winters, these mines also require substantial amounts of heating which is usually provided by burning diesel or propane. In such a scenario, the installation of a diesel exhaust heat recovery system in these remote mines has been considered as a sustainable strategy to preheat the mine intake air. However, this combined heat and power generation strategy cannot provide all the necessary heating due to the daily misbalance between the heating demand and the available heat in the exhaust of the diesel generator. Coupling seasonal thermal energy storage with the waste heat recovery system is a possibility that seeks to resolve such issue. This study investigates the integration of a seasonal thermal energy storage with a diesel exhaust heat recovery system in a remote mine in northern Canada by analyzing several possible alternatives regarding capacity and rates of energy loss. The financial impact of these parameters has been added to show the viability of the proposed strategy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.034
GPT teacher head0.285
Teacher spread0.251 · 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 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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