Benchmarking the Timmins Process – a novel approach for low energy pre‐combustion carbon capture in IGCC flowsheets
Bibliographic record
Abstract
Abstract This paper reports results from an initial benchmarking study of the Timmins Process, a novel pre‐combustion carbon capture process that uses a combination of traditional unit operations, DEPG scrubbing, carbon monoxide shift, and carbon dioxide liquefaction, in a unique arrangement. The study examines the performance of the Timmins Process embedded within an integrated gasifier combined cycle (IGCC) flowsheet and the results are compared to data from the US Department of Energy (DoE) cost and performance baseline studies for coal‐fired energy plants. Modelling was undertaken using UniSim R400 (Honeywell Inc.) with thermodynamic parameters for DEPG interactions being regressed from literature data; these results are also reported here. The net efficiency of an IGCC flowsheet incorporating the Timmins Process, with a carbon capture level of 91.8 % on a mass basis, varies between 33.8 % and 34.3 % depending on the process configuration and the cooling water temperature. This result compares very favourably to a DoE study for a conventional capture process embedded within an IGCC flowsheet that operated at an efficiency of 31.2 %. Further, more detailed, studies are recommended to assess the impact of various assumptions that underpin this work.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".