Prediction of viscosity of blast furnace slag based on NRBO-DNN model
Bibliographic record
Abstract
The viscosity of blast furnace slag significantly impacts operations, slag discharge, and heat recovery. However, accurately measuring or calculating viscosity is challenging due to the complex composition, interactions among variables, and experimental difficulties at high temperatures. To address this issue, a prediction model was developed based on slag composition. Data preprocessing included isolation forest outlier detection, missing data imputation, normalization, and Generative Adversarial Network (GAN)-based data augmentation, ensuring high-quality data. Among traditional neural network models, the Deep Neural Network (DNN) demonstrated the best accuracy. Optimizing the DNN with an intelligent swarm algorithm resulted in the NRBO-DNN model, which achieved MAE, MSE, RMSE, and R² values of 0.04050, 0.00305, 0.05527, and 0.97599, respectively. Compared to the unoptimized DNN, MAE, MSE, and RMSE decreased by 53.86 %, 50.30 %, and 29.50 %, while R² improved by 8.11 %. Tests on 100 datasets confirmed the NRBO-DNN’s superior accuracy, with an average error of 4.30 %. This study provides theoretical support and practical guidance for optimizing blast furnace operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".