Analysis of biomass as a feedstock in a chemical looping‐based polygeneration process for <scp>CO<sub>2</sub></scp> valorization
Bibliographic record
Abstract
Abstract From the viewpoint of circular economy, the utilization of biomass as a preferred feedstock for power generation with carbon capture has become prevalent. Biomass utilization leads to a carbon‐neutral balance by leveraging its inherent carbon that was absorbed from the atmosphere. While chemical looping combustion (CLC) is a promising carbon capture technology for solid fuels such as coal, this paper further explores the feasibility of using biomass in CLC along with valorization of CO 2 to valuable chemicals. A polygeneration approach utilizing biomass has been proposed to produce power and value‐added chemicals (methanol and dimethyl ether [DME]), thus yielding an integrated CO 2 capture and utilization system. Biomass has a significant amount of oxygen and a higher H:C ratio than conventional fuels like coal. The effect of the same has been analyzed by assessing biomass as a feedstock in the proposed process and evaluating the effect of higher oxygen content towards the objective of CO 2 valorization relative to that of coal. Furthermore, a comparative discussion of the suitability of biomass compared to coal from a valorization perspective has been presented. Detailed techno‐feasibility analysis on the schemes and optimization studies to maximize the performance of the system in terms of energetics, CO 2 mitigation, and profitability has been conducted. From the analysis, it has been identified that the utilization of biomass as a feedstock in CLC‐based processes results in higher chemical production rates and higher profits with lesser CO 2 emissions than coal. The above analysis and evaluation has been carried out through simulations using Aspen Plus® software.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".