Maximum solid concentrations of coal wastewater slurries predicted by optimized neural network based on wastewater composition data
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".