Achieving net-zero CO2 emissions from indirect co-combustion of biomass and natural gas with carbon capture using a novel amine blend
Bibliographic record
Abstract
The application of indirect co-combustion of biomass (in the form of producer gas) and natural gas with CCS technology has been identified as an alternative approach for electricity generation while achieving net zero CO 2 emissions. Using the benchmark 5 M MEA (as a basis) and LCA methodology, it was identified that to achieve net-zero CO 2 emissions, 14.5 % producer gas (energy basis) is required. Different amine blends were developed and assessed against 5 M MEA. The best performing solvent was 4 M (2:2) AMP: 1-(2HE) PRLD with CO 2 absorption and desorption parameter of 4.5 % and 1667 % higher than 5 M MEA at absorption and desorption temperatures of 40 °C and 90 °C, respectively. At the desorption temperature of 110 °C, the lean loading of this optimum solvent and 5 M MEA were 0.02 and 0.24 mol CO 2 /mol of amine, respectively implying a higher cyclic capacity. The CO 2 removal efficiency of the final solvent at 110 °C was 31 % higher than that for 5 M MEA. Finally, Using this optimum solvent as the absorbent for CO 2 capture and applying the increased CO 2 removal efficiency to the LCA data, it was determined that 8.2 % of producer gas, on energy basis, is sufficient for achieving net-zero CO 2 emissions. Thus, using a solvent with a better absorption and desorption parameter allows for a reduction in the volume of biomass required for achieving net-zero CO 2 emissions. If the ratio of biomass volume requirement for 5 M MEA is maintained in the scenario where the optimum solvent is used, negative CO 2 emissions will be achieved.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".