Biomass fly-ash derived Li4SiO4 solid for pilot-scale CO2 capture, Part I: Modelling for a waste to capture CO2 process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it