Biomass fly-ash derived Li4SiO4 solid for pilot-scale CO2 capture, Part I: Modelling for a waste to capture CO2 process
Bibliographic record
Abstract
This work presents a new modelled system of a biomass-based lithium orthosilicate solid adsorbent derived from industrial biomass fly-ash used to capture CO 2 from power plant flue gas emissions. The model includes pre-treatment of biomass fly-ash, the synthesis of adsorbent, which utilizes fly-ash as the silicone source and a laboratory produced lithium source, the adsorption of CO 2 from flue gas, and regeneration of adsorbent. The study compares the results from pre-treated and non-pre-treated biomass fly-ash, with benchmark CO 2 capture rates of 87 % and 89.7 %, respectively and a maximum CO 2 capture rate of 93.23 %. Key insights from the scenarios considered in this work show that an increased CO 2 flue gas composition requires higher adsorbent mass and the most effective flue gas volume to adsorbent mass ratio between 3.7–4.1; additionally, higher regeneration temperatures result in improved CO 2 capture while pre-treatment of fly-ash does not impact regeneration kinetics. Energy analysis show that the pre-treated fly-ash adsorbent is more efficient than the non-pretreated adsorbent but is not superior to amine-based post-combustion carbon capture. If effective heat integration were to be incorporated for the pre-treated and non-pre-treated adsorption processes, energy consumption could be reduced by 54 % and 85 % compared to amine-based capture, respectively. Cost analysis indicated that by incorporating a recycle stream for pre-treatment wastewater and altering the acid to solid ratio during pre-treatment acid wash, process costs may be reduced over 20 % making this a feasible alternative carbon capture process. • A CO2 capture model that uses a biomass-based adsorbent from fly-ash is presented. • Model includes pre-treatment section, adsorption of CO2 and regeneration of Li4SiO4. • Model’s performance under changes in key process parameters was investigated. • Energy and cost analyses were made and compared to MEA-based capture plant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".