Good modeling practice for calibration applied to ion exchange breakthrough prediction
Bibliographic record
Abstract
Ion exchange (IX) is a key technology in resource recovery processes for demineralization and fit-for-purpose water production due to its inherent ion-selective recovery properties. A major bottleneck in the optimization of the IX process is the accurate prediction of ion breakthrough times, which has the potential to save on regeneration chemicals by maximizing resin utilization. However, the models used to predict ion breakthrough times are often unreliable due to poor calibration methods and significant uncertainty in parameter estimates. Consequently, we conducted local and global sensitivity analyses to identify the design and operational parameters that contribute most to the prediction of breakthrough curves. The global sensitivity analysis enabled the selection of a limited subset of parameters for calibration, demonstrating that only two parameters, namely the maximum adsorption capacity isotherm parameter and the resin bead particle size, require thorough calibration, resulting in a 76% improvement in the breakthrough prediction. We also showed that the calibration of additional, less sensitive or correlated parameters results in an insignificant improvement of the predictive power, with a 16% to 60% increased uncertainty in the breakthrough time prediction. The model was validated using three independent data sets, which showed a fairly accurate breakthrough time prediction, with a relative error ranging from 1% to 11%. Herein, we propose a robust calibration procedure, based on good modeling practice, that encompasses both sensitivity and uncertainty analyses and therefore provides a basis for process optimization. The framework is presented in a manner that allows for its application to analogous process settings.
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 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 | 0.001 | 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".