Design optimization for an integrated tri-generation of heat, electricity, and hydrogen powered by biomass in cold climates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it