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Record W4313426729 · doi:10.1080/15567036.2022.2156636

Environmental sustainability and exergy return on investment of selected solar dryer designs based on standard and extended exergy approaches

2022· article· en· W4313426729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsExergyExergy efficiencySustainabilityEnvironmental scienceEnvironmental Sustainability IndexEnvironmental engineeringSolar dryerWaste managementProcess engineeringEngineeringSolar energy

Abstract

fetched live from OpenAlex

Different solar dryer designs are available in the literature. However, it is essential to understand how the performance of the dryer designs contributes to environmental sustainability by reducing waste in the exergy-loop and mitigate CO2 emission into the atmosphere. The exergy-based sustainability indices include waste exergy ratio, lack of productivity (LOP), sustainability index, environmental destruction coefficient and exergy recovery ratio. The paper also introduces exergy return on investment (ExRoI) by applying an extended exergy accounting approach with the inclusion of exergy influx of material resources, capital and labor in the exegetic computations of the sustainability index. Four solar dryers consisting of an indirect solar-cabinet dryer, mix-mode solar dryer, hybrid solar dryer and a double-partitioned single-pass-collector solar dryer with a wind generator were used as a case study. Comparatively, the ExRoI is lower than the standard exergy-efficiency for the four dryers by 6.87 to 91.61%. In contrast, the standard exergy-based sustainability index was higher by 5.4 to 61.2%. Total invested exergy ranged from 0.04TJ to 0.081TJ. The values of waste exergy ratio, LOP, ERR, EDC, and carbon credit ranged from 0.47 to 0.79, 0.566 to 16.45, 5.44 × 10−4 to 1.37 × 10−1, 1.57 to 17.45 and 231.45 to $2908, respectively.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.194
Teacher spread0.177 · 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