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Record W4396810208 · doi:10.1186/s40517-024-00293-7

Exergoeconomic evaluation and multi-objective optimization of a novel geothermal-driven zero-emission system for cooling, electricity, and hydrogen production: capable of working with low-temperature resources

2024· article· en· W4396810208 on OpenAlexaff
Hamid‐Reza Bahrami, Marc A. Rosen

Bibliographic record

VenueGeothermal Energy · 2024
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGeothermal gradientHydrogen productionElectricityProduction (economics)Zero emissionProcess engineeringEnvironmental scienceZero (linguistics)Computer scienceHydrogenWaste managementEngineeringChemistryPhysicsElectrical engineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Geothermal energy is an abundant natural resource in many regions around the world. However, in some areas, the temperature of the geothermal energy resource is too low to be efficiently harvested. Organic Rankine cycles (ORCs) are known for recovering heat from low-temperature resources and generating electricity. Furthermore, half-effect absorption chillers (HEACs) are designed to produce cooling with low-temperature resources. This study proposes a novel configuration that utilizes an ORC for electricity generation, a HEAC for cooling production, and a PEM electrolysis system to produce hydrogen. The power section consists of two turbines, one driven by the vapor produced from the geothermal flow expansion, which powers the PEM section, while the other turbine in the ORC is used to drive pumps and electricity production. First, the system is thermoeconomically analyzed for an initial set of inputs. Then, various parameters are analyzed to determine their influences on system performance. The analyses reveal that the system can work with geothermal source temperatures as low as 80 °C, but the exergy and energy (thermal) efficiencies decrease to around 17% under the base settings. Furthermore, the system is capable of working with resource temperatures up to 170 °C. Ten parameters are found to affect the system’s efficiency and effectiveness. To optimize the system, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to find the optimum conditions. The objective functions are exergy efficiency and unit polygeneration cost (UPGC), which can conflict. The optimization shows that the exergy efficiency of the system can reach 48% in the optimal conditions (for a heat source temperature of 112 °C and a mass flow rate of geothermal fluid of 44 kg/s), with a hydrogen production rate of 1.1 kg/h.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.207
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2024
Admission routes1
Has abstractyes

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