From neural network to neuro-fuzzy modeling: Applications to the carbon dioxide capture process
Bibliographic record
Abstract
Research on improving efficiency of the amine-based post combustion carbon dioxide (CO2) capture process has been ongoing during the past decade. A good understanding of the intricate relationships among parameters involved in the CO2 capture process is important for process optimization. The objective of this study is to uncover relationships among the significant parameters impacting CO2 production by modeling the historical real-time process data. The data were collected from the amine-based post combustion CO2 capture process at the International Test Centre of CO2 Capture (ITC) located in Regina, Saskatchewan of Canada. Relevant literature review and opinions from the experienced engineers of the ITC CO2 capture plant suggested that the four parameters of reboiler heat duty, lean loading, CO2 absorption efficiency and CO2 production rate are the key parameters for assessing efficiency of the process. The eight process parameters that influence these four consequent or output parameters were identified as the conditional or input parameters. In this study, two artificial intelligence techniques were applied for modeling the relationships among the conditional and consequent parameters: (1) artificial neural network combined with sensitivity analysis and (2) neuro-fuzzy modeling. The results from the two modeling processes were compared, and it was observed that the neuro-fuzzy modeling technique was able to achieve on average higher accuracies than the combined approach of neural network modeling and sensitivity analysis.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".