Design optimization for an integrated tri-generation of heat, electricity, and hydrogen powered by biomass in cold climates
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".