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Record W4386420184 · doi:10.3390/su151713222

Using Green Energy Sources in Trigeneration Systems to Reduce Environmental Pollutants: Thermodynamic and Environmental Evaluation

2023· article· en· W4386420184 on OpenAlexaff
Nima Ghasemzadeh, Shayan Sharafi Laleh, Saeed Soltani, Mortaza Yari, Marc A. Rosen

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyCogenerationEnvironmental scienceRenewable energyWaste managementNatural gasGeothermal energyCombined cycleEnvironmental engineeringFossil fuelBiogasElectricity generationGeothermal gradientEngineeringProcess engineeringTurbineMechanical engineeringPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

With rising electricity demand and environmental concerns, renewable energy is increasingly important. Geothermal power plants offer an opportunity to utilize natural energy sources advantageously. These systems can be coupled with other power cycles, like gas Brayton cycles, to maximize their potential output. Biogas is considered a viable replacement for fossil fuels such as natural gas to further mitigate pollutant gas emissions. In this paper, a biogas-fueled gas turbine coupled with a double-expansion geothermal cycle is proposed that uses, for heat recovery, combustion product gases to run a Kalina cycle. After heating the geothermal fluid twice for double expansion, the product hot gases also heat water in a domestic water heater. Also, three thermoelectric generators are utilized to increase the overall output. Using the geothermal cycle’s waste heat, a humidifier–dehumidifier desalination unit is considered for freshwater production. Green energy, freshwater, and heat are the system’s products, all of which are useful. The proposed system is examined from a thermodynamic perspective using EES V.10.561 (Engineering Equation Solver) software. For the considered input parameters, energy and exergy efficiencies of 36% and 44% are achieved.

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 score0.062
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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