Generic AI models for mass transfer coefficient prediction in amine‐based CO<sub>2</sub> absorber, Part II: RBFNN and RF model
Bibliographic record
Abstract
Abstract In this work, the radial basis function neural network (RBFNN) and random forest (RF) algorithms were employed to develop generic AI models predicting mass transfer coefficient in amine‐based CO 2 absorber. The models with operating parameters as input gave quite different prediction performance in different CO 2 absorption systems. To secure better applicability, extra parameters related to amine type and packing characteristics were introduced to reasonably describe mass transfer behaviors, respectively. Moreover, the generic models were proposed by considering all influencing factors of mass transfer in CO 2 absorber column. Furthermore, the performance of BPNN, RBFNN, and RF models was completely compared and fully discussed in terms of AARE. All three generic models could predict mass transfer coefficient of CO 2 absorber very well. It was found that the BPNN models provide the best predication with AAREs of below 5%. The developed generic model could serve as a fast and efficient tool for preliminary selection and evaluation of potential amines for CO 2 absorption. The framework of generic ML model development was also clearly presented, which could provide theoretical basis and practical guidance for the implementation and application of ML models in the carbon capture field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".