Potassium sulphate production from an aqueous sodium sulphate from lead‐acid battery recycling: Impact of feedstock impurities on products yields
Bibliographic record
Abstract
Abstract The increasing demand for renewable energy highlights the need for efficient energy storage solutions. Despite various available technologies, lead‐acid batteries remain preferred for many industrial applications due to their inherent advantages. However, their expanded use necessitates proper waste management and recycling practices. During lead‐acid battery recycling, Na₂SO₄ is generated as a waste product, which cannot be directly sold due to quality concerns and limited market demand. Consequently, advanced waste management techniques are required to comply with government regulations on industrial waste disposal. Despite these challenges, Na2SO4 serves as a vital precursor for producing K2SO4, a valuable fertilizer. Prior research on the glaserite process for converting Na2SO4 to K2SO4 has assumed Na2SO4 to be pure—without traces of impurities. However, Na2SO4 recovered from battery recycling contains various contaminants. To address this, HSC Chemistry software was used to model K2SO4 and NaCl production from impure Na2SO4 and KCl, considering feed impurities. Under ideal conditions—a 1 bar pressure, 25°C feed temperature, and 40°C reactor temperature—over 90% yield of K2SO4 and NaCl was achieved in the absence of impurities. However, the addition of impurities resulted in a reduction in yields. Notably, impurity levels ranging from 1% to 4% by weight still allowed for yields exceeding 90%. Furthermore, a review of reactor compositions revealed a significant depletion of potassium and chlorine ions which are crucial for K2SO4 and NaCl production as impurity levels varied from 0% to 10%. These findings emphasize the negative impact of impurities on K2SO4 and NaCl yields.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".