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Record W3197371772 · doi:10.18280/ijdne.160408

Utilization Waste Brine Water Separator for Binary Electric Energy Conversion in Geothermal Wells

2021· article· en· W3197371772 on OpenAlexvenueno aff
Herianto

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringWorking fluidWellheadGeothermal energySeparator (oil production)BrineEnvironmental scienceWaste heatWaste heat recovery unitWaste managementGeothermal gradientElectricityElectricity generationHeat exchangerThermodynamicsEngineeringMechanical engineeringGeologyElectrical engineering

Abstract

fetched live from OpenAlex

Nowadays, geothermal is one of the most environmentally friendly energy that can replace the role of fossils energy by converting steam to electricity. Brine is one of the by-products of the production of geothermal wells that are generally not used or simply re-injected. In fact, brine can be converted into electricity using the binary cycle process. In binary cycle, brine from separator is used as a heater of working fluid and transform it into a vapor phase. The vapor will be used to turn turbines and generators to produce electricity. The working fluid selection in accordance with the heating fluid temperature becomes important because it results in optimization of the thermodynamic cycle. The temperature of the wellhead in the geothermal field will decrease 3% per year and reducing the heating fluid temperature in heat exchanger. So, in this paper intends to utilizes brine to heat the heat exchanger by using iso-butane, n-pentane, and iso-pentane because its critical temperature can be stable at 193℃ wellhead temperatures. From the results of predictions from brain 2 production well for 17 years with iso-butane in this binary cycle planning, can utilize waste brine water separator to converse electric energy to produce 4 MWh electricity.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.408

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Design & Nature and EcodynamicsSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207