Efficient and low-emission approaches for cost-effective hydrogen, power, and heat production based on chemical looping combustion
Bibliographic record
Abstract
Hydrogen production has recently attracted much attention as an energy carrier and sector integrator (i.e., electricity and transport) in future decarbonized smart energy systems . At the same time, power production is highly valued in energy systems as other sectors like transport and heating become electrified. This work compares two different low-emission systems to produce electricity, hydrogen and heat. The proposed systems are based on chemical looping combustion combined with biomass gasification (CLC-BG) and steam methane reforming (CLC-SMR), both benefiting from heat integration between chemical looping combustion and downstream processes. A full process simulation is carried out in Aspen Plus for both systems and detailed modeling is performed for chemical looping combustion. The overall thermal efficiency is calculated to be 71.1 % for CLC-BG and 76.4 % for CLC-SMR. Co-feeding methane into the biomass gasification process of CLC-BG leads to an enhanced overall efficiency. In comparison to CLC-BG, CLC-SMR exhibits greater potential in terms of power and hydrogen generation , resulting in a higher exergy efficiency of 58.3 % as opposed to 44.6 %. Assuming market prices of 5.2 USD/GJ for biomass and 9.1 USD/GJ for natural gas, the lowest minimum hydrogen sale price is estimated to be 4 USD/kg for CLC-SMR.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".