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Record W4412699865 · doi:10.11159/ffhmt25.226

Enhancing Performance of District Heating Systems Using CO₂ as a Working Fluid by Operation Optimization

2025· article· en· W4412699865 on OpenAlexvenueno aff
Meisam Sadi, Rikke C. Pedersen, Robert Pratter, Ahmad Arabkoohsar

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
FundersÖsterreichische ForschungsförderungsgesellschaftEuropean CommissionHORIZON EUROPE Framework ProgrammeInnovationsfonden
KeywordsWorking fluidComputer scienceProcess engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

CO2-based district energy systems offer an up-and-coming alternative to water-based district energy systems due to the potential for lower investment costs, smaller components, and improved efficiency compared to traditional water-based systems.The heating system investigated in this study relies on a CO2-based district energy system, considering a heat pump in the central station for extracting heat from the ambient, while heat pumps in the consumer substations provide the required heating for the end users.The system performance is influenced by temperature throughout the cycle's operation.The purpose of this study is to find the optimal operating conditions of the network considering variations in the ambient temperature and demand profile throughout the year.Based on optimal operation of the system, a 4 % reduction in operation costs appears when compared to the benchmark system.The reduction of operational costs would enhance the economic feasibility of such projects.

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 score0.173
Threshold uncertainty score0.718

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 head0.212
Teacher spread0.200 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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