A controllability analysis of a pilot‐scale CO<sub>2</sub> capture plant using ionic liquids
Bibliographic record
Abstract
Nowadays there is a world concern on the impact and effect of large CO2 atmospheric concentrations on human health. Fossil‐fuel combustion processes in power plants are among the major contributors to this issue. Hence, it becomes important to develop new clean and sustainable processes aimed to reduce the amount of CO2 released to atmosphere by combustion processes in power plants. One of the best feasible manners to achieve this purposes lies in the use of a closed‐loop control system able to keep the amount of green‐house gases under specification even in the presence of unexpected scenarios. Of course, CO2 capture has been extensively researched in the past. However, in this regard the industrial practice has consisted in using Amines leading to sustainability and safety issues. Hence, it makes sense to seek for new and potentially environmental friendly process design to address CO2 reduction from power plants but applying a new type of sustainable stripping solvents. In this work we address the sustainable CO2 reduction issue from a process control point of view applying a previous design proposed by our research team based on the deployment of Ionic Liquids (IL) as potential green solvents and developing an efficient and decentralized multiloop control system. We demonstrate that the closed‐loop system is able to maintain the CO2 concentration levels under specification by testing in presence of several demanding scenarios. Overall, from an economic, sustainable and control point of view it looks feasible to replace the traditional amines‐based CO2 capture process by other alternatives based on the application of IL as potential green solvents. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3298–3309, 2016
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".