Optimizing seawater purification: Ion exchange selective demineralization through single and multi-objective techniques
Bibliographic record
Abstract
This study focused on optimizing a selective demineralization process for seawater purification using ion-exchange technology. Experiments were conducted in three semi-batch reactors containing cation, anion, and mixed resins. Key process parameters included temperature (25 °C–50 °C), resin depth (23–82 cm), and pH (2–12). Statistical modeling and optimization were performed using Response Surface Methodology (RSM) with a central composite design, addressing both single and multi-objective criteria. A desirability function was used to assess process performance based on multiple response variables, such as the removal of trace metals (Ca2+, Mg2+, Mn2+, Zn2+, Fe2+, Cu2+, Ba2+, Cd2+), conductivity reduction, and total dissolved solids (TDS) elimination. Ten quadratic regression models were developed to describe the relationships between input parameters and responses, achieving high R2 values (≥0.7) for most responses except Cu2+, Mn2+, and Ba2+. Multi-objective optimization highlighted TDS, conductivity, and the removal of Ca2+, Mn2+, and Mg2+ as critical targets due to their significant impact on water hardness. The optimal conditions (temperature of 43.9 °C, resin depth of 75.45 cm, and pH of 5.9) yielded a composite desirability score of 0.77. Under these conditions, the process achieved over 99% removal efficiency for key cations (Ca2+, Mg2+), significant conductivity reduction, and near-complete TDS elimination. However, Mn2+ removal efficiency reached approximately 85%, likely due to its lower diffusion coefficient and higher hydration enthalpy. The results, particularly from the multicriteria optimization combined with desirability function approaches, highlight the effectiveness of ion-exchange resins in seawater demineralization and offer a robust framework for enhancing process performance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".