Application of indigenous inorganic sorbents in combination with membrane technology for treatment of radioactive liquid waste from decontamination processes
Bibliographic record
Abstract
Abstract The purpose of the work was to improve the process for treatment of liquid radioactive waste containing complexing agents, which are generated during the decontamination operations. We performed some experiments using simulated waste solutions like secondary waste from the modified CANDEREM process (Canadian Decontamination and Remediation Process) and secondary waste from the modified CANDECON process (Canadian Decontamination Process). To improve efficiency and economics of the process it was proposed to treat the waste by combining the sorption of radionuclides on natural inorganic sorbents (zeolites) with membrane filtration. Standard procedures are applied to compare the sorption of radionuclides on different sorbent forms-determination of the ion exchange capacity, construction of sorption isotherms, determination of the distribution coefficients, and kinetics experiments. To check the influence of converting the sorbents to various cationic forms on their sorption properties, distribution coefficients of 137Cs and 57Co on natural zeolites from local deposits converted to NH4 +, Na+ or H+ forms were determined. The results obtained show that the distribution coefficients of 137Cs on the materials converted to Na+ form are higher than for the remaining forms studied [1]. The parameters of Langmuir, Freundlich and Dubinin–Radushkevich adsorption isotherms have been determined using sorption data. The Dubinin–Radushkevich model shows better correlation between the theoretical and experimental data for 137Cs sorption on natural zeolites from local deposits converted to NH4 + and H+ forms than Langmuir and Freundlich equations. Kinetic studies were carried out with various zeolite forms. The sorbents studied are natural zeolites from local deposits (Marsid-Romania). The batch sorption kinetics has been tested for pseudo-second order reaction. The pseudo-second order model fits the experimental data well for all of the systems studied.
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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 | 0.000 | 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".