Maximum solid concentrations of coal wastewater slurries predicted by optimized neural network based on wastewater composition data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A variety of wastewaters can be generated in the coal chemical industry, and their treatment processes are complicated and have difficulty meeting standards. Using wastewater to prepare coal water slurry is an efficient and convenient new approach. The concentration of coal wastewater slurry is related to the content of the main wastewater components. A backpropagation neural network is developed to predict the maximum slurry concentration and analyze the mechanism at the data level according to the main component indicators, and a particle swarm algorithm is used to improve the neural network. The results are as follows: (a) it is feasible to predict the maximum concentration of coal wastewater slurry by a neural network, and a particle swarm algorithm can effectively improve the prediction accuracy in different models, reducing mean absolute error by up to 0.44%; (b) different input factors have different impacts on model prediction results—organic matter, ammonia nitrogen, and monovalent metal ions content as input factors to predict the maximum slurry concentration can get the most accurate results, obtaining a mean absolute error of 0.16% for the optimized backpropagation neural network and the lowest mean square error; and (c) divalent metal ions and phenols content are not suitable as input factors for predicting, as they all cause an increase in model error due to their weak or complex effects on the slurryability.
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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 it