Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods
Bibliographic record
Abstract
The present study investigates the optimization and advanced simulation of the flotation process of coarse particles (–425 + 106) using micro-nanobubbles (MNBs). For this purpose, flotation experiments in the presence and absence of MNBs were performed on coarse quartz particles, and the results were statistically analyzed. Methyl isobutyl carbinol (MIBC) was employed as a frother for generating MNBs through hydrodynamic cavitation. The significance of the operating variables, including impeller speed, air flow rate, together with the bubble size, and particle size on the flotation recovery was assessed using historical data (HD) design and analysis of variance (ANOVA). The correlation between the flotation parameters and process response in the presence and absence of MNBs was modeled using hybrid convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as the deep learning (DL) frameworks to automatically extract features from input data using a CNN as the base layer. The ANOVA results indicated that all variables affect process responses statistically and meaningfully. Significant interactions were found between air flow rate and particle size as well as impeller speed and MNB size. It was found that a CNN-RNN model could finally be used to model the process based on the intelligent simulation results. Based on Pearson correlation coefficients (PCCs), it was evident that particle size had a strong linear relationship with recovery. However, Shapley additive explanations (SHAP) was considerably more accurate in predicting relationships than Pearson correlations, even though the model outputs agreed well.
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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 | 0.001 | 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".