Solidification optimisation of electroplating sludge
Bibliographic record
Abstract
A systematic study was conducted for the treatment of electroplating sludge by solidification and stabilisation (S/S) with ordinary Portland cement, lime and magnesium oxide (MgO). Response surface methodology (RSM) and artificial neural networks (ANNs) in combination with central composite design were employed to develop the predictive models for simulation and optimisation of the S/S process. The independent variables were magnesium oxide, electroplating dried sludge, lime and distilled water, while the compressive strength and the concentrations of zinc and chromium in the toxicity characteristic leaching procedure leachate of the solidified waste were the response variables. Both the RSM and ANN models were developed based on the experimental designs. The generalisation and predictive capabilities of RSM and ANN were compared by unseen data (the data set that is not used for model training or validation). Both the RSM and ANN models determined the optimum S/S process. The results show that all independent variables had significant effects on the properties of the S/S products. The optimised method as determined by ANN or RSM can be used with confidence for determining response variables. However, the data predicted by the ANN model are more similar to the experimental results than that of RSM predicted results.
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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 | 0.000 | 0.000 |
| 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".