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Record W4361206220 · doi:10.1080/15567036.2023.2193160

Exergoenvironmental evaluation of kalina power generation system

2023· article· en· W4361206220 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 · 2023
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyEnvironmental scienceExergy efficiencyWork (physics)Electricity generationEnvironmental impact assessmentPower (physics)EngineeringProcess engineeringEnvironmental engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The basic needs of man are food, clothing, and shelter besides air and water. In providing these needs, power generation systems play a crucial role. Day by day, for various reasons such as explosion of population, the demand for power is increasing tremendously. Power generation systems need to generate more power. A novel power generation system suitable for recovering waste heat at a medium temperature range is examined in the present work. To assess the performance of a power generation system, energy and exergy measures are necessary. Energy measures provide enough details about the performance of the system. To know the systematic performance analysis, detailed exergy analysis alternatively profound as advanced exergy analysis and environmental impact are to be investigated. Exergoenvironmental analysis on the proposed Kalina power generation system has been carried out under hot sink conditions. Considering the proposed decision variables relative exergy destruction (ĖD/ĖP), relative environmental impact (Ẏ/ĖP), and relative investment cost (Ż/ĖP), the performance of the system has been assessed. The exergy destruction and the destruction cost rate of 29.23 kW and 0.478 $/hr at turbine inlet conditions of 185°C and 45 bar have been achieved. The exergoenvironmental factor fb and the relative difference rb have revealed that the components with high environmental impact have to be minimized. Turbine and HE4 are the components that contribute to higher total exergy and devise related impact on the environment.

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 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 score0.107
Threshold uncertainty score0.691

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 head0.013
GPT teacher head0.205
Teacher spread0.191 · 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