A novel energy system designed to cover electricity, heat, hydrogen and propane for decarbonized buildings
Bibliographic record
Abstract
This study presents an innovative approach to develop an integrated solar-biomass energy system designed to simultaneously generate electricity, heat, hydrogen, and propane, addressing the energy demands of the building sector. The system uses solar energy through a steam Rankine cycle and utilizes biomass pyrolysis to maximize efficiency and sustainability, with biochar as a valuable byproduct. Thermodynamic analysis reveals energy and exergy efficiencies of 65.7 % and 64.6 %, respectively. The system demonstrates strong production capacities, generating 1,688 kW of net electricity, 9,518 kW of heat, 49.02 kg/hr of hydrogen, and 1,094.29 kg/hr of propane. Parametric analyses highlight the impact of key variables, such as thermal storage temperature, pyrolysis pressure, and steam flow rate, on system performance. Raising thermal storage temperatures from 600 °C to 700 °C enhances both energy and exergy efficiencies while optimizing heat and propane output. Additionally, pyrolysis conditions significantly influence hydrogen and propane yields, with hydrogen production peaking at 53.28 kg/hr at 1.5 bar. This innovative design provides a pathway to efficient, low-carbon energy generation, underscoring the potential of integrated renewable systems to meet the building sector’s energy demands and sustainability goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".