Hydrogel-based water removal in biodiesel–diesel blends: efficacy and impact on metal contamination
Bibliographic record
Abstract
After over a decade of using biodiesel blends, the challenge of its hygroscopic nature remains unresolved. Biodiesel’s chemical structure leads to significant water absorption, particularly during storage and distribution. This study explored water reduction in B30 biodiesel blends using two hydrogels: acrylate- and acrylamide-based, containing potassium (H1) and sodium (H2). A B30 blend with 600 ± 50 ppm water was filtered through a fixed-bed column with hydrogel at a flow rate of 10 L/h for 5 h per cycle. The results showed that both hydrogels effectively reduced water content; H1 decreased it to 155 ppm, while H2 reduced it to 187 ppm in the first cycle. Over seven cycles, H2 outperformed H1 with total adsorption capacities of 0.756 and 0.645 g H 2 O/g hydrogel, respectively. However, the adsorption process altered fuel metal content, indicating metal transfer from the hydrogel to the fuel. The concentrations of Ca, K, Mg, and Na increased after 30 min of contact with the hydrogel, with Na showing the most significant rise reaching up to 1.47 times the concentration in the feed. In contrast, the concentration of Al decreased, indicating that ion exchange occurred during the interaction between the fuel and the hydrogel.
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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 | 0.000 | 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 | 0.000 |
| 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".