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Record W4392345041 · doi:10.1016/j.ijft.2024.100618

Design optimization for an integrated tri-generation of heat, electricity, and hydrogen powered by biomass in cold climates

2024· article· en· W4392345041 on OpenAlexaff
Masoud Rezaei, Mohammad Sameti, Fuzhan Nasiri

Bibliographic record

VenueInternational Journal of Thermofluids · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectricity generationSizingReliability engineeringElectricityCapital costRenewable energyHydrogen productionEnvironmental scienceComputer scienceEnvironmental economicsProcess engineeringHydrogenEngineeringPower (physics)EconomicsElectrical engineering

Abstract

fetched live from OpenAlex

For green hydrogen energy systems driven by renewables, despite the complexities in design and operations, uncertainties related to availability of infrastructures or seasonality of resources are significant as well as the uncertainties in technical side such as adoption of technologies for energy generation, conversion, and storage. Such uncertainties put the economy and sustainability of these systems under shadows. Consequently, it has been attempted to balance and offset the impacts of uncertainties by means of providing the side products such as hydrogen. An enviro-economic optimization considering reliability, availability, and maintenance of a biomass-gasification-driven combined heating, hydrogen, and electricity system is considered in this study. The emission penalty cost as well as the electricity and hydrogen generation revenues are also pinpointed as part of the objective function which is the total cost. Such costs incorporate capital cost for purchase and installation of all modules, primary fuel (High Heat Value Woods) purchase, and transportation costs. Probabilistic approach using Weibull function is used for modeling reliability for the whole system. The most optimal values for total cost, hydrogen and electrical modules incomes, rated capacities, utilization times, reliability and maintainability indicators such as mean time to failure and maintenance intervals for modules are derived and compared. The sensitivity to performance parameters and sizing characteristics of those three modules are also investigated. The results support this notion that if there are opportunities to sell hydrogen, it is advantageous to integrate hydrogen module to the heating and power co-generation. The results show that minimum cost is obtained by devoting less rated capacities and utilization times to electricity modules in favor of increasing the hydrogen module utilization times and flow rates.

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)0.001
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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

Citations18
Published2024
Admission routes1
Has abstractyes

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