Biomass fly-ash derived Li4SiO4 solid for pilot-scale CO2 capture, part II: Waste management and utilization
Bibliographic record
Abstract
This study presents an enhanced process model for a carbon capture system that utilizes biomass fly-ash waste as a component in the synthesis of a solid adsorbent (Li 4 SiO 4 ) which is used as the CO 2 capture technology. As presented in Part I of this study, fly-ash in this process is pre-treated with acid and water washing units. In the present study, the enhancements made in the pre-treatment step include the incorporation of wastewater treatment, recycle, and elemental extraction/precipitate formation as a waste management tactic for the water treatments concentrate waste stream. The enhanced system recycles 85 % of the used water and the benchmark results show a 4.1 % cost reduction compared to an existing benchmark process. Additionally, results determine that the cost of resource consumption (CRC) can be reduced by 14.5 % compared to the existing benchmark system. Scenarios examine the impact of varying percentage of water recycled, the acid to fly-ash ratio, and biomass fly-ash source on the process. Key insights from these scenarios show that the amount of DI water and acid have the largest impact on process cost and CRC and that both straw and grass show potential as a silicone source for Li 4 SiO 4 synthesis. Furthermore, an analysis on further utilization of CO 2 and waste streams through elemental extraction of iron (III) hydroxide and calcium carbonate extraction were considered using different flowsheet configurations. Results from the pilot scale plant showed that iron (III) hydroxide is formed and can be removed from the waste stream for sale; however, it did not seem to be economically viable for a pilot-scale plant. Further test showed that selling captured CO 2 and waste streams to cement production is an attractive and sustainable alternative. A cost analysis from this strategy resulted in a 1.35 % decrease in process costs from the baseline results and a 1.61 % decrease in the CRC from the benchmark results thus promoting a circular carbon economy. • An enhanced CO2 capture model to that presented in Part I of this study is presented. • Enhancements made include wastewater treatment and recycle in pre-treatment step. • Elemental extraction/precipitate formation as a waste management tactic is considered. • Utilization of CO2 and waste streams through elemental extraction is investigated. • Selling captured CO2 and waste streams to cement production is a sustainable alternative.
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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 | 0.001 |
| 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 | 0.001 | 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".