Toward predicting CO2 loading capacity in monoethanolamine (MEA) aqueous solutions using deep belief network
Bibliographic record
Abstract
The viability of CO 2 capture projects, particularly through absorption with monoethanolamine (MEA) and other commercial absorbents, strongly depends on the CO 2 loading capacity. Therefore, comprehending the impact of variables on the CO 2 loading capacity of MEA is crucial in designing CO 2 capture units, which can be further optimized through multi-objective optimization. To this end, four machine learning models—Bagging Regression (BR), Categorical Boosting (CatBoost), Deep Belief Network (DBN), and Gaussian Process Regression with Rational Quadratic kernel function (GPR-RQ)—were utilized to predict the CO 2 loading capacity of MEA aqueous solutions. Temperature, partial pressure of CO 2 , and MEA concentration were inputted into the intelligent network to calculate the CO 2 loading capacity. The binary values of R 2 and standard deviation (SD), which were 0.9889 and 0.0628 for Bagging Regression, 0.9932 and 0.06586 for CatBoost, 0.9957 and 0.0588 for GPR-RQ, and 0.9971 and 0.0329 for DBN, confirm that DBN has the highest accuracy in statistical analysis, followed by GPR-RQ, CatBoost, and Bagging Regression. Additionally, graphical methods like scattered plots and relative deviation plots corroborate the superior performance of the DBN model over all other intelligent techniques. By conducting a relevancy factor analysis on DBN outcomes, sensitivity analysis demonstrates that pressure has the most significant influence among the inputs. Furthermore, the Leverage technique affirms that the DBN model has a substantial degree of validity in forecasting the CO 2 loading capacity of MEA. Finally, 3-D image plots were systematically examined to analyze the binary interactive effect of (temperature, CO 2 partial pressure), (temperature, MEA concentration), and (CO 2 partial pressure, MEA concentration) on the carbon absorption efficiency, which is essential to reach the net-zero emission purpose.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".