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Record W3037307759 · doi:10.1115/1.4047599

A Sustainable Trigeneration System for Residential Applications

2020· article· en· W3037307759 on OpenAlexaff
Azzam Abu-Rayash, İbrahim Dinçer

Bibliographic record

VenueJournal of Energy Resources Technology · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyRenewable energyHeat pumpEnvironmental scienceHeat exchangerThermal energy storagePhotovoltaic systemProcess engineeringExergy efficiencyWaste managementElectricityEnvironmental engineeringEngineeringMechanical engineeringThermodynamicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract This paper features the integration of two renewable energy sources, making a new trigeneration system for residential applications. The system is primarily powered by solar photovoltaic-thermal (PVT) along with geothermal energy. This trigeneration system consists of a ground source heat pump, solar system, high-grade and low-grade heat exchangers, a heat pump system, and a water storage tank (WST). The objective of this system is to provide the main commodities for residential use including domestic hot water (DHW), electricity, and space heating. The system is analyzed energetically and exergetically using thermodynamic-based concepts. The overall energy and exergy efficiencies of the proposed system are found to be 86.9% and 74.7%, respectively. In addition, the energy and exergy efficiencies of the PVT system are obtained to be 57.91% and 34.19%, respectively. The exergy destructions at the high-grade heat exchanger and the water storage tank add up to 36.9 kW, which makes up 80% of the total exergy destruction of the system. Additionally, parametric studies are conducted to evaluate the degree of impact that various important parameters have on the overall system performance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.561

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 science0.0010.000
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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