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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".